JP7670286B2 - Information processing system, specific system and program - Google Patents

Description

The present invention relates to an information processing system, a specific system, and a program.

Patent Document 1 discloses an influenza infection test drug and test method that uses the influenza virus nucleoprotein (NP) as an antigen for detecting AIV antibodies.

JP 2007-285749 A

There is a technology that diagnoses that a living organism is in a specific state when a predetermined biomolecule is detected from the organism. However, in this case, the state of the organism cannot be identified based on a biomolecule that is different from the predetermined biomolecule, and the state of the organism cannot be identified using multiple biomolecules.

The objective of the present invention is to identify the state of a living organism using multiple biomolecules.

The information processing system of the present invention comprises an acquisition means for acquiring a distribution image in which each of a plurality of biomolecules of a specific organism is shown as a distribution of a first physical quantity and a second physical quantity, for the distribution at a plurality of time points, and an identification means for identifying a state of the specific organism, wherein the first physical quantity and the second physical quantity are different physical quantities of the biomolecule, the distribution image is an image in which the first physical quantity and the second physical quantity are identified for each of the plurality of biomolecules of the specific organism from a molecular image displayed as an image showing a biomolecule, the molecular image is displayed in an area in the distribution image corresponding to at least the first physical quantity, the identification by the identification means is performed such that the state of the specific organism is identified as a different state depending on an area of the distribution image that satisfies a change condition defined for a change in the second physical quantity identified from the plurality of distribution images acquired for the distribution at a plurality of time points, and the acquisition means acquires molecular information in which the second physical quantity of the biomolecules of the organism is shown, for the plurality of biomolecules of the organism. the identification by the identification means is performed by comparing the second physical quantity of the biomolecule of the specified organism identified from the distribution image with the second physical quantity of the biomolecule of the organism identified from the molecular information, the biomolecule of the specified organism used for the comparison in the distribution image is not determined before the distribution image is acquired by the acquisition means, and the multiple biomolecules shown in the distribution image are candidates for the comparison, the identification by the identification means is performed based on the molecular information in which a change in the second physical quantity of a biomolecule that satisfies a relationship condition determined for a relationship with the change in the second physical quantity of the biomolecule of the specified organism is identified, and the relationship is a relationship between the change in the second physical quantity of the biomolecule of the specified organism, which is the first physical quantity identified from a region of the distribution image that satisfies the change condition, and the change in the second physical quantity identified from the molecular information for the biomolecule in the first physical quantity corresponding to the region.

According to the present invention, the state of a living organism can be identified using multiple biomolecules.

FIG. 1 is a diagram illustrating an example of the overall configuration of a condition specifying system. FIG. 2 illustrates an example of the hardware configuration of a server device and a terminal. FIG. 2 illustrates an example of a functional configuration of a server device. FIG. 13 is a diagram showing an example of a biological information management table. 13 is a flowchart showing the flow of a statistics creation process. FIG. 11 is a diagram showing an example of how statistical information is created. FIG. 11 is a diagram showing an example of how statistical information is created. 13 is a flowchart showing a flow of a state identification process. 11 is a diagram showing an example of how information about a state identified in the state identification process is output to a terminal. FIG. 11 is a diagram showing an example of how information about a state identified in the state identification process is output to a terminal. FIG. 11 is a diagram showing an example of how information about a state identified in the state identification process is output to a terminal. FIG. FIG. 13 is a diagram showing a status notification screen. 11 is a diagram showing an example of how the state of a target living organism is identified by the state identification process. FIG. 11 is a diagram showing an example of how the state of a target living organism is identified by the state identification process. FIG. 11 is a diagram showing an example of how the state of a target living organism is identified by the state identification process. FIG. 11 is a diagram showing an example of how the state of a target living organism is identified by the state identification process. FIG. 13 is a flowchart showing a flow of a state identification process according to the second embodiment. 13A and 13B are diagrams showing images displayed on a display unit of a terminal when a state identification process according to a second embodiment is performed; 13A to 13C are diagrams showing an example of how the state of a target living organism is identified by the state identifying process according to the second embodiment; 20(A) is a two-dimensional electrophoretic image showing each protein contained in serum obtained from a horse at a single time point, and (B) is a diagram showing the two-dimensional electrophoretic image of FIG. 20(A) divided into multiple regions. (A) is a graph showing the body temperature of the subject horses, and (B) is a graph showing the amount of amyloid A substance obtained from the subject horses. FIG. 1 shows statistics calculated for the amount of each protein contained in the serum of each subject horse.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
First Embodiment
FIG. 1 is a diagram showing an example of the overall configuration of a condition specifying system 1.
A condition specifying system 1, which is an example of an information processing system, is a system that specifies the condition of a living organism. Examples of living organisms include animals, plants, and microorganisms. Examples of animals include terrestrial animals and aquatic animals. Animals also include humans. Plants also include agricultural crops.

Examples of the state of a living organism include the health state of the living organism, the beauty state of the living organism, the physical state of the living organism when the living organism is an animal, the brain state of the living organism when the living organism is an animal, the quality state of the living organism when the living organism is food, and the growth state of the living organism when the living organism is a plant or microorganism. Examples of living organisms that are food include agricultural crops and animals. Examples of the health state of a living organism include the injury or illness state of the living organism, and the fatigue state of the living organism. Examples of the physical state of a living organism include the height state of the living organism, the weight state of the living organism, the body fat rate state of the living organism, the muscle mass state of the living organism, and the body state of the living organism. The fatigue state of a living organism may also be considered as the physical state of the living organism. Examples of the brain state of a living organism include the memory state of the living organism, the thinking state of the living organism, and the like. Examples of the quality state of a living organism include the taste state of a living organism. In addition, the state of growth of a living organism may include, for example, the state of the size of each part or the whole organism, the state of the color of the organism, the state of the odor of the organism, the state of withering of the organism, and the state of bacteria and viruses that the organism possesses.

The condition identification system 1 includes a server device 10, a biomolecule analysis device 20, and a terminal 30. The server device 10, the biomolecule analysis device 20, and the terminal 30 are connected via a network.

The server device 10 compares a biomolecule of one organism with another biomolecule different from the biomolecule, and identifies the state of the one organism based on the result of the comparison. The organism whose state is identified by the server device 10 may be referred to as a target organism below. Also, the organism whose state is compared with the target organism by the server device 10 may be referred to as a comparison organism below.
The server device 10 acquires information indicating the physical quantity of a biomolecule. Examples of biomolecules include proteins, amino acids, and peptides. Examples of the physical quantity of a biomolecule include the substance amount of the biomolecule, the molecular weight of the biomolecule, the isoelectric point of the biomolecule, and the charge amount of the biomolecule. The isoelectric point is the pH at which the average charge of the biomolecule after ionization is zero. The information indicating the physical quantity of the biomolecule also includes, for example, information indicating the distribution of the physical quantity of the biomolecule. Hereinafter, the information indicating the physical quantity of the biomolecule may be referred to as molecular information. Hereinafter, the information indicating the physical quantity of the biomolecule of the target organism may be referred to as target molecular information. Hereinafter, the information indicating the physical quantity of the biomolecule of the comparison organism may be referred to as comparison molecular information.

Examples of the protein include hemoglobin, albumin, transferrin, haptoglobin, globulin, lipoprotein, peptide hormone, cytokine, prealbumin, ubiquitin, lactalbumin, glutathione peroxidase, superoxide dismutase, thrombin, prothrombin, actin, acylphosphatase, adrenodoxin, avidin, aldolase, uteroglobin, elastase, ovalbumin, parvalbumin, plastocyanin, relaxin, myoglobin, and leghemoglobin. The protein may be a protein contained in a body fluid such as blood or saliva. The protein may also be a protein contained in serum. The protein may also be a protein contained in a part of a living body. That is, the protein may be any protein contained in a component of a living body. The protein may also be a protein different from any of the above examples.
Examples of amino acids include valine, isoleucine, leucine, methionine, lysine, phenylalanine, tryptophan, threonine, and histidine. The amino acid may be an amino acid contained in a body fluid such as blood or saliva. The amino acid may be an amino acid contained in serum. The amino acid may be an amino acid contained in a part of a living body. That is, the amino acid may be any amino acid contained in a component of a living body. The amino acid may be an amino acid different from any of the above examples.

The server device 10 acquires a plurality of pieces of comparative molecular information. The plurality of pieces of comparative molecular information are molecular information in which biomolecules at different time points are shown for one comparison organism. The server device 10 also associates the state of the comparison organism in a period including the plurality of time points with the acquired plurality of pieces of comparative molecular information. The server device 10 acquires the plurality of pieces of comparative molecular information and associates the state of the comparison organism with the plurality of pieces of comparative molecular information for each comparison organism, as described above. As a result, the server device 10 stores a plurality of pieces of comparative molecular information associated with the state of the organism for each comparison organism.
The server device 10 also acquires a plurality of pieces of target molecule information, each of which indicates a biomolecule at a different time point for a single target organism.

The server device 10 detects changes in biomolecules over time from multiple pieces of target molecular information. The server device 10 also compares the detected biomolecules with the biomolecules shown in the comparative molecular information to extract comparative molecular information from the comparative molecular information stored in the server device 10 that indicates changes in biomolecules that correspond to the changes in the biomolecules detected in the target organism. The server device 10 then identifies the state of the comparative organism associated with the extracted comparative molecular information as the state of the target organism. The server device 10 also outputs information related to the identified state to the terminal 30.

The server device 10 is realized, for example, by a computer. The server device 10 may be configured by a single computer, or may be realized by distributed processing using multiple computers. The server device 10 may also be realized on virtual hardware provided by cloud computing.

The biomolecule analyzer 20 analyzes biomolecules contained in the components collected from a living organism. More specifically, the biomolecule analyzer 20 detects the distribution of biomolecules by analyzing the biomolecules contained in the components collected from a living organism. For example, a two-dimensional electrophoresis device is used as the biomolecule analyzer 20. This two-dimensional electrophoresis device separates each of the biomolecules contained in the components collected from a living organism by molecular weight and by isoelectric point, thereby creating a two-dimensional electrophoretic image showing the molecular weight distribution and isoelectric point distribution of each biomolecule. In the two-dimensional electrophoretic image, for example, the position where the pixels constituting the image showing the biomolecule are arranged is defined in a two-dimensional orthogonal coordinate system in which the distribution of the isoelectric points of the biomolecules is shown on the x-axis and the distribution of the molecular weights of the biomolecules is shown on the y-axis. That is, in the two-dimensional electrophoretic image, information indicating whether or not a biomolecule is present is written for each pixel coordinate. This two-dimensional electrophoretic image can be regarded as an example of molecular information. In addition, information showing the distribution of biomolecules, such as a two-dimensional electrophoretic image, may be referred to as distribution information below.In addition, information showing the distribution of biomolecules in a target organism may be referred to as target distribution information below.In addition, information showing the distribution of biomolecules in a comparison organism may be referred to as comparison distribution information below.
The two-dimensional electrophoretic image also includes information indicating the distribution of the substance amount of the biomolecules. That is, the distribution of the biomolecules includes the distribution of the molecular weights of the biomolecules, the distribution of the isoelectric points of the biomolecules, the distribution of the presence of the biomolecules, and the distribution of the substance amount of the biomolecules. The information indicating the substance amount of the biomolecules in the two-dimensional electrophoretic image may be displayed, for example, by the shading of the image showing the biomolecules. For example, a higher density of the image showing the biomolecules in the two-dimensional electrophoretic image may indicate a larger substance amount of the biomolecules.

In addition, for example, a biosensor may be used as the biomolecule analysis device 20. This biosensor identifies biomolecules in a living organism. The biosensor also separates the identified biomolecules by molecular weight and by isoelectric point, and creates an image showing the distribution of molecular weights and isoelectric points of the biomolecules. Therefore, the image created using the biosensor can also be regarded as distribution information. Furthermore, the image created by the biosensor may include information showing the distribution of the substance amount of the biomolecules.

In this embodiment, a user of the condition identification system 1 uses the biomolecule analyzer 20 to analyze the components of a comparative organism obtained at multiple time points in one period, and creates comparative distribution information for each of the multiple time points showing the distribution of each biomolecule contained in the components. Each of the multiple time points is a time point at which the state of the comparative organism is specified. Also, one period is a period in which a state that includes the states of the comparative organism at multiple time points is specified. Hereinafter, each individual state of the comparative organism at multiple time points may be referred to as an individual state. Hereinafter, a state of the comparative organism that includes each individual state at multiple time points may be referred to as a comprehensive state. An example of the comprehensive state is a state in which the comparative organism has influenza. Also, an example of an individual state is a state in which the comparative organism has influenza, a state in which the comparative organism has a fever, etc.
Furthermore, the user creates, for each comparison organism, a plurality of pieces of comparative distribution information, each of which represents a different time point at which the components of the comparison organism, including the biomolecules, were obtained, using the biomolecule analyzer 20. In this way, a plurality of pieces of comparative distribution information, each of which shows the biomolecules in their individual states at a plurality of time points, are created for each comparison organism.

In particular, in this embodiment, the user uses the biomolecular analysis device 20 to analyze the biomolecules obtained at each point in time when multiple comparison organisms have the same inclusion state and individual state, and creates comparison molecular information.
In addition, the user analyzes the components of the target organism obtained at multiple time points using the biomolecule analyzer 20, and creates target distribution information for each of the multiple time points showing the distribution of each biomolecule contained in the components. These multiple time points are the time points at which the condition identification system 1 identifies the condition of the target organism.

When the biomolecule analysis device 20 creates the distribution information, it transmits the created distribution information to the server device 10 together with date and time information indicating the date and time when the biomolecule identified from the distribution information was obtained, and bioinformation indicating the organism having the biomolecule indicated by the distribution information. Note that an organism that "has" a biomolecule includes not only an organism that has the target biomolecule, but also an organism that had the target biomolecule until it was collected, even though the organism no longer has the target biomolecule due to the target biomolecule being collected from the organism.

The biomolecule analyzer 20 is not limited to analyzing each biomolecule contained in a component collected from a living organism. For example, if the biomolecule analyzer 20 is capable of analyzing each biomolecule in a living organism and creating distribution information without collecting a component containing the biomolecule from the living organism, such a biomolecule analyzer 20 may be used.

The terminal 30 has a display unit 31 for displaying information. When the terminal 30 acquires information from the server device 10, the terminal 30 displays the acquired information on the display unit 31.
The terminal 30 is realized by, for example, a computer, a tablet information terminal, or another information processing device. The terminal 30 may be, for example, a smartphone. That is, the terminal 30 may be any type of terminal.

The network used to connect the server device 10 to the biomolecule analysis device 20 and the terminal 30 is not particularly limited in type as long as data can be sent and received, and may be, for example, the Internet, a LAN (Local Area Network), a WAN (Wide Area Network), etc. The communication line used to send and receive data may be wired or wireless. In addition, the devices may be connected via multiple networks or communication lines.

<Hardware configuration example>
FIG. 2 is a diagram showing an example of the hardware configuration of the server device 10 and the terminal 30. As shown in FIG.
2, the server device 10 and the terminal 30 each include a central processing unit (CPU) 10a, which is a computing means, and a memory 10c, which is a main storage means. Each device also includes, as external devices, a non-volatile storage device 10g, a network interface 10f, a display mechanism 10d, an audio mechanism 10h, and an input device 10i, such as a keyboard or a mouse.

The memory 10c and the display mechanism 10d are connected to the CPU 10a via the system controller 10b. The network interface 10f, the non-volatile recording device 10g, the audio mechanism 10h, and the input device 10i are connected to the system controller 10b via the bridge controller 10e. Each component is connected by various buses such as a system bus and an input/output bus.

The non-volatile recording device 10g stores programs for implementing each function. These programs are then loaded into the memory 10c, and the CPU 10a executes processing based on these programs to implement various functions. Examples of non-volatile recording devices 10g include semiconductor memories such as SSDs (Solid State Drives) and magnetic disk devices such as HDDs (Hard Disk Drives).

<Functional configuration of server device>
Next, the functional configuration of the server device 10 will be described.
FIG. 3 is a diagram showing an example of the functional configuration of the server device 10. As shown in FIG.
The server device 10 includes an information acquisition unit 101, a memory unit 102, an area identification unit 103, a partition unit 104, a substance amount identification unit 105, a statistics unit 106, a detection unit 107, a target determination unit 108, a state identification unit 109, and an output unit 110.

The information acquisition unit 101, which is an example of an acquisition means, acquires distribution information from the biomolecule analysis device 20. More specifically, the information acquisition unit 101 acquires distribution information consisting of target distribution information or comparison distribution information, together with date and time information and bioinformation. The information acquisition unit 101 also acquires information indicating the state of the comparison organism. The information indicating the state of the comparison organism may be referred to as state information below. The information indicating the individual states of the comparison organism may be referred to as individual state information below. The information indicating the overall state of the comparison organism may be referred to as overall state information below.
A user of the condition identification system 1 inputs individual state information corresponding to the comparative distribution information to the terminal 30 for each piece of comparative distribution information. The individual state information corresponding to the comparative distribution information is information indicating a state of each individual state at the time when the biomolecule indicated in the comparative distribution information was obtained. In addition, the user inputs comprehensive state information corresponding to a plurality of pieces of comparative distribution information to the terminal 30. The comprehensive state information corresponding to a plurality of pieces of comparative distribution information is information indicating a comprehensive state in a period including a plurality of time points at which the biomolecule indicated in the plurality of pieces of comparative distribution information was obtained.

When the information acquiring unit 101 acquires the comparative distribution information and the state information, the information acquiring unit 101 stores the acquired state information in the storage unit 102 in association with the comparative distribution information corresponding to this state information.
In this embodiment, as described above, for a plurality of comparison organisms whose comprehensive state and individual state are the same, comparative molecular information is created for each time point when the organisms were in their individual states. Therefore, even if the comparative distribution information is related to different comparison organisms, these comparative molecular information are associated with individual state information showing the same individual state and comprehensive state information showing the same comprehensive state. The information acquisition unit 101 can also be regarded as a molecular information acquisition means for acquiring molecular information.

The storage unit 102 stores the distribution information, date and time information, biometric information, status information, etc. acquired by the information acquisition unit 101. The contents stored in the storage unit 102 will be described in detail later.

The region identification unit 103 identifies a region in the distribution information where a biomolecule exists. The region identification unit 103 uses both the target distribution information and the comparison molecule information as targets for region identification.
An example of a method for identifying a region where a biomolecule exists in the distribution information by the region identification unit 103 will be described below. The region identification unit 103 detects an image showing a biomolecule in the distribution information, and identifies the coordinates where the detected image is shown, thereby identifying the region where the biomolecule exists.
When multiple biomolecules are present in the distribution information, the region identification unit 103 identifies a region in which the biomolecule exists for each of the biomolecules present. Furthermore, the region identification unit 103 identifies a region in which the biomolecule exists for each of the distribution information. When the region identification unit 103 identifies a region in which the biomolecule exists in the distribution information of the target, it associates region information indicating the identified region with the distribution information of the target.

The partitioning unit 104 partitions a region in which a biomolecule exists in the distribution information. More specifically, the partitioning unit 104 partitions a region in which a biomolecule exists in the molecular information based on the region information created by the region identification unit 103. The partitioning unit 104 also partitions a region in which a biomolecule exists for each distribution information. The partitioning unit 104 may partition a region in which a biomolecule exists in the molecular information into one region, or may divide the region into multiple regions. The partitioning unit 104 may also partition a region in which a biomolecule does not exist in the distribution information. The region partitioned by the partitioning unit 104 may be referred to as a partitioned region hereinafter.
The partitioning unit 104 uses both the target distribution information and the comparison molecule information as targets for partitioning the regions.

The substance amount specifying unit 105 specifies the amount of substance of the biomolecule shown in the distribution information. More specifically, the substance amount specifying unit 105 specifies the amount of substance of the biomolecule included in the partitioned area in the distribution information for each partitioned area. When the substance amount specifying unit 105 specifies the amount of substance of the biomolecule included in a target partitioned area in the distribution information, it associates substance amount information indicating the specified amount of substance with the target partitioned area.
The substance amount specifying unit 105 uses both the target distribution information and the comparison molecule information as targets for specifying the substance amount of a biomolecule.

The statistics section 106 calculates statistics of the substance amounts of the biomolecules indicated in each of the comparative distribution information associated with the same individual state information.
The statistics unit 106 extracts comparative distribution information associated with the same individual state information from the comparative distribution information stored in the storage unit 102. Then, the statistics unit 106 calculates statistics of the substance amounts of biomolecules included in the partitioned areas in each of the extracted comparative distribution information for each partitioned area. More specifically, the statistics unit 106 calculates statistics of the substance amounts of biomolecules included in partitioned areas having the same coordinates in each of the extracted comparative distribution information based on the substance amount information associated with this partitioned area. Furthermore, the statistics unit 106 calculates statistics of the substance amounts of biomolecules for each of the comparative distribution information associated with the same individual state information. In this way, statistics of the substance amounts of biomolecules are calculated for each individual state.

The statistics unit 106 may calculate statistics of the amounts of substance of the biomolecules by calculating an average value of the amounts of substance of the biomolecules that are the subject of the statistics. The statistics unit 106 may also calculate statistics of the amounts of substance of the biomolecules by calculating a median value of the amounts of substance of the biomolecules that are the subject of the statistics. In this way, any method may be used as the statistical method used by the statistics unit 106.
Furthermore, when the statistics unit 106 calculates statistics of the substance amounts of biomolecules for each individual state included in one inclusive state, it associates each calculated statistic with the corresponding individual state information. As a result, information indicating the statistics calculated by the statistics unit 106 is created for each individual state. Hereinafter, the information indicating the statistics calculated by the statistics unit 106 for each individual state may be referred to as statistical information. The statistics unit 106 associates the created statistical information with partitioned area information that identifies the partitioned area that is the subject of this statistical information, and stores it in the storage unit 102.
The statistical information is information showing the statistical amounts of substances of biomolecules, and therefore, in a broad sense, the statistical information can also be regarded as molecular information.

The detection unit 107 detects changes in biomolecules of a target organism.
The detection unit 107 compares the amounts of substance of the biomolecules shown in the two pieces of target distribution information based on the substance amount information related to the two pieces of target distribution information. The two pieces of target distribution information are distribution information showing biomolecules obtained from a target organism at different times. Of the two pieces of target distribution information to be compared, the target distribution information showing the biomolecule obtained at an earlier time point may be referred to as early distribution information below. Of the two pieces of target distribution information to be compared, the target distribution information showing the biomolecule obtained at a later time point may be referred to as late distribution information below. The target biomolecules to be compared are biomolecules included in partitioned areas with the same coordinates in the two pieces of target distribution information.

The detection unit 107 judges whether the biomolecules to be compared satisfy the change condition. The change condition is a condition determined for detecting a change in the biomolecules accompanying a change in the state of the target organism. In this embodiment, the change condition is determined to be that the difference in the amount of substance between the two biomolecules to be compared is equal to or greater than a predetermined value. The detection unit 107 detects the two biomolecules that satisfy the change condition as biomolecules whose amount of substance has changed. Note that, hereinafter, the two biomolecules detected by the detection unit 107 as satisfying the change condition may be referred to as detected molecules. Also, hereinafter, the difference in the amount of substance between the two biomolecules detected by the detection unit 107 may be referred to as a detection value.
When the biomolecules satisfy the change condition in a plurality of partitioned regions, the detection unit 107 detects all of the biomolecules in the plurality of partitioned regions.

The target determining unit 108 determines target statistical information to be compared with the detection molecule from the statistical information stored in the storage unit 102 .
The target determination unit 108 determines the statistical information for the partitioned area having the same coordinates as the partitioned area in which the detection molecule is shown as the statistical information to be compared with the detection molecule. The target determination unit 108 refers to the partitioned area information associated with the statistical information, and extracts the statistical information for the partitioned area having the same coordinates as the partitioned area in which the detection molecule is shown. In addition, when the detection molecule is detected in multiple partitioned areas, the target determination unit 108 determines the statistical information to be compared for each detection molecule. Note that the statistical information determined by the target determination unit 108 as the target for comparison with the detection molecule may be referred to as comparison statistical information hereinafter.

The condition specifying unit 109, which is an example of a specifying means, specifies the condition of the target living body.
The state identification unit 109 extracts biomolecules that satisfy the same similarity condition from among the biomolecules shown in the comparison statistical information. The same similarity condition is a condition determined to identify a change in a biomolecule that is the same as or similar to a change in the substance amount of the biomolecule indicated by the detection value of the detection molecule. In this embodiment, the same similarity condition is determined to be that the change in the substance amount of the biomolecule indicated in the comparison statistical information and the change in the substance amount of the biomolecule indicated by the detection value are the same as or similar to each other. In addition, an example of the same similarity condition is that the difference between the change in the substance amount of the biomolecule indicated in the comparison statistical information and the detection value is equal to or less than a predetermined value. The state identification unit 109 identifies the comprehensive state associated with the comparison statistical information that satisfies the same similarity condition as the state of the target organism. The comparison statistical information that satisfies the same similarity condition is comparison statistical information in which a biomolecule that satisfies the same similarity condition is indicated.

The output unit 110, which is an example of an output means, outputs information about the state identified by the state identification unit 109 to the terminal 30.

<Storage contents of storage unit>
Next, the contents of the information stored in the storage unit 102 will be described.
4(A) and 4(B) are diagrams showing examples of a biometric information management table. The biometric information management table is a table for managing information on a comparison organism. This biometric information management table is provided for each type of organism. Fig. 4(A) shows a biometric information management table for "humans," a type of animal, and Fig. 4(B) shows a biometric information management table for "bananas," a type of agricultural crop.

In the biological information management table shown in FIG. 4A, the name of the comparison biological body is shown in the "name" field.
In addition, the "date and time" field indicates date and time information.
Moreover, comparative distribution information is shown in "distribution." More specifically, in "distribution," the distribution of the physical quantities of the biomolecules identified from the distribution information is shown for each biomolecule.

In addition, individual condition information is shown in "individual condition." In this embodiment, the "individual condition" is divided into four items: "health condition,""physicalcondition,""braincondition," and "beauty condition."
"Health condition" indicates the health condition of the comparison organism. "Physical condition" indicates the physical condition of the comparison organism. "Brain condition" indicates the brain condition of the comparison organism. "Cosmetic condition" indicates the cosmetic condition of the comparison organism. "Individual condition" may indicate the growth condition of the comparison organism.
In addition, in the "comprehensive state" field, comprehensive state information is shown.

The "individual state" and "comprehensive state" in the biological information management table shown in FIG. 4A will be specifically described.
For example, the comparative distribution information of organism "A" is associated with "influenza" which is an "overall condition", as well as with "health conditions" such as "infection" and "fever". "Influenza" is a state in which the comparative organism has influenza. "Infection" is a state in which the comparative organism is infected with the influenza virus. "Fever" is a state in which the comparative organism has a fever.
For example, the comparative distribution information of organism "H" is associated with the "comprehensive state" of "anticancer drug effective," as well as with "health states" such as "anticancer drug intake" and "fatigue.""Anticancer drug effective" is a state in which the comparative organism is experiencing the effects of taking an anticancer drug. "Anticancer drug intake" is a state in which the comparative organism has taken an anticancer drug. "Fatigue" is a state in which the comparative organism has experienced fatigue.
For example, the comparative distribution information of organism "I" is associated with a "general state" of "no effect of anticancer drug," as well as with "health states" such as "taking anticancer drug" and "headache.""No effect of anticancer drug" is a state in which the comparative organism experiences no effect from taking anticancer drug. Also, "headache" is a state in which the comparative organism has a headache.

Furthermore, for example, the comparative distribution information of organism "J" is associated with a "general state" of "training effective," as well as with "physical states" such as "training performed" and "muscle pain.""Trainingeffective" is a state in which the comparative organism has experienced the effects of training. "Training performed" is a state in which the comparative organism has performed training. "Muscle pain" is a state in which the comparative organism has experienced muscle pain.
For example, the comparative distribution information of organism "J" is associated with a "general state" of "no training effect," as well as with "physical states" such as "training performed" and "fatigue.""No training effect" is a state in which there is no effect from the training performed by the comparative organism. "Fatigue" is a state in which the comparative organism has a feeling of fatigue.
Furthermore, for example, the comparative distribution information of organism "K" is associated with "improved memory" which is a "comprehensive state", as well as with "physical states" such as "sleep 8 hours" and "brain states" such as "study 3 hours". "Improved memory" is a state in which the memory of the comparison organism has improved. Furthermore, "sleep 8 hours" is a state in which the comparison organism has slept for 8 hours. Furthermore, "study 3 hours" is a state in which the comparison organism has studied for 3 hours.
For example, the comparative distribution information of the organism "L" is associated with "deteriorating memory" which is a "comprehensive state", as well as with "physical states" such as "sleep for 4 hours" and "brain states" such as "study for 3 hours". "Deteriorating memory" is a state in which the memory of the comparative organism has deteriorated. "Sleep for 4 hours" is a state in which the comparative organism has slept for 4 hours.

For example, the comparative distribution information of the organism "M" is associated with a "comprehensive state" of "skin age 40 years old" and with "beauty states" such as "use of skin cream A.""Skin age 40 years old" is a state in which the skin age of the comparative organism is identified as 40 years old. "Use of skin cream A" is a state in which the comparative organism has used "skin cream A."
For example, the comparative distribution information of the organism "M" is associated with a "general state" of "skin age 25 years old" and with "beauty states" such as "use of skin cream B.""Skin age 25 years old" is a state in which the skin age of the comparative organism is identified as 25 years old. "Use of skin cream B" is a state in which the comparative organism has used "skin cream B."

In the biometric information management table shown in Fig. 4B, the name for identifying the comparison organism is shown in "identification name". In the illustrated example, the "identification name" shows the name for identifying each "banana".
In addition, the "date and time" field indicates date and time information.
In addition, in the "distribution" section, comparison distribution information is shown, similar to the example in FIG.
Further, individual condition information is shown in "individual condition". In this embodiment, "quality condition" is shown as "individual condition". "Quality condition" is the quality condition of the comparison living body.
In addition, in the "comprehensive state" field, comprehensive state information is shown.

The "individual state" and "comprehensive state" in the biological information management table shown in FIG. 4B will be specifically described.
The comparative distribution information of organism "X" is associated with a "comprehensive state" of "good quality" as well as with "quality states" such as "sugar content 20 degrees.""Goodquality" is a state in which the quality of the comparative organism is specified as good. Furthermore, "sugar content 20 degrees" is a state in which the sugar content of the comparative organism is specified as 20 degrees.
The comparative distribution information of organism "Y" is associated with a "general state" of "poor quality" as well as with "quality states" such as "sugar content 10 degrees.""Poorquality" is a state in which the quality of the comparative organism is identified as poor. Furthermore, "sugar content 10 degrees" is a state in which the sugar content of the comparative organism is identified as 10 degrees.
Although not shown in FIGS. 4A and 4B, the storage unit 102 has a biological information management table for each type of comparison biological specimen.

<Statistics creation process>
Next, the flow of the statistics creation process will be described with reference to an example up to the creation of statistical information. The statistics creation process is a process in which the server device 10 creates statistical information. In this embodiment, the statistics creation process is started when a user of the condition identification system 1 designates one comprehensive state and instructs the server device 10 to execute the statistics creation process. Note that, hereinafter, the one comprehensive state designated by the user may be referred to as a designated state. Also, hereinafter, a case in which the designated state is "influenza" will be described.
Fig. 5 is a flow chart showing the flow of the statistics creation process, and Figs. 6 and 7 are diagrams showing an example of the process up to the creation of statistical information.

First, the server device 10 designates one of the comparison biomimetics that were in the designated state (step (hereinafter referred to as "S") 101). The server device 10 identifies the comparison biomimetics that were in the designated state from the biomimetics information associated with the comprehensive state information that indicates the designated state. Hereinafter, the one comparison biomimetics designated by the server device 10 in step 101 may be referred to as the designated biomimetics.
Next, the server device 10 designates one of the comparative distribution information associated with the comprehensive state information indicating the designated state (S102). The comparative distribution information designated to the server device 10 in step 102 is comparative distribution information indicating the biomolecules of the designated organism. In addition, the comparative distribution information designated to the server device 10 in step 102 may be referred to as designated distribution information hereinafter.

In this example, as described above, the designated state is "influenza". In this case, when the designated organism becomes organism "A", comparative distribution information for organism "A" associated with "influenza" is designated in step 102, as shown in FIG. 6(A). The designated distribution information shown in FIG. 6(A) is one of the comparative distribution information associated with organism "A" and "influenza" in the organism information management table (see FIG. 4(A)) for "humans". This designated distribution information is information showing the distribution of the substance amount of the biomolecule when the isoelectric point of the biomolecule is the horizontal axis in the figure and the molecular weight of the biomolecule is the vertical axis in the figure. In addition, in the designated distribution information shown in FIG. 6(A), the image shown by the diagonal line and the line surrounding the diagonal line represents the biomolecule. Seven different biomolecules are shown in the distribution information shown in FIG. 6(A).
In the illustrated example, the biomolecules are spaced apart in the distribution information, but multiple biomolecules may be adjacent to each other in the distribution information.

Next, the region identification unit 103 identifies a region in which a biomolecule exists in the designated distribution information (S103).
The partitioning unit 104 partitions the area identified by the area identification unit 103 in the designated distribution information (S104).
In this example, in step 104, the partitioning unit 104 partitions the region in which the biomolecule is indicated in the designated distribution information into twenty regions consisting of region 1 to region 20, as shown in FIG. 6(B). In the example shown in FIG. 6(B), one biomolecule is partitioned into five regions consisting of region 1 to region 5. Also, one biomolecule is partitioned into one region consisting of region 6. Also, one biomolecule is partitioned into one region consisting of region 7. Also, one biomolecule is partitioned into six regions consisting of region 8 to region 13. Also, one biomolecule is partitioned into two regions consisting of region 14 and region 15. Also, one biomolecule is partitioned into four regions consisting of region 16 to region 19. Also, one biomolecule is partitioned into one region consisting of region 20.

Next, the substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in each divided region (S105).
In this example, in step 105, the substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in the partitioned region as shown in FIG. 6C. FIG. 6C is a diagram showing the biomolecule present in the partitioned region, region 20, when the time when the biomolecule was obtained is the horizontal axis in the figure and the substance amount of the biomolecule is the vertical axis in the figure. In FIG. 6C, the symbol "●" means the biomolecule of organism "A". In addition, the biomolecule shown in FIG. 6C is associated with "infection", which is one of the individual states. This "infection" is one of the individual states (see FIG. 4A) associated with the comparison distribution information in which the biomolecule of organism "A" specified in FIG. 6C is shown. In the example of FIG. 6C, only the biomolecule in region 20 is shown, but in step 105, the substance amount of the biomolecule is specified for each of regions 1 to 20, i.e., all partitioned regions in the specified distribution information.

Next, the server device 10 determines whether or not the substance amount of the biomolecule has been specified for all comparative distribution information associated with comprehensive state information indicating the designated state among the comparative distribution information related to the designated organism (S106).
If the substance amount of the biomolecule is not specified for any of the comparative distribution information associated with the comprehensive state information indicating the designated state (NO in S106), the server device 10 newly specifies one of the comparative distribution information for which the substance amount is not specified (S102). After this, the process from step 103 onwards is performed.

In this way, the process from step 102 onwards is repeated until the substance amount of the biomolecule is specified for all comparative distribution information associated with the comprehensive state information indicating the specified state among the comparative distribution information related to the specified organism. As a result, in this example, as shown in FIG. 6(D), the substance amount of the biomolecule present in the partitioned area is specified for all comparative distribution information associated with "influenza" specified for the organism "A". FIG. 6(D) is a diagram showing the biomolecules present in the area 20 at each time point when the biomolecule was obtained during the period specified as "influenza", with the horizontal axis representing the time point when the biomolecule was obtained and the vertical axis representing the substance amount of the biomolecule. Each of the biomolecules shown in FIG. 6(D) is associated with an individual state. This individual state is an individual state (see FIG. 4(A)) associated with the comparative distribution information in which the biomolecule of the organism "A" specified in FIG. 6(D) is shown. In addition, the individual state shows the number of days from "1 day" to "14 days". This number of days means the number of days that have passed since the organism "A" was infected with the influenza virus.
In the example of FIG. 6D, only the biomolecules in region 20 are shown, but by repeating steps 102 to 106, the substance amounts of the biomolecules in all partitioned regions are identified.

When the substance amounts of the biomolecules are identified for all of the comparative distribution information related to the designated organism that is associated with the comprehensive state information indicating the designated state (YES in S106), the server device 10 proceeds to the next step. For all of the comparison organisms that were in the designated state, the server device 10 determines whether the substance amounts of the biomolecules in the comparative distribution information are identified (S107).
If the substance amount of the biomolecule in the comparative distribution information is not specified for any of the comparison organisms that were in the designated state (NO in S107), the server device 10 newly specifies one comparison organism in which the substance amount of the biomolecule in the comparative distribution information is not specified (S101). After this, the process from step 102 onwards is performed.

In this way, the process from step 101 onwards is repeated until the amount of substance of the biomolecule in the comparative distribution information is specified for all the comparison organisms that were in the designated state. As a result, in this example, as shown in FIG. 7(E), the amount of substance of the biomolecule present in the partitioned area is specified for all the comparison distribution information associated with "influenza" for all the comparison organisms "A", "B", and "C" that were in the designated state. FIG. 7(E) is a diagram showing the biomolecules present in the area 20 at each time point when the biomolecule was obtained during the period specified as "influenza" for the organisms "A", "B", and "C". In FIG. 7(E), the horizontal axis is the time point when the biomolecule was obtained, and the vertical axis is the amount of substance of the biomolecule. In addition, in FIG. 7(E), the symbol "●" means the biomolecule of the organism "A", the symbol "▲" means the biomolecule of the organism "B", and the symbol "■" means the biomolecule of the organism "C". The amount of substance of the biomolecule of organism "A", the amount of substance of the biomolecule of organism "B", and the amount of substance of the biomolecule of organism "C" are each associated with the same individual state information. This individual state is the individual state (see FIG. 4(A)) associated with the comparative distribution information in which the biomolecules of organism "A", organism "B", and organism "C" identified in FIG. 7(E) are respectively shown. In the example of FIG. 7(E), only the biomolecules in region 20 are shown, but by repeating steps 101 to 107, the amounts of substance of the biomolecules in all partitioned regions are identified.

When the substance amounts of the biomolecules in the comparative distribution information are specified for all comparison organisms that were in the designated state (YES in S107), the statistical unit 106 calculates statistics of the substance amounts of the biomolecules associated with the same state among the individual states. More specifically, the statistical unit 106 calculates statistics of the substance amounts of the biomolecules associated with the same state among the individual states for each individual state, thereby creating statistical information indicating the statistics of the substance amounts of the biomolecules for each individual state (S108). In addition, the statistical unit 106 creates statistical information for each partitioned region.
In this example, the statistical unit 106 calculates statistics of the amounts of substance of biomolecules of organism "A", organism "B", and organism "C" associated with the same state among the individual states, for each individual state, to create the statistical information shown in Fig. 7(F). Fig. 7(F) shows statistics of the amounts of substance of biomolecules in region 20. This statistical information shows statistics of the amounts of substance of biomolecules of organism "A", organism "B", and organism "C" for each individual state.

<Status Identification Processing>
Next, the flow of the condition identification process will be described with reference to an example up to the point where information on the condition identified by the condition identification process is output to the terminal 30. The condition identification process is a process in which the server device 10 identifies the condition of a target living organism. In this embodiment, the condition identification process is started when a user of the condition identification system 1 specifies target distribution information to be used in the condition identification process and instructs the server device 10 to execute the condition identification process. Note that, hereinafter, the target distribution information specified by the user as the target distribution information to be used in the condition identification process may be referred to as usage distribution information.

Fig. 8 is a flowchart showing the flow of the state specification process. Figs. 9 to 12 are diagrams showing an example of the process up to when information on the state specified in the state specification process is output to the terminal 30.
In the following, an example will be described in which the target organism is organism "D" and the two target distribution information shown in Fig. 9(A) is usage distribution information. In Fig. 9(A), early distribution information and late distribution information for organism "D" are shown as usage distribution information. The early distribution information and late distribution information each show the distribution of seven different biomolecules, similar to the distribution information shown in Fig. 6(A). Moreover, organism "D" is, for example, a human being.

First, the region identification unit 103 identifies a region in which a biomolecule exists in the usage distribution information for each piece of usage distribution information (S201).
The partitioning unit 104 partitions the area identified by the area identification unit 103 for each piece of usage distribution information (S202).
In this example, in step 202, the partitioning unit 104 partitions the regions in which biomolecules are indicated in the early distribution information and the later distribution information into twenty regions consisting of regions 1 to 20, as shown in Fig. 9(B) . That is, the partitioning unit 104 partitions the regions in which biomolecules are indicated in the usage distribution information in the same manner as the partitioning performed on the comparison distribution information (see Fig. 6(B) ).

Next, the substance amount identification unit 105 identifies the substance amount of the biomolecule contained in each partition area for each used distribution information (S203). As a result, in this example, as shown in FIG. 9(C), the substance amount of the biomolecule shown in the early distribution information and the late distribution information for organism "D" is identified for each partition area. FIG. 9(C) is a diagram showing the biomolecules present in each partition area, with the time when the biomolecule was obtained being plotted on the horizontal axis and the substance amount of the biomolecule being plotted on the vertical axis. In FIG. 9(C), the symbol "●" refers to the biomolecule shown in the early distribution information, and the symbol "▲" refers to the biomolecule shown in the late distribution information.

Next, the detection unit 107 detects biomolecules that satisfy the change condition (S204). More specifically, the detection unit 107 determines whether or not the biomolecule satisfies the change condition for each partitioned region, and detects the biomolecule that is determined to satisfy the change condition.
In this example, in regions 14, 15, and 20 shown in FIG. 9C, the amount of substance of the biomolecule indicated in the later distribution information has changed from the amount of substance of the biomolecule indicated in the early distribution information. More specifically, in region 14, the amount of substance of the biomolecule indicated in the later distribution information has increased compared to the amount of substance of the biomolecule indicated in the early distribution information. Also, in regions 15 and 20, the amount of substance of the biomolecule indicated in the later distribution information has decreased compared to the amount of substance of the biomolecule indicated in the early distribution information. Then, the biomolecules contained in regions 14, 15, and 20 are all detected by detection unit 107 as satisfying the change condition.

The multiple biomolecules that satisfy the change condition, i.e., the biomolecules contained in regions 14 and 15 and the biomolecules contained in region 20, are different biomolecules. One example of different biomolecules is hemoglobin and transferrin. Another example of different biomolecules is hemoglobin HbA and hemoglobin HbA2. In other words, different biomolecules may be any biomolecules as long as the molecules that make up the biomolecules are not identical.

Next, the object determining unit 108 determines comparative statistical information to be compared with the detected molecule (S205). The object determining unit 108 determines, from the statistical information stored in the storage unit 102, statistical information that targets a partitioned area having the same coordinates as the partitioned area in which the detected molecule is indicated, as the comparative statistical information.
In this example, the object determining unit 108 determines each piece of statistical information targeting the region 20 as comparative statistical information for the detection molecule shown in the region 20 for the organism "D". This comparative statistical information includes statistical information associated with the inclusive state "influenza" and targeting the region 20, as shown in FIG. 10(D). Furthermore, although not shown, the object determining unit 108 determines each piece of statistical information targeting the region 15 as comparative statistical information for the detection molecule shown in the region 15 for the organism "D". Furthermore, the object determining unit 108 determines each piece of statistical information targeting the region 14 as comparative statistical information for the detection molecule shown in the region 14 for the organism "D".

Next, the state identifying unit 109 extracts biomolecules that satisfy the sameness/similarity condition from among the biomolecules shown in the comparison statistical information (S206). More specifically, the state identifying unit 109 compares the detection molecule with the biomolecules shown in the comparison statistical information to extract biomolecules that satisfy the sameness/similarity condition from among the biomolecules shown in the comparison statistical information.
In this example, as shown in FIG. 10(E), the state specifying unit 109 determines that the biomolecules shown in the two comparative statistical information pieces both satisfy the same similarity condition with respect to the detection molecule included in the region 20 for the organism "D". One of the two comparative statistical information pieces is comparative statistical information that is associated with the inclusive state "influenza" and targets the region 20. The dotted line portion A in the comparative statistical information piece shows a decrease in the substance amount of the biomolecule over time, and the state specifying unit 109 determines that the biomolecule shown in this portion A satisfies the same similarity condition with respect to the detection molecule included in the region 20 for the organism "D". The comparative statistical information that is associated with "influenza" and targets the region 20 is the statistical information shown in FIG. 7(F). The other of the two comparative statistical information pieces is comparative statistical information that is associated with the inclusive state "COVID (Corona Virus Disease)-19" and targets the region 20. The dotted line point B in the figure of this comparison statistical information also shows a decrease in the amount of substance of the biomolecule over time, and the state determination unit 109 determines that the biomolecule shown at this point B satisfies the same similarity condition with the detected molecule contained in region 20 for organism "D."

In this way, it may be determined that the biomolecules shown in the multiple pieces of comparison statistical information all satisfy the same or similar condition with respect to the detection molecule included in one partitioned area. In addition, influenza virus and COVID-19 have in common the fact that they are both infectious diseases, and a specific biomolecule in the target organism may show the same or similar changes when the target organism is infected with influenza virus and when the target organism is infected with COVID-19. In this case, the state identification unit 109 compares the biomolecule shown in the extracted comparison statistical information with the detection molecule included in the partitioned area that has not yet been compared.
In this example, as shown in FIG. 11(F), the state identification unit 109 compares the detection molecule shown in the region 14 for the organism "D" with the biomolecule shown in the comparative statistical information associated with "influenza" and targeting the region 14. The state identification unit 109 also compares the detection molecule shown in the region 14 for the organism "D" with the biomolecule shown in the comparative statistical information associated with "COVID-19" and targeting the region 14. The state identification unit 109 also compares the detection molecule shown in the region 15 for the organism "D" with the biomolecule shown in the comparative statistical information associated with "influenza" and targeting the region 15. The state identification unit 109 also compares the detection molecule shown in the region 15 for the organism "D" with the biomolecule shown in the comparative statistical information associated with "COVID-19" and targeting the region 15.

Here, the dashed line portion C in the comparative statistical information associated with "influenza" and targeting region 14 shows an increase in the amount of substance of the biomolecule over time. The state identifying unit 109 then determines that the biomolecule shown at this portion C satisfies the same similarity condition with respect to the detected molecule shown in region 14 for organism "D". Also, the dashed line portion D in the comparative statistical information associated with "influenza" and targeting region 15 shows a decrease in the amount of substance of the biomolecule over time. The state identifying unit 109 then determines that the biomolecule shown at this portion D satisfies the same similarity condition with respect to the detected molecule shown in region 15 for organism "D".
On the other hand, the comparative statistical information associated with "COVID-19" and targeting region 14 indicates that the amount of substance of the biomolecule does not change over time. The state identifying unit 109 then determines that this biomolecule does not satisfy the same-similar condition with respect to the detection molecule shown in region 14 for organism "D". The comparative statistical information associated with "COVID-19" and targeting region 15 also indicates that the amount of substance of the biomolecule does not change over time. Therefore, the state identifying unit 109 also determines that this biomolecule does not satisfy the same-similar condition with respect to the detection molecule shown in region 15 for organism "D".
In this case, the state identifying unit 109 extracts the biomolecule indicated in the statistical information associated with "influenza" as a biomolecule satisfying the same similarity condition. Note that the comparison statistical information indicating the biomolecule extracted by the state identifying unit 109 as satisfying the same similarity condition may be referred to as extracted statistical information below.

Next, the state identifying unit 109 identifies the state of the target organism from the extracted statistical information. More specifically, the state identifying unit 109 identifies the comprehensive state associated with the extracted statistical information as the state of the target organism (S207). Note that the state of the target organism identified by the state identifying unit 109 may be referred to as a specific state hereinafter.
In this example, the condition identifying unit 109 identifies "influenza," which is a comprehensive condition associated with the extracted statistical information, as the condition of the living body "D."

In this example, it has been explained that the biomolecules shown in the comparative statistical information associated with "influenza" and the biomolecules shown in the comparative statistical information associated with "COVID-19" both satisfy the same similarity condition for the detected molecules included in region 20.

Here, Fig. 11(G) is a diagram showing biomolecules included in region 7 of the early distribution information and the late distribution information, comparative statistical information associated with "influenza" and targeting region 7, and comparative statistical information associated with "COVID-19" and targeting region 7. In each diagram shown in Fig. 11(G), the horizontal axis indicates the time point at which the biomolecule was obtained, and the vertical axis indicates the substance amount of the biomolecule. In Fig. 11(G), the symbol "●" indicates a biomolecule shown in the early distribution information, and the symbol "▲" indicates a biomolecule shown in the late distribution information.
As shown in FIG. 11(G), in region 7 for organism "D", there is no change between the amount of substance of the biomolecule shown in the early distribution information and the amount of substance of the biomolecule shown in the later distribution information. Also, in the comparative statistical information associated with "influenza" and targeting region 7, there is no change in the amount of substance of the biomolecule over time. On the other hand, the dashed line portion E in the comparative statistical information associated with "COVID-19" and targeting region 7 shows an increase in the amount of substance of the biomolecule over time. Therefore, when, for organism "D", the biomolecule in region 20 shows the change at portion B in FIG. 10(E) and the biomolecule in region 7 shows the change at portion E in FIG. 11(G), the state identification unit 109 identifies the state of organism "D" as "COVID-19".

In this embodiment, the state identification unit 109 identifies the state of the target organism as "influenza" in response to the biomolecule indicated in the comparative statistical information associated with "influenza" satisfying the same similarity condition with respect to each of the detection molecules contained in region 20, region 15, and region 14. Here, while the biomolecule indicated in the comparative statistical information associated with "influenza" satisfies the same similarity condition with respect to the detection molecules contained in region 20 and region 15, there are cases in which the biomolecule does not satisfy the same similarity condition with respect to the detection molecules contained in region 14. In this case, the state of the target organism may be identified as a state other than "influenza."

Next, the output unit 110 outputs the information on the specific state to the terminal 30 (S208).
In this example, the output unit 110 displays a status notification screen 40 on the display unit 31 of the terminal 30 as shown in Fig. 12. The status notification screen 40 is a screen for notifying the user of a specific status. The status notification screen 40 displays a biometric identification section 41, a status notification section 42, a time notification section 43, and a future status notification section 44.
In the following description, it is assumed that the date on which the status notification screen 40 is displayed is the same as the date on which the biomolecule indicated in the later distribution information was obtained.

Information for identifying a target living body is displayed in the biometric identification section 41. In the illustrated example, the biometric identification section 41 displays text for "Mr. D" including information for identifying the target living body.
The specific status is displayed in the status notification section 42. In the illustrated example, the status notification section 42 displays the text "Current Status" and the text "Influenza", which is the specific status.

The time notifying unit 43 indicates the time when the target organism entered the specific state. In the illustrated example, the time notifying unit 43 indicates the text "time when the current state was reached" and the text "one day ago", which is the time when the target organism entered the specific state. The time when the target organism entered the specific state is identified by the state identifying unit 109 from the relationship between the time when the comparison organism entered the inclusive state associated with the extracted statistical information and the time when a biomolecule satisfying the identical similarity condition was obtained.
In this example, in the extracted statistical information shown in Figure 7 (F), the time when the biomolecule satisfying the identical similarity condition, i.e., the biomolecule shown at dashed line point A in the figure, was obtained is the time of "1 day", which is one of the individual states. Furthermore, the time when the comparison organism became "influenza" is the time when the biomolecule associated with "infection", which is one of the individual states, was obtained, which is one day before the time of "1 day". Therefore, the state identification unit 109 identifies the time when the target organism became in the specified state as "1 day ago", which is displayed in the time notification unit 43.

The future state notification unit 44 indicates the future state of the target organism specified by the state specification unit 109 together with the time when this future state will be reached. The future state of the target organism specified by the state specification unit 109 may be referred to as the future state below. In the illustrated example, the future state notification unit 44 indicates the text "future state", and the text "1 day later: fever" and the text "5 days later: normal temperature" which include the future state and the time when the future state will be reached. The future state is a state associated with a time later than the time when a biomolecule satisfying the same similarity condition was obtained, among the individual states associated with the extracted statistical information. The time when the future state will be reached is specified by the state specification unit 109 based on the relationship between the time when a biomolecule satisfying the same similarity condition was obtained and the time when the future state was associated.
7(F), the time points when the future states "fever" and "normal temperature" are associated are "day 2" and "day 6", which are one day and five days after the time points when the biomolecules satisfying the same similarity conditions were obtained. Therefore, the condition specifying unit 109 specifies the time points when the target organism will reach the future states of "fever" and "normal temperature" as "one day later" and "five days later", which are displayed on the future condition notifying unit 44.

As described above, in this embodiment, the detection unit 107 detects a biomolecule that satisfies a change condition among the multiple biomolecules shown in the target distribution information. Furthermore, the target determination unit 108 determines the comparison statistical information according to the partitioned area including the biomolecule that satisfies the change condition. Then, the state specification unit 109 specifies the state of the target organism from the relationship between the biomolecule that satisfies the change condition and the biomolecule shown in the comparison statistical information. That is, in this embodiment, the state specification unit 109 specifies the state of the target organism according to the target of the biomolecule that satisfies the condition determined for the change of the biomolecule determined from the molecular information among the multiple biomolecules. Examples of the condition determined for the change of the biomolecule include a change condition. Furthermore, examples of the target of the biomolecule include a biomolecule included in the partitioned area among the multiple biomolecules.
In this case, the state of the target organism can be identified using not only one biomolecule but also multiple biomolecules. In addition, a method for improving the state of the target organism may be determined based on the identified state of the target organism. In other words, the result of identifying the state of the target organism may be used to determine how to interact with the target organism.

In particular, the condition identification system 1 of this embodiment creates a two-dimensional electrophoretic image using components collected from the target organism, without determining in advance which biomolecules will be collected from the target organism, and identifies the state of the target organism from each of the biomolecules shown in the created two-dimensional electrophoretic image.
Here, a method is also conceivable in which a biomolecule to be collected from a target organism is determined in advance according to the target state of the target organism, and the state of the target organism is determined using only the determined biomolecule. However, in this case, even if other biomolecules different from the determined biomolecule change over time, the state of the target organism cannot be determined according to the other biomolecules.
Therefore, in this embodiment, the state of the target living organism is identified using all biomolecules contained in the components obtained from the target living organism.

Note that the state determination unit 109 of this embodiment determines not only the current state of the target organism, but also the future state of the target organism (see FIG. 12). In other words, the above-mentioned "determining the state of the target organism" includes not only determining the current state of the target organism, but also determining the future state of the target organism.

Furthermore, in this embodiment, the biomolecules displayed in the two-dimensional electrophoretic image include not only biomolecules with known functions, but also biomolecules with unknown functions.
Here, a method of identifying the state of the target organism using only biomolecules with known functions is also conceivable. However, in this case, if none of the biomolecules with known functions satisfy the change condition, the state of the target organism cannot be identified. Therefore, in this embodiment, the state of the target organism is identified using not only biomolecules with known functions, but also biomolecules with unknown functions.

Furthermore, in this embodiment, the output unit 110 outputs information according to the subject of a biomolecule that satisfies a condition set for a change in a biomolecule identified from molecular information, with respect to the state of the subject living organism.
In this case, information regarding the state of the target organism can be output using not only one biomolecule but multiple biomolecules.

In particular, in this embodiment, the target of the biomolecule is a portion of the biomolecule that satisfies the change condition.
In this case, even if the biomolecules as a whole do not satisfy the conditions defined for the changes in the biomolecules, the state of the target living organism can be identified.

In addition, in this embodiment, the state of the organism "A" is identified as "influenza" when one biomolecule contained in region 14 and region 15 satisfies the change condition and one biomolecule contained in region 20 satisfies the change condition. As described above, the state of the organism "A" is identified as "COVID-19" when one biomolecule contained in region 20 satisfies the change condition and one biomolecule contained in region 7 satisfies the change condition. That is, in this embodiment, the state identification unit 109 identifies the state of the target organism depending on which of the multiple biomolecules satisfies the change condition.
In this case, the state of the target living organism can be identified according to a plurality of biomolecules.

In this embodiment, the distribution information includes information about biomolecules in each of a plurality of sections divided according to the physical quantities of the biomolecules. The change condition is determined for each of the plurality of sections. Examples of the physical quantities of the biomolecules include molecular weights and isoelectric points. Examples of the sections include partitioned regions.
In this case, the state of the target organism can be identified according to the target physical quantity of the biomolecule that satisfies the change condition.

In addition, in this embodiment, the state of the organism "A" is identified as "influenza" when one biomolecule contained in region 20 satisfies the change condition and when one biomolecule contained in region 14 and region 15 satisfies the change condition. That is, in this embodiment, the state identifying unit 109 identifies the state of the target organism when both the specific target of the first biomolecule and the specific target of the second biomolecule in the target organism satisfy the change condition.
In this case, the accuracy of identifying the state of the target organism can be improved compared to a configuration in which the state of the target organism is identified only based on a specific target in a single biomolecule satisfying a change condition.

In particular, in this embodiment, for organism "A," one biomolecule with a decreased amount of substance in region 20 satisfies the change condition, and one biomolecule with an increased amount of substance in region 14 satisfies the change condition. That is, in this embodiment, an increase in the physical quantity of a specific target in the first biomolecule satisfies the change condition, and a decrease in the physical quantity of a specific target in the second biomolecule satisfies the change condition.
In this case, even if the tendency of change in the physical quantity of the first biomolecule differs from the tendency of change in the physical quantity of the second biomolecule, the state of the target living organism can be identified.

In this embodiment, the state of the organism "A" is identified as "influenza" when one biomolecule contained in the region 20 and one biomolecule contained in the region 14 satisfy the change condition and one biomolecule contained in the region 7 does not satisfy the change condition. In addition, it has been described that the state of the organism "A" is identified as "COVID-19" when one biomolecule contained in the region 20 and one biomolecule contained in the region 7 satisfy the change condition and one biomolecule contained in the region 14 does not satisfy the change condition. That is, in this embodiment, the state identifying unit 109 identifies the state of the target organism as the first state when a specific target in the first biomolecule and a specific target in the second biomolecule of the target organism satisfy the change condition and a specific target in the third biomolecule does not satisfy the change condition. In addition, the state identifying unit 109 identifies the state of the target organism as the second state when a specific target in the first biomolecule and a specific target in the third biomolecule of the target organism satisfy the change condition and a specific target in the second biomolecule does not satisfy the change condition.
In this case, even if a common biomolecule between the first state and the second state satisfies a condition defined for a change in the biomolecule, the state of the target living organism can be identified.

In addition, in this embodiment, the state of the living organism "A" is identified as "influenza" when a portion of one biomolecule contained in region 14 and a portion contained in region 15 satisfy a change condition. That is, in this embodiment, the state identifying unit 109 identifies the state of the target living organism when both the first portion and the second portion of a specific biomolecule satisfy the change condition.
In this case, the accuracy of identifying the state of the target organism can be improved compared to a configuration in which the state of the target organism is identified only in accordance with one portion of one biomolecule satisfying a change condition.

In particular, in this embodiment, for a living organism "A," an increase in the amount of substance in a portion of a biomolecule contained in region 14 satisfies the change condition, and a decrease in the amount of substance in a portion of a biomolecule contained in region 15 satisfies the change condition, and accordingly, the state of this living organism "A" is identified as "influenza." That is, in this embodiment, an increase in a specific physical quantity in the first portion satisfies the change condition, and a decrease in the physical quantity in the second portion satisfies the change condition.
In this case, even if the tendency of the change in the physical quantity in the first portion differs from the tendency of the change in the physical quantity in the second portion, the state of the target living body can be identified.

The method of identifying the state by the state identifying unit 109 is not limited to the above example. For example, the state identified for the target organism may differ depending on whether the change in the amount of substance of the portion included in region 1 satisfies the change condition or the change in the amount of substance of the portion included in region 5 satisfies the change condition for one biomolecule consisting of regions 1 to 5 from the relationship with the biomolecule shown in the comparison statistical information. That is, the state identifying unit 109 may identify the state of the target organism as a first state when a first portion of a specific biomolecule of the target organism satisfies the change condition and a second portion does not, and may identify the state of the target organism as a second state different from the first state when a first portion of a specific biomolecule of the target organism does not satisfy the change condition and a second portion satisfies the change condition.
The state of a biomolecule may differ depending on the site of post-translational modification of the biomolecule. For example, when a phosphate group is added to a specific biomolecule containing an amino acid, the activation state and function of the specific biomolecule differs between when the 20th amino acid is phosphorylated and when the 45th amino acid is phosphorylated. In this embodiment, the respective states of the target organism can be identified from such different states of the biomolecule. More specifically, the respective states of the target organism can be identified from the change in the physical quantity of the biomolecules in the respective different states.

(Another example of the state identification process)
Next, an example of the state specification process that is performed when object distribution information different from the example shown in FIG. 9A is used will be described.
13 and 14 are diagrams showing an example of how the state of a target living organism is identified by the state identifying process.

In the following, an example will be described in which the target organism is an organism "E" and the two target distribution information shown in FIG. 13(A) are use distribution information. In FIG. 13(A), early distribution information and late distribution information for the organism "E" are shown as use distribution information. The early distribution information and late distribution information each show the distribution of seven different biomolecules, similar to the distribution information shown in FIG. 6(A). The early distribution information shown in FIG. 13(A) is target distribution information showing each biomolecule contained in the component obtained from the organism "E" on the day before the organism "E" takes an anticancer drug. The late distribution information shown in FIG. 13(A) is target distribution information showing each biomolecule contained in the component obtained from the organism "E" on the day after the organism "E" takes an anticancer drug. The organism "E" is, for example, a human being.
The region identification unit 103 identifies the region where the biomolecule exists in each piece of usage distribution information. The partitioning unit 104 partitions the region where the biomolecule exists in each piece of usage distribution information into twenty regions consisting of region 1 to region 20, in the same manner as the partitioning performed on the comparison distribution information (see FIG. 6B).

The substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in each partitioned region for each use distribution information. The detection unit 107 determines whether the biomolecule satisfies a change condition for each partitioned region, and detects the biomolecule determined to satisfy the change condition.
In this example, in regions 6, 19, and 20 shown in FIG. 13A, the amounts of substance of the biomolecules shown in the later distribution information have changed from the amounts of substance of the biomolecules shown in the early distribution information. More specifically, in regions 6 and 19, the amounts of substance of the biomolecules shown in the later distribution information have decreased compared to the amounts of substance of the biomolecules shown in the early distribution information. Also, in region 20, the amounts of substance of the biomolecules shown in the later distribution information have increased compared to the amounts of substance of the biomolecules shown in the early distribution information. Then, the biomolecules shown in the early distribution information and the later distribution information of each of regions 6, 19, and 20 are detected by the detection unit 107 as satisfying the change condition.

Next, the target determining unit 108 determines the comparison statistical information to be compared with the detection molecule, which is a biomolecule that satisfies the change condition.
In this example, as shown in FIG. 13(B), the target determining unit 108 determines each piece of statistical information for the region 20 as comparative statistical information for the detection molecule shown in the region 20 for the organism "E". Also, the target determining unit 108 determines each piece of statistical information for the region 19 as comparative statistical information for the detection molecule shown in the region 19 for the organism "E", and determines each piece of statistical information for the region 6 as comparative statistical information for the detection molecule shown in the region 6 for the organism "E". As shown in FIG. 13(B), these comparative statistical information include statistical information associated with the comprehensive state "anticancer drug effective" and statistical information associated with the comprehensive state "anticancer drug ineffective". FIG. 13(B) is a diagram showing information to be compared by the state identifying unit 109. More specifically, FIG. 13(B) is a diagram showing biomolecules included in the early distribution information and the late distribution information, comparative statistical information associated with "anticancer drug effective", and comparative statistical information associated with "anticancer drug ineffective". In addition, in FIG. 13(B), the symbol "●" indicates a biomolecule shown in the early distribution information, and the symbol "▲" indicates a biomolecule shown in the later distribution information.

Next, the state specifying unit 109 extracts biomolecules that satisfy the identical or similar condition from the comparison statistical information.
In this example, the dotted line portion a in the comparative statistical information associated with "anticancer drug effective" and targeting the region 20 shows an increase in the substance amount of the biomolecule over time. The state specifying unit 109 determines that the biomolecule shown in this portion a satisfies the same similarity condition with respect to the detection molecule included in the region 20 for the organism "E". The dotted line portion b in the comparative statistical information associated with "anticancer drug ineffective" and targeting the region 20 shows an increase in the substance amount of the biomolecule over time. The state specifying unit 109 determines that the biomolecule shown in this portion b satisfies the same similarity condition with respect to the detection molecule included in the region 20 for the organism "E". In this case, the state specifying unit 109 compares the biomolecule shown in the extracted comparative statistical information with the detection molecule shown in the partitioned region that has not been compared. More specifically, the state specifying unit 109 compares the detection molecule included in the region 19 for the organism "E" with the comparative statistical information targeting the region 19. Furthermore, the condition specifying unit 109 compares the detection molecules contained in the region 6 for the organism “E” with the comparative statistical information for the region 6 .

Here, the dotted line portion c in the comparative statistical information associated with "anticancer drug effective" and targeting region 19 shows a decrease in the substance amount of the biomolecule over time. The state specifying unit 109 specifies that the biomolecule shown in this portion c satisfies the same similarity condition with respect to the detection molecule included in region 19 for the organism "E". The dotted line portion d in the comparative statistical information associated with "anticancer drug effective" and targeting region 6 shows a decrease in the substance amount of the biomolecule over time. The state specifying unit 109 specifies that the biomolecule shown in this portion d satisfies the same similarity condition with respect to the detection molecule included in region 6 for the organism "E".
On the other hand, the comparative statistical information associated with "no anticancer drug effect" and targeting region 19 indicates that the substance amount of the biomolecule does not change over time. The state specifying unit 109 then determines that this biomolecule does not satisfy the same similarity condition with respect to the detection molecule shown in region 19. On the other hand, the comparative statistical information associated with "no anticancer drug effect" and targeting region 6 indicates that the substance amount of the biomolecule does not change over time. The state specifying unit 109 then determines that this biomolecule does not satisfy the same similarity condition with respect to the detection molecule shown in region 6.

In this case, the state identifying unit 109 extracts the biomolecule indicated in the comparative statistical information associated with "anticancer drug effective" as a biomolecule that satisfies the same similarity condition. The state identifying unit 109 also identifies the comprehensive state "anticancer drug effective" associated with the extracted comparative statistical information as the state of organism "E".
Furthermore, the output unit 110 causes the display unit 31 of the terminal 30 to display a status notification screen 40 (see FIG. 12) relating to the status identified by the status identification unit 109 .

In this example, it has been explained that the biomolecules shown in the comparative statistical information associated with "anticancer drug effective" and the biomolecules shown in the comparative statistical information associated with "anticancer drug ineffective" both satisfy the same similarity condition with respect to the detected molecules contained in region 20.

Here, Fig. 14(C) is a diagram showing biomolecules included in region 1 of the early distribution information and the late distribution information, comparative statistical information associated with "anticancer drug effective" and targeting region 1, and comparative statistical information associated with "anticancer drug ineffective" and targeting region 1. In each diagram shown in Fig. 14(C), the horizontal axis is the time point at which the biomolecule was obtained, and the vertical axis is the substance amount of the biomolecule. In Fig. 14(C), the symbol "●" means a biomolecule shown in the early distribution information, and the symbol "▲" means a biomolecule shown in the late distribution information.
As shown in FIG. 14C, in region 1 for organism "E", there is no change between the amount of substance of the biomolecule shown in the early distribution information and the amount of substance of the biomolecule shown in the later distribution information. Also, in the comparative statistical information associated with "anticancer drug effective" and targeting region 1, there is no change in the amount of substance of the biomolecule over time. On the other hand, the comparative statistical information associated with "anticancer drug ineffective" and targeting region 1 shows an increase in the amount of substance of the biomolecule over time at the dotted line portion e in the figure. Therefore, for organism "E", when the biomolecule contained in region 20 shows a change at portion b in FIG. 13B and the biomolecule contained in region 1 shows a change at portion e in FIG. 14C, the state identification unit 109 identifies the state of organism "E" as "anticancer drug ineffective".

As described above, in this embodiment, the information acquisition unit 101 acquires usage distribution information that can identify the state of a biomolecule over time from before a specific action acts on the target organism to after the specific action acts. An example of the specific action is the ingestion of an anticancer drug. Then, the state identification unit 109 identifies the state of the target organism as a first state related to the specific action when a specific target in the specific biomolecule satisfies a change condition, and identifies the state of the target organism as a second state related to the specific action when a specific target in the specific biomolecule does not satisfy a change condition. An example of the specific target in the specific biomolecule is a part of the biomolecule shown in the region 19 (see FIG. 13(A) and FIG. 13(B)). Also, an example of the first state is a state in which the target organism has an effect due to the ingestion of an anticancer drug. Also, an example of the second state is a state in which the target organism has no effect due to the ingestion of an anticancer drug.
In this case, it is possible to identify the effect on the target organism caused by a specific action being exerted on the target organism.

In particular, in this embodiment, the state determination unit 109 determines the state of the target organism to be a first state when the specific target in the first biomolecule satisfies the change condition and the specific target in the second biomolecule does not satisfy the change condition, and determines the state of the target organism to be a second state when the specific target in the first biomolecule does not satisfy the change condition and the specific target in the second biomolecule satisfies the change condition. An example of the specific target in the second biomolecule is the part of the biomolecule shown in region 1 (see Figures 13(A) and 14(C)).
In this case, the accuracy of identifying the state of the target organism as the second state can be improved compared to a configuration in which the state of the target organism is identified as the second state solely based on the fact that a specific target in the first biological molecule does not satisfy a change condition.

In this embodiment, when the biomolecules contained in region 20, the biomolecules contained in region 19, and the biomolecules contained in region 6 in the usage distribution information all satisfy the change conditions, the state determination unit 109 determines that the target organism is in the first state, but this is not limited to this.
For example, in the usage distribution information, when the biomolecule included in the region 19 satisfies the change condition and the biomolecule included in the region 6 does not satisfy the change condition, if the biomolecule shown in the comparative statistical information that is associated with "anticancer drug effective" and targets the region 19 satisfies the identical similarity condition, the state specifying unit 109 may specify the target organism as "anticancer drug effective". That is, when the specific target in the first biomolecule and the specific target in the second biomolecule both satisfy the change condition, the state specifying unit 109 may specify the state of the target organism as the first state, and when the specific target in the first biomolecule does not satisfy the change condition and the specific target in the second biomolecule satisfies the change condition, the state of the target organism may be specified as the first state. An example of the specific target in the first biomolecule is a portion of the biomolecule included in the region 19. Also, an example of the specific target in the second biomolecule is a portion of the biomolecule included in the region 6 (see FIG. 13(A) and FIG. 13(B)).
In this case, it is possible to prevent the target living organism from being identified as being in a state different from the first state even though the target living organism is in the first state.

In addition, in this embodiment, the condition specifying unit 109 specifies a condition related to a specific action of the target living body as a comprehensive condition associated with a health condition as an individual condition, but the present invention is not limited to this.
The state identifying unit 109 may identify the state of the target organism as a comprehensive state associated with a physical state as an individual state based on, for example, a change in the biomolecules of the target organism. As one example, the state identifying unit 109 may identify the state of the target organism as "training effective" associated with the physical state of "muscle pain" (see FIG. 4(A)). As another example, the state identifying unit 109 may identify the state of the target organism as "training ineffective" associated with the physical state of "fatigue.""Trainingeffective" and "training ineffective" are examples of states related to the implementation of training by the target organism. Implementation of training is also an example of a specific action.
In addition, the state identification unit 109 may identify the state of the target organism as a comprehensive state associated with a brain state as an individual state based on, for example, changes in the biomolecules of the target organism. As an example, the state identification unit 109 may identify the state of the target organism as "improved memory" associated with the brain state "study for 3 hours" (see FIG. 4A) and the physical state "sleep for 8 hours". As another example, the state identification unit 109 may identify the state of the target organism as "deteriorated memory" associated with the brain state "study for 3 hours" and the physical state "sleep for 4 hours". In this way, two or more pieces of information among the individual state information may be associated with the comprehensive state information. "Improved memory" and "deteriorated memory" are examples of states related to learning by the target organism. Learning is also an example of a specific action. In addition, the state identified by the state identification unit 109 as the state of the target organism in which a specific action has occurred may not only be a state in which the specific action has an effect or a state in which the specific action has no effect, but also a state in which a positive effect or a negative effect has occurred as a result of the specific action.
Furthermore, the state identifying unit 109 may identify the state of the target organism as a comprehensive state associated with a cosmetic state as an individual state, for example, based on a change in the biomolecules of the target organism. As one example, the state identifying unit 109 may identify the state of the target organism as "skin age 40 years old" associated with the cosmetic state "skin cream A used" (see FIG. 4(A)). As another example, the state identifying unit 109 may identify the state of the target organism as "skin age 25 years old" associated with the cosmetic state "skin cream B used". "Skin age 40 years old" and "skin age 25 years old" are examples of states related to the use of skin cream by the target organism. The use of skin cream is also an example of a specific action.

(Another example of the state identification process)
Next, an example of the state specification process that is performed when target distribution information different from either the example shown in FIG. 9(A) or the example shown in FIG. 13(A) is used will be described.
15 and 16 are diagrams showing an example of how the state of a target living organism is identified by the state identifying process.

In the following, an example will be described in which the target organism is organism "F" and the four pieces of target distribution information shown in FIG. 15 are usage distribution information. In FIG. 15, four pieces of target distribution information are shown as usage distribution information, each of which has a different time point at which the biomolecules were obtained for organism "F". Each of these four pieces of usage distribution information shows the distribution of seven different biomolecules, similar to the distribution information shown in FIG. 6(A). In the following, the four pieces of usage distribution information shown in FIG. 15 may be referred to as the first time point distribution information, the second time point distribution information, the third time point distribution information, and the fourth time point distribution information, in order from the distribution information at which the biomolecules were obtained earliest. Note that organism "F" is, for example, a banana.

In this example, the partitioning unit 104 partitions all the regions in each piece of usage distribution information. In addition, as shown in FIG. 15, each piece of usage distribution information is a two-dimensional orthogonal coordinate system in which the horizontal axis is indicated by coordinates of 1 to 11 for the isoelectric point of the biomolecule and the vertical axis is indicated by coordinates of 1 to 9 for the molecular weight of the biomolecule. In addition, the larger the coordinate on the horizontal axis, the higher the isoelectric point of the biomolecule, and the larger the coordinate on the vertical axis, the higher the molecular weight of the biomolecule. Then, the partitioning unit 104 partitions the regions indicated in each piece of usage distribution information into regions for each coordinate. In the example shown in FIG. 15, all the regions are partitioned in each piece of usage distribution information, so that partitioned regions where biomolecules are present and partitioned regions where biomolecules are not present are provided.

Although not shown in the figure, in this example, the partitioning unit 104 partitions all areas of the comparative distribution information stored in the memory unit 102 in the same manner as the usage distribution information shown in FIG. 15. Furthermore, the substance amount identification unit 105 identifies the substance amount of the biomolecule contained in the partitioned area in the comparative distribution information for each partitioned area. Furthermore, the statistics unit 106 calculates statistics of the substance amount of the biomolecule for each partitioned area and creates statistical information for each partitioned area.

The substance amount specifying unit 105 specifies the substance amount of the biomolecule contained in each partitioned region for each use distribution information. The detection unit 107 determines whether the biomolecule satisfies a change condition for each partitioned region, and detects the biomolecule determined to satisfy the change condition.
In this example, in the region where the horizontal coordinate is "9" and the vertical coordinate is "5", that is, the region (9, 5), the amount of substance of the biomolecule shown in the second time distribution information is decreased compared to the amount of substance of the biomolecule shown in the first time distribution information. In addition, in the region (9, 5), the amount of substance of the biomolecule shown in the fourth time distribution information is increased compared to the amount of substance of the biomolecule shown in the third time distribution information. Furthermore, in the region (3, 7), the third time distribution information does not show a biomolecule, while the fourth time distribution information shows a biomolecule. In this case, the detection unit 107 detects the biomolecule shown in the first time distribution information and the second time distribution information of the region (9, 5) as satisfying the change condition. In addition, the detection unit 107 detects the biomolecule shown in the third time distribution information and the fourth time distribution information of the region (9, 5) as satisfying the change condition. Furthermore, the detection unit 107 detects the biomolecule indicated in the fourth time point distribution information in the region (3, 7) as satisfying the change condition.
In the region (3, 7), there is no change in the amount of the biomolecule substance in the first, second, and third time point distribution information. More specifically, in the region (3, 7), the biomolecule is not shown in the first, second, and third time point distribution information.

Next, the target determining unit 108 determines the comparison statistical information to be compared with the detection molecule, which is a biomolecule that satisfies the change condition.
In this example, as shown in FIG. 16, the target determination unit 108 determines each of the statistical information targeting the region (9, 5) as comparative statistical information for the detection molecule shown in the region (9, 5) for the organism "F". Also, the target determination unit 108 determines each of the statistical information targeting the region (3, 7) as comparative statistical information for the detection molecule shown in the region (3, 7) for the organism "F". As shown in FIG. 16, these comparative statistical information include statistical information associated with the comprehensive state "good quality" and statistical information associated with the comprehensive state "poor quality". FIG. 16 is a diagram showing information to be compared by the state identification unit 109. More specifically, FIG. 16 is a diagram showing the biomolecules shown in each of the usage distribution information, the comparative statistical information associated with "good quality", and the comparative statistical information associated with "poor quality".

In the biomolecules of organism "F" shown in FIG. 16, the ● symbol represents the biomolecule shown in the first time point distribution information, the ▲ symbol represents the biomolecule shown in the second time point distribution information, the ■ symbol represents the biomolecule shown in the third time point distribution information, and the ★ symbol represents the biomolecule shown in the fourth time point distribution information.

Next, the state determining unit 109 extracts biomolecules that satisfy the identical similarity condition from the comparison statistical information.
In this example, the dotted line portion A in the comparative statistical information for the region (9, 5) associated with the inclusive state "good quality" shows a decrease in the amount of substance of the biomolecule over time. The state identifying unit 109 determines that the biomolecule shown in this portion A satisfies the same similarity condition with respect to the biomolecule shown in the region (9, 5) of the first time point distribution information and the second time point distribution information. The dotted line portion B in the comparative statistical information for the region (9, 5) associated with the inclusive state "poor quality" shows a decrease in the amount of substance of the biomolecule over time. The state identifying unit 109 determines that the biomolecule shown in this portion B satisfies the same similarity condition with respect to the biomolecule shown in the region (9, 5) of the first time point distribution information and the second time point distribution information.

In this example, the dotted line portion C in the comparative statistical information for the region (9, 5) associated with the inclusive state "good quality" shows an increase in the amount of substance of the biomolecule over time. The state specifying unit 109 determines that the biomolecule shown in this portion C satisfies the same similarity condition with respect to the biomolecule shown in the region (9, 5) of the third time point distribution information and the fourth time point distribution information. The dotted line portion D in the comparative statistical information for the region (3, 7) associated with the inclusive state "good quality" shows an increase in the amount of substance of the biomolecule over time. The state specifying unit 109 determines that the biomolecule shown in this portion D satisfies the same similarity condition with respect to the biomolecule shown in the region (3, 7) of the fourth time point distribution information.
On the other hand, the comparative statistical information associated with the inclusive state "poor quality" and targeting the region (9, 5) does not show an increase in the amount of substance of the biomolecule over time. In this case, the state identifying unit 109 determines that the biomolecule shown in this comparative statistical information does not satisfy the same similarity condition with respect to the biomolecule shown in the region (9, 5) of the third time point distribution information and the fourth time point distribution information. In addition, the comparative statistical information associated with the inclusive state "poor quality" and targeting the region (3, 7) shows that the amount of substance of the biomolecule does not change over time. In this case, the state identifying unit 109 determines that the biomolecule shown in this comparative statistical information does not satisfy the same similarity condition with respect to the biomolecule shown in the region (3, 7) of the fourth time point distribution information.

In this case, the state identifying unit 109 extracts the biomolecule indicated in the comparison statistical information associated with "good quality" as a biomolecule that satisfies the same similarity condition. In addition, the state identifying unit 109 identifies "good quality", which is the comprehensive state associated with the extracted comparison statistical information, as the state of biomolecule "F".
Furthermore, the output unit 110 causes the display unit 31 of the terminal 30 to display a status notification screen 40 (see FIG. 12) relating to the status identified by the status identification unit 109 .

As described above, no biomolecules are shown in the region (3, 7) in the first time point distribution information, the second time point distribution information, and the third time point distribution information of the usage distribution information shown in Fig. 15. That is, in this embodiment, the usage distribution information includes information on the presence of biomolecules at multiple time points in each of the multiple partitioned regions, and the multiple partitioned regions include regions in which no biomolecules of the target organism are present at at least one of the multiple time points.
In this case, the state of the target living organism can be identified based on the change in the biomolecules in the area where the biomolecules were not present.

In this embodiment, the information acquisition unit 101 acquires information including information that can identify changes in the biomolecules of the target organism in a first period and information that can identify changes in the biomolecules in a second period after the first period. The first period can be, for example, a period from the time when the biomolecules shown in the first time distribution information are obtained to the time when the biomolecules shown in the second time distribution information are obtained. The second period can be, for example, a period from the time when the biomolecules shown in the third time distribution information are obtained to the time when the biomolecules shown in the fourth time distribution information are obtained. The state identification unit 109 then identifies the state of the target organism according to the target of the biomolecules that satisfy the change condition for the change in the first period and the target of the biomolecules that satisfy the change condition for the change in the second period.
In this case, the accuracy of identifying the state of the target organism can be improved compared to a configuration in which the state of the target organism is identified based only on whether a biomolecule changes in a single period of time and satisfies a change condition.

In particular, in this embodiment, the target biomolecule that satisfies the change condition for the change in the first time period is the same as the target biomolecule that satisfies the change condition for the change in the second time period. For example, the target biomolecule is the biomolecule included in the region (9, 5) (see FIG. 15 and FIG. 16).
In this case, the state of the target living organism can be identified based on changes in a specific biomolecule over multiple periods.

Second Embodiment
Next, a second embodiment will be described. The second embodiment is common to the first embodiment in that a condition specifying system 1 specifies a condition of a target living body.
On the other hand, the second embodiment differs from the first embodiment in that the user of the condition identifying system 1 can select what type of condition the condition identifying unit 109 is to identify for the target living organism.
The second embodiment will be described below. The second embodiment has the same configuration as the first embodiment except for the points described below. In addition, the following will describe the configuration different from the first embodiment, and the description of the same configuration as the first embodiment may be omitted.

Fig. 17 is a flowchart showing the flow of the condition identification process of the second embodiment. Fig. 18 is a diagram showing an image displayed on the display unit 31 of the terminal 30 when the condition identification process of the second embodiment is performed. Fig. 19 is a diagram showing an example of how the condition of a target living organism is identified by the condition identification process of the second embodiment. In the following, an example will be described in which the target living organism is a living organism "G". Furthermore, the living organism "G" is, for example, a human being.
In the present embodiment as well, when a user of the condition identification system 1 designates target distribution information to be used in the condition identification process and instructs the server device 10 to execute the condition identification process, the condition identification process is started.

First, the information acquisition unit 101 determines whether or not an instruction to identify the state of the target living organism has been given (S301 in FIG. 17). When the user inputs an instruction to identify the state of the target living organism into the terminal 30, the terminal 30 notifies the server device 10 that an instruction has been given. The information acquisition unit 101 determines whether or not an instruction to identify the state of the target living organism has been given based on whether or not this notification has been received. Furthermore, while a negative result continues, the information acquisition unit 101 repeats the processing of step 301.

When the terminal 30 notifies the server device 10 that an instruction to identify the state of the target living organism has been given (YES in S301), the information acquisition unit 101 determines whether or not a type of state has been selected for the state identification unit 109 to identify (S302). When the user selects on the terminal 30 which type of state the state identification unit 109 is to identify, type information indicating the type of selected state is transmitted from the terminal 30 to the server device 10. The information acquisition unit 101 determines whether or not a type of state has been selected for the state identification unit 109 to identify, depending on whether or not this type information has been acquired. Also, while a negative result continues, the information acquisition unit 101 repeats the process of step 302.

In this example, when the user inputs an instruction to identify the state of a target living organism to the terminal 30 in the state identification process, a selection screen 50 is displayed on the display unit 31 of the terminal 30, as shown in Fig. 18 (A). The selection screen 50 is a screen for allowing the user to select which type of state is to be identified by the state identification unit 109. The selection screen 50 shows a selection prompting unit 51, an item unit 52, and a decision unit 53.
The selection prompting section 51 displays information for prompting the user to select which type of status should be identified by the status identification section 109. In the illustrated example, the selection prompting section 51 displays the text "Please select the type of status you want to know about."

Items that the user can select are displayed in the item section 52. In the item section 52, an item selection section 521 is displayed.
The item selection section 521 displays items classified according to the type of condition identified by the condition identification section 109. In the illustrated example, the item selection section 521 displays items including "health condition,""physicalcondition,""braincondition," and "beauty condition" classified according to the condition identified by the condition identification section 109. In the illustrated example, "health condition" is selected in the item selection section 521.

When "health condition" is selected in the item selection section 521 and the user selects the decision section 53, as shown in FIG. 18 (B), a selection item section 522 and a detailed selection section 523 are displayed in the item section 52 of the selection screen 50. In addition, a return section 54 and a decision section 55 are displayed on the selection screen 50.

The selection item section 522 of the item section 52 displays the item selected in the item selection section 521. In the illustrated example, the selection item section 522 displays "health condition".
The detailed selection section 523 of the item section 52 shows items that are further divided from the items shown in the selection item section 522. The items shown in the detailed selection section 523 are items that can be selected by the user. In the illustrated example, the detailed selection section 523 shows items including "influenza,""COVID-19,""norovirus," and "hepatitis," which are further divided from the "health condition" shown in the selection item section 522. Also, in the illustrated example, "influenza" is selected in the detailed selection section 523.

When the user selects the return section 54, the selection screen 50 shown in FIG.
In addition, when “influenza” is selected in the detailed selection section 523 and the user selects the decision section 55, type information indicating “influenza”, which is the type of the selected condition, is transmitted from the terminal 30 to the server device 10.

When the information acquisition unit 101 acquires the type information (YES in S302), the process proceeds to the next step. The target determination unit 108 extracts, from the statistical information stored in the storage unit 102, statistical information to which the type information acquired by the information acquisition unit 101 is associated as comprehensive state information, as statistical information to be compared with the biomolecule of the target organism (S303). More specifically, the target determination unit 108 extracts statistical information to which the type information is associated, the statistical information targeting an area in which a biomolecule satisfying the extraction condition is shown. The extraction condition is a condition determined for the target determination unit 108 to extract statistical information to be compared with the biomolecule of the target organism. The extraction condition of this embodiment is determined from the viewpoint of extracting statistical information targeting an area in which a biomolecule whose substance amount has changed over time is shown. In addition, in this embodiment, the extraction condition is determined to be that the substance amount of the biomolecule included in the statistical information has changed by more than a predetermined value over time.

19A is a diagram showing an example of statistical information stored in the storage unit 102. In this example, as shown in FIG. 19A, the storage unit 102 stores statistical information associated with the type information "influenza" as comprehensive state information. In addition, the dashed line portion I in the figure of the statistical information associated with "influenza" and targeting the region 20 shows a decrease in the substance amount of the biomolecule over time. In addition, the dashed line portion II in the figure of the statistical information associated with "influenza" and targeting the region 15 shows a decrease in the substance amount of the biomolecule over time. In addition, the dashed line portion III in the figure of the statistical information associated with "influenza" and targeting the region 14 shows an increase in the substance amount of the biomolecule over time. In this case, the target determination unit 108 extracts the statistical information associated with "influenza" and targeting the region 20, the statistical information associated with the region 15, and the statistical information associated with the region 14 as statistical information that satisfies the extraction condition.

Next, the substance amount specifying unit 105 specifies, for each partition area, the substance amount of the biomolecule shown in the partition area for each piece of usage distribution information (S304).
Next, the target determining unit 108 determines (S305) a biomolecule of a target organism to be compared with the biomolecule indicated in the statistical information extracted in step 303. More specifically, the target determining unit 108 determines a biomolecule included in a partitioned area in which the statistical information extracted in step 303 and the target coordinates are the same as each other as the biomolecule to be compared.

In this example, as described above, statistical information for region 20, statistical information for region 15, and statistical information for region 14 are extracted. In this case, the target determination unit 108 determines the biomolecules included in region 20, the biomolecules included in region 15, and the biomolecules included in region 14, among the biomolecules shown in the usage distribution information for organism "G", as the biomolecules to be compared.
Fig. 19(B) is a diagram showing the statistical information extracted in step 303 and the biomolecules of the target organism determined by the target determination unit 108 as the target for comparison with this statistical information. Fig. 19(B) shows statistical information for region 20, statistical information for region 15, and statistical information for region 14, each of which is associated with "influenza" as statistical information. Fig. 19(B) also shows the biomolecules included in region 20, the biomolecules included in region 15, and the biomolecules included in region 14 in each usage distribution information as the biomolecules of organism "G". In each diagram shown in Fig. 19(B), the horizontal axis indicates the time point when the biomolecule was obtained, and the vertical axis indicates the substance amount of the biomolecule.

Next, the state identification unit 109 compares the statistical information with the biomolecules of the target organism, and identifies the state of the target organism from the comparison result (S306). More specifically, the state identification unit 109 identifies the state of the target organism based on whether or not the biomolecules determined by the target determination unit 108 as the subjects of comparison satisfy a specific condition. The specific condition is a condition determined so that the state identification unit 109 identifies the state of the target organism as a comprehensive state associated with the statistical information. In this embodiment, the specific condition is determined such that the difference between the value of the change over time in the substance amount of the biomolecules in the target organism and the value of the change in the substance amount of the biomolecules that satisfy the extraction condition in the statistical information is equal to or less than a predetermined value.

When the biomolecules of the target organism satisfy the specific condition, the state identifying unit 109 identifies the inclusive state associated with the statistical information extracted in step 303 as the state of the target organism. When the biomolecules of the target organism do not satisfy the specific condition, the state identifying unit 109 identifies a non-inclusive state associated with the statistical information extracted in step 303 as the state of the target organism.
In addition, the biomolecule of the target organism determined as the comparison target by the target determination unit 108 may be a biomolecule in multiple partitioned regions. In this case, the state determination unit 109 determines that the biomolecule of the target organism satisfies the specific condition when all of the biomolecules contained in each partitioned region satisfy the specific condition. In addition, the state determination unit 109 determines that the biomolecule of the target organism does not satisfy the specific condition when any of the biomolecules contained in each partitioned region does not satisfy the specific condition.

In this example, as shown in FIG. 19B, the amount of substance of the biomolecules contained in region 20 for organism "G" does not change over time.
Furthermore, the amount of substance of the biomolecule contained in region 15 for the organism "G" increases over time. This change in the amount of substance of the biomolecule is an opposite trend to the decrease in the amount of substance of the biomolecule associated with "influenza" and shown in the statistical information for region 15.
Furthermore, for the organism "G," the amount of substance of the biomolecules contained in region 14 does not change over time.
In this case, the state identifying unit 109 determines that the biomolecule of the target organism does not satisfy the specific condition. Furthermore, the state identifying unit 109 identifies the organism "G" as being in a state other than "influenza" associated with the statistical information.

Next, the output unit 110 outputs information on the state identified by the state identifying unit 109 to the terminal 30 (S307).
18(C), the output unit 110 displays a status notification screen 40 on the display unit 31 of the terminal 30. The status notification screen 40 of this embodiment includes a biometric identification unit 41, a status notification unit 42, a warning unit 45, a detail prompting unit 46, a negation unit 47, and a decision unit 48.

The biometric identification section 41 displays the text of "Mr. G" including information for identifying the target biometric entity.
The status notification section 42 displays the text "Current Status" and the text "Not influenza" indicating the status identified by the status identification section 109.

The attention-calling unit 45 displays information for calling the user's attention to the state of the target living body. In this embodiment, the output unit 110 displays the attention-calling unit 45 when a biomolecule included in any of the partitioned regions in the usage distribution information satisfies a change condition.
When the amount of a substance of a biomolecule of a target organism changes, the state of the target organism may change accordingly. Therefore, in the server device 10 of the present embodiment, when the biomolecule indicated in the usage distribution information satisfies the change condition, even if the biomolecule of the target organism does not satisfy the specific condition, the server device 10 alerts the user to the state of the target organism.

In this example, as shown in FIG. 19(B), among the biomolecules contained in region 15 for organism G, the biomolecule shown at dashed line portion IV in the figure increases in substance amount over time, and the biomolecule shown at this portion IV satisfies the change condition. Therefore, a warning section 45 is displayed on the status notification screen 40. In the illustrated example, warning section 45 displays the text "Caution" and the text "Your health condition may be in another unhealthy state."

The detail prompting section 46 displays information that prompts the user to have the condition specifying section 109 specify a detailed condition of the target living body. In the illustrated example, the detail prompting section 46 displays the text "Do you want to specify another condition?"
When the user selects the negation section 47, the detailed condition of the target living body is not specified, and the condition specifying process ends.
Furthermore, when the user selects the determination unit 48, the condition identification process shown in Fig. 8 is executed. As a result, the detailed condition of the target living body is identified, and a condition notification screen 40 (see Fig. 12) relating to the identified condition is displayed on the display unit 31 of the terminal 30.

As described above, in this embodiment, when the user selects which type of state the state specifying unit 109 should specify for the target organism, the information acquiring unit 101 acquires type information indicating the type of the selected state. Then, the target determining unit 108 determines the usage distribution information used to specify the state of the target organism from the type of state specified from the type information. Furthermore, the state specifying unit 109 specifies the state of the target organism from the change in the biomolecules indicated in the usage distribution information determined by the target determining unit 108. That is, in this embodiment, the information acquiring unit 101 acquires item information regarding the target item specified by the state specifying system 1 among a plurality of items classified for the state of the organism. Then, the state specifying unit 109 specifies the state of the target organism according to the change specified from the usage distribution information for the target of the biomolecule determined according to the item specified from the item information. For example, type information can be given as the item information. The information acquiring unit 101 can also be regarded as an item information acquiring means for acquiring item information.
In this case, the state of the target living organism can be identified according to the biomolecule, among the multiple biomolecules, that is determined by the item of the target state identified for the target living organism.

In this embodiment, when the usage distribution information is the two target distribution information shown in Fig. 9(A) and "influenza" is selected as the type of condition to be identified by the condition identification unit 109, the condition identification unit 109 identifies the condition of the target living body as "influenza." In this case, the output unit 110 causes the display unit 31 of the terminal 30 to display the condition notification screen 40 shown in Fig. 12.
13A and “anticancer drug effect” is selected as the type of condition identified by the condition identifying unit 109, the condition identifying unit 109 identifies the condition of the target organism as “anticancer drug effect.” The output unit 110 displays a condition notification screen 40 related to the condition identified by the condition identifying unit 109 on the display unit 31.
15 and “quality” is selected as the type of state to be identified by the state identifying unit 109, the state identifying unit 109 identifies the state of the target organism as “good quality.” The output unit 110 displays a state notification screen 40 related to the state identified by the state identifying unit 109 on the display unit 31.

Next, we will explain the examples carried out by the inventor.

[Example]
The present inventors prepared two-dimensional electrophoretic images showing a plurality of biomolecules of a living organism for each of the biomolecules obtained at a plurality of time points, and examined the change over time in the substance amount of each biomolecule shown in the two-dimensional electrophoresis from the prepared plurality of two-dimensional electrophoretic images. In this embodiment, the living organisms to be examined for the change in the substance amount of the biomolecules were three racehorses. When describing the three horses individually, they may be referred to as "horse A", "horse B", and "horse C", respectively. When describing the horses to be examined for the change in the substance amount of the biomolecule without distinction, they may be referred to as the subject horses. The present inventors prepared two-dimensional electrophoretic images showing each protein contained in the serum using serum obtained from the subject horses.
The age of all the subject horses at the time when the serum was obtained was 2 years old. Furthermore, at the time when the serum was obtained from the subject horses, the body weight of Horse A was 348 kg, the body weight of Horse B was 379 kg, and the body weight of Horse C was 377 kg.

20(A) is a diagram showing a two-dimensional electrophoretic image showing each protein contained in serum obtained from horse A at a single time point. In this two-dimensional electrophoretic image, the distribution of the substance amount of each protein is shown in a two-dimensional orthogonal coordinate system in which the horizontal axis shows the distribution of the isoelectric point of the protein and the vertical axis shows the distribution of the molecular weight of the protein. Each black image shown in this two-dimensional electrophoretic image represents a protein. Also, the higher the density of this black image, the greater the substance amount of each protein.
The inventors obtained serum from each subject horse at 15 time points and created a two-dimensional electrophoretic image for each serum obtained. More specifically, each subject horse was infected with influenza virus, serum was obtained from the subject horse on each day up to 14 days after the day of infection, and a two-dimensional electrophoretic image was created for each serum obtained.

The inventor also divided the area shown in the two-dimensional electrophoretic image into multiple regions. Figure 20(B) is a diagram showing the state in which the two-dimensional electrophoretic image of Figure 20(A) has been divided into multiple regions. In the example shown, the area shown in the two-dimensional electrophoretic image is divided into 338 regions. Furthermore, the twenty regions, which are an example of the divided regions, are numbered 1 to 20, respectively. The inventor divided the regions in each of the two-dimensional electrophoretic images he created in the same manner as the example shown in Figure 20(B).

The inventor also identified the amount of protein contained in each divided region of each of the created two-dimensional electrophoretic images. Furthermore, the inventor calculated statistics for each subject horse regarding the amount of protein contained in the identified region. More specifically, the inventor calculated statistics regarding the amount of protein contained in each day since the subject horse was infected with influenza virus. Furthermore, the calculation of these statistics was performed for each divided region in the two-dimensional electrophoretic image. The inventor then evaluated the change in the amount of protein contained in each region over time from the calculated statistics.

Comparative Example
In addition, as a comparative example to the examples, the body temperature of each subject horse was measured every day up to 14 days after the day the subject horse was infected with the influenza virus, and the amount of amyloid A substance, an example of a protein, obtained from the subject horse was measured.
Figure 21 (A) is a diagram showing the body temperatures of the subject horses. In Figure 21 (A), the horizontal axis shows the number of days that have passed since the subject horse was infected with influenza virus, and the vertical axis shows the body temperature of the subject horse. In Figure 21 (A), the symbol "●" indicates horse A, the symbol "▲" indicates horse B, and the symbol "■" indicates horse C.
Moreover, Figure 21 (B) is a diagram showing the amount of amyloid A substance obtained from the subject horses. In Figure 21 (B), the horizontal axis shows the number of days that have passed since the subject horses were infected with influenza virus, and the vertical axis shows the amount of amyloid A substance in the subject horses. In Figure 21 (B), the symbol "●" indicates horse A, the symbol "▲" indicates horse B, and the symbol "■" indicates horse C.

As can be seen from FIG. 21(A), for each of the subject horses, an increase in body temperature was confirmed two days after infection with the influenza virus. Therefore, the horse is identified as being infected with the influenza virus based on the increase in body temperature two days after infection with the influenza virus. In other words, it takes two days after the horse is infected with the influenza virus for a user to identify that the horse is infected with the influenza virus from the horse's body temperature.

Furthermore, from FIG. 21(B), an increase in the amount of amyloid A was confirmed for each of the subject horses three days after infection with the influenza virus. Therefore, the horse is identified as being infected with the influenza virus based on the increase in the amount of amyloid A three days after infection with the influenza virus. In other words, it takes three days for a user to identify that a horse is infected with the influenza virus from the amount of amyloid A in the horse. The method described in the comparative example can be regarded as an example of a method for identifying the state of a subject organism using only biomolecules with known functions.

Figure 22 shows statistics calculated for the amount of each protein contained in the serum of each subject horse. Figure 22 shows statistics for the amounts of proteins contained in regions where changes in the amounts of proteins were confirmed, out of 338 regions defined in the two-dimensional electrophoretic image. In each of the graphs shown in Figure 22, the horizontal axis shows the number of days since the subject horse was infected with influenza virus, and the vertical axis shows the amount of protein contained in the region defined in the two-dimensional electrophoretic image. In the illustrated example, the amount of protein substance for each subject horse and statistics on the amount of protein substance for each subject horse are shown for each number of days since the subject horse was infected with the influenza virus, for areas 11, 18, 20, 25, 37, 38, 42, 56, 58, 59, 80, 101, 105, 106, 107, 108, 116, 119, 124, 125, 136, 137, and 169.
The number "1" in each region of Fig. 22 represents the amount of protein substance of horse A. The number "2" in each region of Fig. 22 represents the amount of protein substance of horse B. The number "3" in each region of Fig. 22 represents the amount of protein substance of horse C. The solid lines in each region of Fig. 22 represent statistics calculated for the amount of protein substance of each subject horse.

22, it was confirmed that the amount of protein changed over time after infection with influenza virus for all the subject horses. In particular, the statistics for regions 11, 18, 20, 101, 105, 106, 107, 119, 124, 125, 136, and 137 confirmed a decrease in the amount of protein after one day had passed since infection with influenza virus. Furthermore, the statistics for regions 37, 38, 42, 56, 58, 80, and 116 confirmed an increase in the amount of protein after one day had passed since infection with influenza virus.
Therefore, the horse is identified as being infected with the influenza virus based on the change in the amount of substance of the protein shown in FIG. 22 one day after the horse is infected with the influenza virus. That is, it takes one day after the horse is infected with the influenza virus for the user to identify the horse as being infected with the influenza virus from the change in the amount of substance of the protein shown in FIG. 22. In other words, when the user uses a two-dimensional electrophoretic image showing biomolecules to identify the state of the living organism, the state of the living organism can be identified earlier than when the body temperature of the living organism or the amount of substance of amyloid A in the living organism is used. In addition, since the state of the living organism is identified from the change in each biomolecule shown in the two-dimensional electrophoretic image, the accuracy of identifying the state of the living organism can be improved compared to a configuration in which the state of the living organism is identified from the change in a single biomolecule. The method described in this embodiment can be regarded as an example of a method for identifying the state of a target living organism using not only biomolecules with known functions but also biomolecules with unknown functions.

Although the embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear from the claims that the technical scope of the present invention also includes various modifications or improvements to the above embodiments.

In the above-described embodiment, the state identified by the state identification unit 109 for the target organism varies depending on the usage distribution information (see FIG. 9(A), FIG. 13(A), FIG. 15) used in the state identification process. Here, the biomolecules shown in each usage distribution information are all biomolecules contained in the components obtained from each target organism, and it is not predetermined which biomolecules are obtained from the target organism.

In the above-described embodiment, the information acquiring unit 101 acquires a two-dimensional electrophoretic image including information on a plurality of biomolecules as molecular information, but the invention is not limited to this.
The information acquiring unit 101 may acquire molecular information, which is information about a single biomolecule, for each biomolecule. In other words, the information acquiring unit 101 acquiring molecular information about multiple biomolecules includes both acquiring molecular information, which is information about multiple biomolecules, and acquiring multiple pieces of molecular information for each biomolecule.

In addition, in the above-described embodiment, the information indicating the distribution of physical quantities of biomolecules is described as information indicating the molecular weight of the biomolecules, information indicating the isoelectric point of the biomolecules, information indicating the amount of substance of the biomolecules, etc., but is not limited to this.
The information indicating the distribution of the physical amount of the biomolecule may be, for example, information indicating the distribution of the charge amount of the biomolecule. Also, for example, in the two-dimensional electrophoretic image, instead of the distribution of the isoelectric point of the biomolecule, the distribution of the charge amount of the biomolecule may be shown. Also, the information indicating the distribution of the physical amount of the biomolecule may be, for example, information indicating the distribution of the hydrophobicity of the biomolecule or information indicating the three-dimensional structure of the biomolecule. In this case, in the two-dimensional electrophoretic image, the distribution of the hydrophobicity of the biomolecule may be shown on one of the x-axis and the y-axis, and the distribution of the molecular weight of the biomolecule may be shown on the other. Also, in the two-dimensional electrophoretic image, the three-dimensional structure of the biomolecule may be shown on one of the x-axis and the y-axis, and the distribution of the isoelectric point of the biomolecule may be shown on the other. In addition, in the two-dimensional electrophoretic image, the information indicated on the x-axis and the information indicated on the y-axis may be information indicating any physical property as long as they are information indicating different physical properties. Furthermore, the biomolecule analyzer 20 may create a three-dimensional electrophoretic image in which three pieces of information indicating different physical properties among the above-mentioned pieces of information indicating physical properties are shown on the x-axis, y-axis, and z-axis, respectively. This three-dimensional electrophoretic image may be used to identify the state of a living organism.

In the above-described embodiment, the distribution information indicates the distribution of biomolecules, but the present invention is not limited to this.
For example, information indicating the amount of substance of a biomolecule may be created for each isoelectric point of the biomolecule or for each charge amount of the biomolecule. Also, information indicating the amount of substance of a biomolecule may be created for each molecular weight of the biomolecule. Then, the server device 10 may identify the distribution of the biomolecule from the information created for each isoelectric point of the biomolecule, the information created for each charge amount of the biomolecule, the information created for each molecular weight of the biomolecule, etc.

In the above-described embodiment, the molecular information is information indicating the physical quantities of biomolecules, but the present invention is not limited to this.
The molecular information may be, for example, information indicating whether or not a biomolecule is present. In other words, the molecular information may be any information related to a biomolecule.

In addition, in the above-described embodiment, the biomolecules to be statistically analyzed by the statistical unit 106 are biomolecules that are associated with the same state among the individual states, but this is not limited to this.
The biomolecules to be statistically collected by the statistical unit 106 may be, for example, biomolecules obtained from multiple organisms when the number of days that have elapsed since the multiple organisms entered a specific inclusion state is the same.

In the above-described embodiment, the biomolecules for which statistics are collected by the statistics unit 106 are biomolecules of different organisms, but the present invention is not limited to this.
The biomolecules that are the subject of statistical analysis by the statistical unit 106 may include biomolecules of the same organism.

In the above embodiment, the distribution information used by the detection unit 107 for comparison is described as two pieces of distribution information among three or more pieces of usage distribution information that have the closest time points at which the biomolecules were obtained, but this is not limited thereto. The distribution information used by the detection unit 107 for comparison may be two pieces of distribution information among three or more pieces of usage distribution information that have different time points at which the biomolecules were obtained. As an example, the detection unit 107 may use the first time point distribution information and the fourth time point distribution information shown in FIG. 15 for comparison to detect a change from the biomolecules shown in the first time point distribution information to the biomolecules shown in the fourth time point distribution information.

In addition, in the above-described embodiment, the biomolecules compared by the detection unit 107 and the biomolecules determined as the targets for comparison by the target determination unit 108 are each described as biomolecules contained in partitioned areas with the same coordinates, but this is not limited to this.
The biomolecules compared by the detection unit 107 and the biomolecules determined as the comparison targets by the target determination unit 108 may be biomolecules contained in partitioned regions with different coordinates, as long as they are contained in corresponding partitioned regions. In this case, the detection unit 107 and the target determination unit 108 may identify the biomolecules contained in the corresponding partitioned regions on the condition that the biomolecules to be compared are each shown in a predetermined coordinate range. Furthermore, the detection unit 107 and the target determination unit 108 may identify the biomolecules contained in the corresponding partitioned regions from the arrangement relationship in the distribution information between the biomolecules to be compared and other biomolecules.

In addition, in the above-described embodiment, the state identification unit 109 identifies the state of the target organism as a comprehensive state associated with statistical information indicating biomolecules that satisfy the same similarity condition, but this is not limited to this.
The state identifying unit 109 may identify the state of the target organism as any one of the individual states associated with statistical information indicating biomolecules that satisfy the same similarity condition.

In the above-described embodiment, the state specifying unit 109 specifies the state of the target living organism in accordance with changes in the substance amounts of biomolecules in the target living organism, but the present invention is not limited to this.
For example, the detection unit 107 may detect a change in the molecular weight of a biomolecule, a change in the isoelectric point of a biomolecule, a change in the charge amount of a biomolecule, a change in the presence or absence of a biomolecule, etc. from the molecular information related to the target organism. Then, the state identification unit 109 may extract molecular information related to a comparison organism that has experienced a change corresponding to the change detected by the detection unit 107, and identify the state of the comparison organism associated with the extracted molecular information as the state of the target organism. In other words, the change in the biomolecule used to identify the state of the target organism is not limited to a change in the substance amount of the biomolecule.

In addition, in the above-described embodiment, the server device 10 acquires multiple pieces of molecular information for a single living organism at different times when the biomolecules were obtained, and identifies changes in the biomolecules over time from the acquired multiple pieces of molecular information, but is not limited to this.
The server device 10 may acquire molecular information indicating changes in a biomolecule over time, such as information indicating changes in a biomolecule over a specific period of time. Examples of changes in a biomolecule include changes in a specific physical quantity of a biomolecule and changes in the presence or absence of a biomolecule. Examples of specific physical quantities include the amount of substance, molecular weight, isoelectric point, and charge amount. The server device 10 may then identify changes in a biomolecule over time from the acquired molecular information. That is, the molecular information may be any type of information as long as it allows the server device 10 to identify changes in a biomolecule.

In the above-described embodiment, the change condition is set to be a change in a biomolecule, but the change condition is not limited to this.
For example, the change condition may be that the biomolecules do not change. As one example, the change condition may be that the difference in the amount of substance between two biomolecules is equal to or less than a predetermined value. Then, the state identification unit 109 may extract molecular information indicating a biomolecule of a comparison organism corresponding to a biomolecule that satisfies the change condition, and identify the state associated with the extracted molecular information as the state of the target organism.
As an example, when the change condition is satisfied by the fact that the amount of substance of the biomolecule contained in region 20 does not change and the change condition is satisfied by the fact that the amount of substance of the biomolecule contained in region 15 does not change in the usage distribution information shown in Fig. 9 (B), the state of the target organism is specified as another state other than either "influenza" or "COVID-19". An example of the other state is a state in which the target organism is healthy.

The above-mentioned molecular information that can identify the state of a biomolecule over time from before a specific action is exerted on a target organism to after this specific action is exerted includes not only information that can identify that a biomolecule has changed over time, but also information that can identify that a biomolecule has not changed over time.

The detection unit 107 may also detect the degree of change in the biomolecules. More specifically, the detection unit 107 may detect which of multiple stages the degree of change in the biomolecules contained in the partitioned area is from each usage distribution information. Then, the state identification unit 109 may extract distribution information related to a comparison organism in which a change corresponding to the stage detected by the detection unit 107 has been shown, and identify the state associated with the extracted distribution information as the state of the target organism.

Further, the state specifying unit 109 compares the detected molecules with the biomolecules shown in the statistical information, but this is not limiting.
The state identification unit 109 may, for example, compare the detection molecule with a biomolecule indicated in comparative distribution information relating to one comparison organism, and if the biomolecule indicated in the comparative distribution information satisfies the same similarity condition, may identify the state associated with the comparative distribution information as the state of the target organism.

In addition, in the above-described embodiment, the subject organism is different from the comparison organism, but this is not limiting.
The subject organism may be the same organism as the comparison organism.

In addition, in the above-described embodiment, the condition determination unit 109 determines the condition of the target organism by comparing the biomolecules of the target organism with the biomolecules of a comparison organism, which is an organism with the same attributes as the target organism, but this is not limited to this.
The state identification unit 109 may identify the state of the target organism by comparing the biomolecules of the target organism with the biomolecules of a comparison organism, which is an organism with attributes different from the target organism. For example, the state identification unit 109 may identify the state of the target organism by comparing the biomolecules of a human organism, which is the target organism, with the biomolecules of a horse, which is the comparison organism. In this case, the server device 10 may identify the characteristics of the physical amount of the biomolecules for each attribute of the organism, and correct the physical amount of the biomolecules shown in the comparison molecule information for each attribute of the comparison organism based on the identified characteristics. Then, the server device 10 may identify the state of the target organism by comparing the biomolecules of the target organism with the corrected biomolecules in the comparison molecule information.

Furthermore, the condition identifying unit 109 may identify the condition of the target living organism based on the results of machine learning.
The state specifying unit 109 learns the relationship between the distribution of biomolecules and the state of a living organism, using as teacher data distribution information and individual state information in which the distribution of the substance amount of each biomolecule shown in the two-dimensional electrophoretic image is associated with the state of the living organism at the time when the biomolecule shown in the two-dimensional electrophoretic image was obtained from the living organism. The state specifying unit 109 generates a learning model that inputs the distribution information and outputs the individual state information based on the learning result. Then, the state specifying unit 109 may specify the state of the target living organism from the distribution information related to the target living organism based on the generated learning model. In other words, the state specifying unit 109 may specify the state of the target living organism from the distribution pattern of the biomolecules shown in the distribution information related to the target living organism by associating the distribution of the biomolecules shown in the input distribution information with the individual state information and performing pattern recognition. In this way, the state specifying unit 109 may specify the state of the target living organism without comparing the biomolecules of the target living organism with other biomolecules. The biomolecules that are pattern-recognized by the state specifying unit 109 in the distribution information are regarded as targets of biomolecules that satisfy a condition defined for a change in the biomolecules identified from the molecular information. The condition defined for a change in the biomolecules identified from the molecular information is that the biomolecules identified from the distribution information are pattern-recognized by the state specifying unit 109. The targets of biomolecules that satisfy the condition are the distribution of biomolecules shown in the distribution information.

In the above embodiment, the condition specifying system 1 is configured to include the server device 10 and the biomolecule analyzer 20, but the present invention is not limited to this.
For example, an apparatus in which the server apparatus 10 and the biomolecule analysis apparatus 20 are integrated may be provided in the condition determination system 1. That is, a single apparatus provided in the condition determination system 1 may be used to analyze biomolecules, create molecular information, and determine the condition of a target living organism.

In the present embodiment, the user inputs individual status information and comprehensive status information by operating the terminal 30 and the server device 10, but the present invention is not limited to this.
For example, a living body may be photographed using a photographing means such as a video camera. Furthermore, a living body shown in the photographed video may be analyzed using an analysis means for analyzing the living body, and individual status information or comprehensive status information showing the status of the living body may be created from the analysis result, and the created information may be transmitted to the information acquisition unit 101. In this manner, the information acquisition unit 101 may acquire information from a functional means different from the terminal 30.

In addition, in the present embodiment, the server device 10 is configured to identify the state of the target living body, but the present invention is not limited to this.
For example, the terminal 30 may have the functions of the server device 10. In other words, the terminal 30 may have the functions of the server device 10, such as the information acquisition unit 101, the storage unit 102, the region identification unit 103, the partition unit 104, the substance amount identification unit 105, the statistics unit 106, the detection unit 107, the target determination unit 108, the state identification unit 109, and the output unit 110.

In addition, a program for implementing an embodiment of the present invention may be provided in a state where it is stored on a computer-readable recording medium, such as a magnetic recording medium (such as a magnetic tape or a magnetic disk), an optical recording medium (such as an optical disk), a magneto-optical recording medium, or a semiconductor memory. It may also be provided using a communication means such as the Internet.

Although multiple embodiments have been described above, the configuration included in one embodiment may be interchanged with the configuration included in another embodiment, or the configuration included in one embodiment may be added to another embodiment.

1...Status identification system, 10...Server, 20...Biomolecular analysis device, 30...Terminal, 40...Status notification screen, 101...Information acquisition unit, 102...Storage unit, 109...Status identification unit, 110...Output unit

Claims (16)

an acquisition means for acquiring a distribution image in which each of a plurality of biomolecules of a specific organism is shown as a distribution of a first physical quantity and a second physical quantity, for the distribution at a plurality of time points;
A determination means for determining the state of the specific living body;
Equipped with
The first physical quantity and the second physical quantity are different physical quantities of a biomolecule,
the distribution image is an image in which the first physical quantity and the second physical quantity are identified for each of the plurality of biomolecules of the specific organism from a molecular image displayed as an image showing the biomolecule,
the molecular image is displayed in an area of the distribution image corresponding to at least the first physical quantity,
The identification by the identification means is performed such that the state of the specific living body is identified to a different state depending on an area of the distribution image that satisfies a change condition determined for a change in the second physical quantity identified from a plurality of distribution images acquired for the distribution at a plurality of time points ,
The acquisition means acquires molecular information indicating the second physical quantity of a biomolecule of a living organism for a plurality of biomolecules of the living organism;
The identification by the identification means is performed by comparing the second physical amount of the biomolecule of the specified organism identified from the distribution image with the second physical amount of the biomolecule of the specified organism identified from the molecular information;
the biomolecules of the specific organism used for the comparison in the distribution image are not determined before the distribution image is acquired by the acquisition means, and the plurality of biomolecules shown in the distribution image are candidates for the comparison;
The identification by the identification means is performed based on the molecular information that identifies a change in the second physical quantity of a biomolecule that satisfies a relationship condition defined regarding a relationship between the change in the second physical quantity of the biomolecule of the specified organism,
The information processing system, wherein the relationship is a relationship between the change in the second physical quantity of a biomolecule of the specific organism, which is the first physical quantity identified from a region of the distribution image that satisfies the change condition, and the change in the second physical quantity identified from the molecular information for the biomolecule in the first physical quantity corresponding to the region . The identification means is
When a first region in the distribution image satisfies the change condition, a state indicating a relationship with a first item among the items classified with respect to the state of the specific living body is not specified, but a state indicating a relationship with a second item different from the first item among the items is specified;
The information processing system of claim 1, wherein when a second region in the distribution image different from the first region satisfies the change condition, a state indicating a relationship with the first item is identified for the specific living organism without identifying a state indicating a relationship with the second item. The identification means identifies the state of the specific living body as a state indicating a relationship with at least one item among a plurality of items classified regarding the state of the living body,
which of the plurality of items is the item for which the relationship is identified by the identification means is not determined prior to the comparison;
The information processing system according to claim 1 , wherein the identification means identifies the item for which the relationship is to be identified and the state indicating the relationship with the item in response to identifying the molecular information in which a change in the second physical quantity that satisfies the relationship condition is identified. 2. The information processing system according to claim 1, wherein the region of the distribution image that satisfies the change condition is a region in which the molecular image corresponding to a portion of a biomolecule of the specific organism is displayed. a display area of the distribution image is divided into a plurality of sections according to the first physical quantity of a biomolecule;
2. The information processing system according to claim 1, wherein the change condition is determined for each of the plurality of sections. The information processing system according to claim 5 , wherein the plurality of sections includes a section in which the molecular image is not displayed. The plurality of biomolecules of the specific organism include a first biomolecule and a second biomolecule different from the first biomolecule,
The information processing system according to claim 1, wherein the identification means identifies the state in accordance with a state in which both an area in the distribution image in which the molecular image relating to the first biomolecule is displayed and an area in which the molecular image relating to the second biomolecule is displayed satisfy the change condition. a region in the distribution image in which the molecular image of the first biomolecule is displayed is a region that satisfies the change condition due to an increase in the second physical quantity,
The information processing system according to claim 7 , wherein the region in the distribution image in which the molecular image of the second biomolecule is displayed is a region that satisfies the change condition by a decrease in the second physical quantity. The plurality of biomolecules of the specific organism further includes a third biomolecule different from both the first biomolecule and the second biomolecule,
The identification means is
When a region in the distribution image in which the molecular image related to the first biomolecule of the specific organism and a region in which the molecular image related to the second biomolecule are displayed satisfy the change condition and a region in which the molecular image related to the third biomolecule is displayed does not satisfy the change condition, the state is specified as a first state;
The information processing system of claim 7, wherein the state is identified as a second state when an area in the distribution image where the molecular image relating to the first biomolecule of the specific organism and an area where the molecular image relating to the third biomolecule are displayed satisfy the change condition, and an area where the molecular image relating to the second biomolecule is displayed does not satisfy the change condition. The information processing system according to claim 1, wherein the identification means identifies the state in response to both of an area in the distribution image where the molecular image relating to a first portion of a specific biomolecule among the multiple biomolecules in the specific organism is displayed and an area where the molecular image relating to a second portion of the specific biomolecule different from the first portion is displayed, satisfying the change condition. a region in the distribution image in which the molecular image relating to the first portion is displayed is a region in which the change condition is satisfied by an increase in the second physical quantity,
11. The information processing system according to claim 10 , wherein the region in the distribution image in which the molecular image relating to the second portion is displayed is a region in which the change condition is satisfied by a decrease in the second physical quantity. The acquisition means acquires the distribution image showing the distribution at a time point before a specific action is applied to the specific living body, and the distribution image showing the distribution at a time point after the action is applied to the specific living body,
The identification means is
specifying the state as a first state related to the action when a region in the distribution image in which the molecular image related to a specific biomolecule among the plurality of biomolecules is displayed satisfies the change condition;
The information processing system according to claim 1 , wherein when an area in the distribution image in which the molecular image relating to the specific biomolecule is displayed does not satisfy the change condition, the state is specified as a second state relating to the action. The plurality of biomolecules of the specific organism include a first biomolecule and a second biomolecule different from the first biomolecule,
The specific biomolecule is a first biomolecule,
The identification means is
When an area in the distribution image where the molecular image of the first biomolecule is displayed satisfies the change condition and an area in which the molecular image of the second biomolecule is displayed does not satisfy the change condition, the state is specified as the first state;
The information processing system of claim 12, wherein the state is identified as the second state when, in the distribution image, an area in which the molecular image relating to the first biomolecule is displayed does not satisfy the change condition but an area in which the molecular image relating to the second biomolecule is displayed satisfies the change condition. The plurality of biomolecules of the specific organism include a first biomolecule and a second biomolecule different from the first biomolecule,
the specific biomolecule is the first biomolecule,
The identification means is
specifying the state as the first state when a region in the distribution image in which the molecular image of the first biomolecule is displayed and a region in which the molecular image of the second biomolecule is displayed both satisfy the change condition;
The information processing system of claim 12, wherein the state is identified as the second state when, in the distribution image, an area in which the molecular image relating to the first biomolecule is displayed does not satisfy the change condition but an area in which the molecular image relating to the second biomolecule is displayed satisfies the change condition. The distribution images acquired by the acquisition means include a first distribution image showing the distribution at a first time point and a second distribution image showing the distribution at a second time point after the first time point,
2 . The information processing system according to claim 1 , wherein the specifying means specifies the state according to an area in the first distribution image that satisfies the change condition and an area in the second distribution image that satisfies the change condition. A program for causing a computer to function as the information processing system according to any one of claims 1 to 15 .