JP7095040B2 - Information processing equipment, information processing methods and information processing programs - Google Patents

Description

The present invention relates to an information processing apparatus, an information processing method and an information processing program.

A technique for transmitting position information of a communication device with low power consumption is disclosed.

Japanese Unexamined Patent Publication No. 2020-042681

However, in the above-mentioned prior art, the manually driven vehicle predetermines communication device information including information on the current position to the communication partner by low power consumption wide area wireless communication (LPWA). It is merely transmitting sequentially with an interval between transmissions. Normally, the communication device does not consume much power just to acquire the position information, but when the communication device transmits the position information to the outside (especially when moving at high speed), it consumes a large amount of power. Even if the power consumption per transmission is reduced, when the position information is repeatedly transmitted, the power consumption increases according to the number of cases. If it is a manually driven vehicle as exemplified by the above-mentioned conventional technique, the power supply capacity may not be insufficient, but if it is a terminal device (handheld device) such as a smartphone, it cannot be said that the power supply capacity is sufficient. ..

Further, in recent years, from the viewpoint of personal information protection, there is a tendency that restrictions on transmitting the position information of an individual terminal device to the outside become stricter due to the specifications of terminal devices such as smartphones, and the business operator is an individual. It is extremely difficult to acquire the position information of the terminal device one by one. For example, regarding the permission to use the location information of an individual terminal device, the setting of "always permit" is becoming an option. Further, there is a problem that the location information cannot be transmitted when the application is running in the background due to the control on the OS (Operating System) side.

Therefore, the inventors are studying a method of embedding (vectorizing) position information and transmitting it. However, since the embedding method and parameters change depending on how the embedded information (embedding information) is used, embedding cannot be performed if the goal (purpose of use) is not determined. Furthermore, there is a problem that the embedding information collected for a specific purpose cannot be used for other purposes.

The present application has been made in view of the above, and an object thereof is to connect a plurality of forms of embedding information in advance to generate embedding information that can be used for multiple purposes.

The information processing apparatus according to the present application is coupled with an acquisition unit for acquiring position information, a conversion unit for converting the position information into a plurality of different vectors by different methods, and a coupling unit for connecting a plurality of different vectors. A transmission unit for transmitting the vector is provided.

According to one aspect of the embodiment, it is possible to generate embedding information that can be used for multiple purposes by connecting a plurality of forms of embedding information in advance.

FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. FIG. 3 is a diagram showing a configuration example of the terminal device according to the embodiment. FIG. 4 is a diagram showing a configuration example of the information processing apparatus according to the embodiment. FIG. 5 is a diagram showing an example of a conversion information database. FIG. 6 is a diagram showing an example of a vector information database. FIG. 7 is a sequence diagram showing a processing procedure according to the embodiment. FIG. 8 is a diagram showing an example of a hardware configuration.

Hereinafter, an information processing apparatus, an information processing method, and an embodiment for implementing an information processing program (hereinafter referred to as “embodiment”) according to the present application will be described in detail with reference to the drawings. Note that this embodiment does not limit the information processing apparatus, information processing method, and information processing program according to the present application. Further, in the following embodiments, the same parts are designated by the same reference numerals, and duplicate explanations are omitted.

[1. Overview of information processing method]
First, with reference to FIG. 1, an outline of an information processing method performed by the information processing apparatus according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. In FIG. 1, a case where the terminal device embeds and transmits the position information will be described as an example.

As shown in FIG. 1, the information processing system 1 includes a terminal device 10 and an information providing device 100. The terminal device 10 and the information providing device 100 are connected to each other via a network N (see FIG. 2) so as to be able to communicate with each other by wire or wirelessly.

The terminal device 10 is a smart device such as a smartphone or tablet used by a user U (user), and communicates with an arbitrary server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). It is a portable terminal device capable of performing. Further, the terminal device 10 is a screen such as a liquid crystal display and has a screen having a touch panel function, and various types of display data such as contents such as tap operation, slide operation, scroll operation, etc. from the user by a finger, a stylus, or the like. Accept the operation of. The operation performed on the area of the screen on which the content is displayed may be an operation on the content. Further, the terminal device 10 may be not only a smart device but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

Here, the terminal device 10 acquires its own position information and converts (vectorizes) the acquired position information into a vector. That is, the terminal device 10 performs so-called embedding (embedding) and generates an embedding vector (embedding vector) based on the position information. Then, the terminal device 10 transmits an embedding vector based on the position information to the information providing device 100.

The information providing device 100 is an information processing device that receives an embedding vector based on position information from the terminal device 10 and analyzes the behavior (location, behavior, movement, etc.) of the user U based on the embedding vector based on the position information. , Realized by server equipment, cloud system, etc.

[1-1. Advantages of converting location information to vectors]
First, the advantage of converting position information into a vector will be described.

Conventionally, the terminal device 10 has transmitted location information to the information providing device 100, but at this time, the terminal device 10 rounds the location information in order to consider the privacy of the user U and reduce the amount of data communication. (Rounding process) was performed, and the position information was rounded and converted into coarse position information before transmission. For example, if the position information is the latitude / longitude information of GPS (Global Positioning System) coordinates, the values below the second decimal place at the decimal angle of latitude and longitude are rounded up or down (rounding is also possible). It was converted to the position information of the value.

In this case, when the information providing device 100 tries to estimate the behavior of the user U based on the position information, the rounded position information is a rough value. Even if it could be identified, it was sometimes not possible to identify a narrow place (and / or the difference between indoors and outdoors) such as an adjacent small building or room. Therefore, although the rough behavior of the user U can be estimated, the detailed behavior of the user U may not be estimated. For example, if a small building A and a building B (for example, a convenience store and a fast food store) are adjacent to each other within the range indicated by the rounded location information of the user U, the user U is the building A and the building. In some cases, it was not possible to identify from the location information whether it was in B or inside or outside the building (or site). What the information providing device 100 (server side) really wants to know is not the position information itself of the user U, but the behavior of the user U indicated by the position information (that is, the context of the user U).

This time, when the terminal device 10 converts (vectorizes) its own position information, it converts the raw data of the position information before the rounding process (rounding process) into a vector. Therefore, the terminal device 10 can generate an embedding vector after specifying a narrow place (and / or a difference between indoors and outdoors) such as an adjacent small building or room. Therefore, the information providing device 100 can know the detailed behavior of the user U based on the embedding vector. That is, the information providing device 100 can acquire specific and detailed information about the behavior of the user U from the terminal device 10 rather than receiving the conventional rounded position information. Further, since it is the information providing device 100 (server side) that defines the vector value of the embedding vector and the content that it means, even if a third party sees only the vector value of the embedding vector, what is the vector value? It's safe because I don't understand what it means.

[1-2. Overview of information processing method for embedding and transmitting location information]
Next, with reference to FIG. 1, an outline of an information processing method for embedding and transmitting location information will be described.

As shown in FIG. 1, the terminal device 10 of the user U acquires its own position information (step S1).

Then, the terminal device 10 accumulates the acquired position information (step S2).

Then, the terminal device 10 embeds the position information by a plurality of different methods, converts (vectorizes) the plurality of position information into one vector for each method, and is based on the same plurality of position information. A plurality of embedding vectors (embedded vectors) generated by different methods are generated (step S3).

Then, the terminal device 10 combines a plurality of embedding vectors generated by different methods based on the same plurality of position information (step S4).

Then, the terminal device 10 transmits the combined embedding vector to the information providing device 100 (step S5).

Then, the information providing device 100 receives the combined embedding vector from the terminal device 10 (step S6).

Then, the information providing device 100 analyzes the behavior (location, behavior, movement, etc.) of the user U based on the embedding vector received from the terminal device 10 (step S7).

[1-3. Example 1]
Next, the first embodiment of the information processing method of embedding and transmitting the above-mentioned position information will be described.

For example, in step S1, the terminal device 10 possessed by each of the plurality of users U located in each area uses positioning technology such as GNSS (Global Navigation Satellite System) or indoor positioning technology. Each acquires its own position information. At this time, the terminal device 10 acquires the time information indicating the time when the position information is acquired together with the position information of the terminal device 10.

Subsequently, in step S2, the terminal device 10 accumulates the acquired position information as a position history (for example, location history, action history, etc.).

Subsequently, in step S3, the terminal device 10 arranges the accumulated position information in time series to generate sequence data (time series data), and when embedding each sequence data, a plurality of one sequence data are used. Convert to multiple different vectors by different methods to generate an embedding vector (embedded vector). That is, the terminal device 10 converts the position information into a plurality of different vectors by different methods, and generates a plurality of different embedding vectors based on the same position information. At this time, the terminal device 10 collectively converts a plurality of position information into a single multidimensional vector in each method. For example, when the terminal device 10 acquires the position information every minute, the position information every minute is arranged in chronological order and the position for 30 minutes (for example, 8:01 to 8:30) in each time zone. Generate a singular embedding vector from the information.

For example, the terminal device 10 generates sequence data (time-series data) in which position information for each minute in 30 minutes is arranged in time series. Alternatively, the terminal device 10 generates sequence data in which the position information for the first minute in 30 minutes and the difference for each minute with respect to the position information are arranged in chronological order. Then, the terminal device 10 generates an embedding vector based on the sequence data.

For example, the terminal device 10 uses sequence data as a data set and converts the sequence data into a multidimensional real-valued vector by using a machine learning method using a neural network or the like. That is, the terminal device 10 inputs sequence data into each of a plurality of different vector conversion models generated through machine learning using, for example, RNN (Recurrent Neural Network) or LSTM (Long short-term memory), and each of them. Get multiple different embedding vectors as the output of. The terminal device 10 may receive a plurality of different vector conversion models from the information providing device 100.

The RNN and LSTM may be a neural network based on an attention mechanism. Attention can handle data such as sentences where the arrangement before and after is important. Further, the terminal device 10 may use a similar natural language processing model. By generating the embedding vector from the sequence data using such a model, it is possible to realize the generation of the embedding vector with more emphasis on the order of information.

Alternatively, the terminal device 10 may convert the sequence data into a plurality of different embedding vectors according to a plurality of different conversion rules (rules) (on a rule basis).

However, the sequence data is only an example. Actually, for example, the raw data of the position information may be input to the model in order in chronological order as it is.

Here, when the terminal device 10 embeds based on the position information data according to the above vector conversion model, a predetermined conversion rule, or the like, the output value becomes a vector expression. Arranging these output vectors in chronological order shows the user's travel path. For example, although the specific latitude and longitude are unknown, it is shown that the vector space has moved from point A to point B. That is, the result (output value) of embedding by the terminal device 10 using the position information is the embedding vector. This embedding vector is a value indicating the user's behavior (value indicating the context).

The timing at which the terminal device 10 performs embedding is arbitrary. For example, the accumulated position information may be embedded at the timing when the transmission of the position information from the terminal device 10 to the information providing device 100 is permitted. Further, the storage of the location information may be started from the time when the application (application) is started, and the stored location information may be embedded at the timing when the use of the application is finished. Further, when the accumulated position information reaches a predetermined number (for example, 100 cases), the accumulated position information may be embedded. Alternatively, the accumulated location information may be embedded when the user moves from the place where he / she was staying (when the location information changes significantly).

Subsequently, in step S4, the terminal device 10 combines different versions (plurality of patterns) of embedding results (vectors). It should be noted that different versions of the embedding results are generated using different models, conversion rules, and the like.

For example, among the multidimensional embedding vectors having 20 or more dimensions, the embedding result of the 100 m mesh is arranged in the first element to the tenth element, and the embedding result of the 500 m mesh is arranged in the eleventh element to the twentieth element. That is, the embedding results of a plurality of different patterns are combined. As a result, it is possible to generate a vector showing the user U's behavior in 100 m units and a vector showing the user U's behavior in 500 m units. When embedding, movement in 100m units can be recognized only by a vector of 100m mesh, but large movement in 500m units is difficult to recognize. By adding a vector of 500m mesh, it becomes possible to recognize a large movement in units of 500m. The 100m mesh and the 500m mesh are only examples. For example, an embedding result such as a 10 m mesh or a 1 km mesh may be arranged. In addition, the embedding result of the regional mesh may be arranged.

Alternatively, among the multidimensional embedding vectors having 20 or more dimensions, the vector indicating the destination of the user U is arranged in the first to tenth elements, and the moving means of the user U is arranged in the eleventh to the twentieth elements. The indicated vector may be arranged. For example, the first element indicates "whether or not it is heading for home", the second element indicates "whether or not it is heading for work", and the third element indicates "whether or not it is heading for store 1". Place the vector. In addition, the 11th element is "whether or not it is moving on foot", the 12th element is "whether or not it is moving by vehicle", the 13th element is "whether or not it is moving by rail", etc. Place a vector indicating. The means of transportation can be determined from the movement speed and the movement route based on the position information. Further, a vector indicating a time zone, a day of the week, a holiday, etc. may be arranged after the 21st element. By combining different embedding results in this way, the elements of the multidimensional embedding vector are divided by a predetermined number of elements according to the vector type, and a vector showing highly versatile information that can be used for multiple purposes is arranged. You may.

Further, the terminal device 10 may add additional information to the embedding vector. For example, the terminal device 10 may add information defining a data type to the embedding vector. The data type indicates, for example, the type of individual embedding result combined.

Further, the terminal device 10 may add noise to the embedding vector. For example, in the terminal device 10, in order to prevent personal information such as position information from being specified from the embedding vector, noise such as dummy data is added to a predetermined element (may be a plurality of elements) of the multidimensional embedding vector. Place the vector that becomes. At this time, the terminal device 10 may define an element in which a vector that becomes noise is arranged in the multidimensional embedding vector as a data type. The server side (information providing device 100) may remove noise from the embedding vector, or may execute the embedding process while including the noise.

Subsequently, in step S5, the terminal device 10 transmits the embedded vector coupled in step S4 to the information providing device 100 via the network N (see FIG. 2).

The timing at which the ending vector to which the terminal device 10 is connected is transmitted to the information providing device 100 is arbitrary. For example, it may be the timing when the transmission of the position information from the terminal device 10 to the information providing device 100 is permitted. Further, the application (application) may be started or used, at a predetermined time, at a predetermined cycle (every 2 hours, etc.), or the like. Further, the information providing device 100 may request the terminal device 10 to transmit the embedding vector.

Subsequently, in step S6, the information providing device 100 receives each of the combined embedding vectors from the terminal device 10 possessed by each of the plurality of users U located in each area via the network N.

Subsequently, in step S7, the information providing device 100 decomposes the combined embedding vector and arranges the embedding vector generated by the same method in the same specific space. By arranging the embedding vector in a specific space, the information providing device 100 clearly grasps the similarity and change of the behavior of the user U and / or the similarity and the difference of the behavior between the user U and another user. can do. For example, since the terminal device 10 embeds the position information (latitude and longitude, etc.), if they are at the same position, the embedding vector of the same value is generated. The information providing device 100 arranges the embedding vector of the same value at the same position on the specific space.

In this case, since the information providing device 100 is not mapped (associated) with the position (latitude / longitude, etc.) on the map, the specific position cannot be specified, but the user U is from point A to B during this time zone. It is possible to confirm a specific action of the user U, such as moving to a point, then moving to a point C, and returning to a point A again. Further, the information providing device 100 can confirm the fact of the movement of the user U without using the position information.

As described above, in the present embodiment, when transmitting the user's position information, the position information acquired by the terminal device is not transmitted as it is to the receiving side (server side), but after the position information is embedded. Send to the receiving side. Since the embedded information is difficult to restore on the receiving side, it is difficult to specify accurate location information, and the problem of personal information is solved. That is, not only the amount of information is reduced, but also the raw data (GPS position information itself) detected by the terminal side cannot be restored by the server side, so that it is secure. In addition, the problem of battery consumption can be solved by collectively embedding and transmitting the acquired position information.

Further, the embedding information can be used for multiple purposes by combining a plurality of different embedding results generated by different methods based on the same position information to generate the embedding information. For example, when the content (usage) to be estimated is not decided on the server side, NPP (Next Place Prediction) or POI (Point of Interest) natural language processing (Bag-) Data can be embedded in multiple formats such as of-words) or data type (Data Type).

[1-4. Example 2]
Next, the second embodiment of the information processing method for embedding and transmitting the above-mentioned position information will be described.

For example, in step S5, the terminal device 10 may transmit some of its own position information to the information providing device 100 together with the combined embedding vector. That is, the terminal device 10 may transmit a set of the combined embedding vector and its own position information to the information providing device 100.

For example, when there are no restrictions on the transmission of location information (when location information can be transmitted freely), the terminal side will have an average of 17 points per day if "always allow". Information was sent to the server side, but recently, restrictions have become stricter, and the terminal side has come to send only about three points of location information to the server side. Therefore, for example, the terminal device 10 generates and combines a plurality of embedding vectors obtained by vectorizing the position information of 14 points out of the position information of 17 points by a plurality of different methods, and combines the position information of the remaining 3 points on the server side. You may send it to. That is, the terminal device 10 may transmit the embedding information obtained by vectorizing a part of the position information by a plurality of different methods and combining them, and the remaining position information itself. The remaining position information is the number of position information that can be transmitted by the terminal side.

The embedding vector indicates the behavior of the user U, but does not indicate the specific position of the user U. Therefore, the information providing device 100 (server side) cannot know in which area the user U's action was performed. Therefore, by combining the embedding vector and the rounded position information, the information providing device 100 (server side) can know the approximate location of the user U's action in which area. For example, the embedding vector can indicate that "user U had lunch" but does not indicate "in which area" user U had lunch. By adding the rounded position information indicating a rough place (for example, XX ward △△ town) to this, it is possible to know the approximate place where the user U had lunch.

Further, it may be carried out in combination with the above-mentioned Example 1. For example, in step S7, when the information providing device 100 arranges the embedding vector in the specific space, the position and map of the specific space are used by using the position information of three points to map the specific space and the map. It can be mapped to the upper position (latitude and longitude, etc.). This allows the information providing device 100 to estimate a location to some extent, although it is not perfect.

Further, since the terminal device 10 embeds the position information (latitude and longitude, etc.), if they are at the same position, the embedding vector of the same value is generated. Therefore, the information providing device 100 can also estimate that the user U is staying at the same place based on the embedding vector of the same value. As a result, the information providing device 100 "is at home" on a daily basis when the user U routinely stays at the same place for a long time at night without specifying a specific position. It is also possible to presume that you are "at work" if you are staying in a place different from the night for a long time.

[1-5. Example 3]
Next, the third embodiment of the information processing method for embedding and transmitting the above-mentioned position information will be described.

For example, in step S3, the terminal device 10 embeds the user U's behavior (user's context) represented by two or more position information as a vector value based on the sequence data in which the position information is arranged in chronological order. You may generate a vector.

For example, the terminal device 10 inputs sequence data to a vector conversion model generated through machine learning using, for example, RNN or LSTM, and acquires an embedding vector as an output. At this time, even if the RNN and LSTM have the same data, the output changes in the order in which they are input. For example, if you are moving in the order of "Position 1"-> "Position 2"-> "Position 3", "Attendance" (outward route) is output, and "Position 3"-> "Position 2"-> "Position 1". If you are moving in, output "I came home" (return trip). By using a model that provides information according to the time series of such information, it is possible to generate an embedding vector based on the characteristics of the transition of the position information.

In this way, when the embedding vector is generated from the sequence data using LSTM, the embedding vector indicating whether or not the user has performed a predetermined action is generated from the action of the user U (context of the user U) indicated by the sequence data. be able to. For example, in the terminal device 10, when the sequence data of the position information indicates that the user U has moved from the area where the home is located to the area where the workplace is located, the element corresponding to the action of "going to work" is a predetermined value. It is possible to generate an embedding vector that takes.

The RNN and LSTM may be a neural network based on an attention mechanism. Further, the terminal device 10 may use a similar natural language processing model.

Alternatively, the terminal device 10 may convert the sequence data into an embedding vector according to a predetermined conversion rule. For example, in the terminal device 10, the first acquired position information # 1 is within a predetermined range from the home of the user U, the second acquired position information # 2 is within a predetermined range from the station, and finally. When the acquired position information # 3 is within a predetermined range from the place of employment, an embedding vector may be generated in which the element corresponding to the action of "going to work" takes a predetermined value.

However, the sequence data is only an example. Actually, for example, the raw data of the position information may be input to the model in order in chronological order as it is.

Further, for example, when the terminal device 10 does not show a large change in the position information acquired in 30 minutes, the terminal device 10 obtains the average value, the median value, etc. of the position information acquired in the 30 minutes, and obtains the average value, the median value, etc. of the position information acquired in the 30 minutes. And the embedding vector may be generated from the position information.

Then, in step S7, the information providing device 100 decomposes the combined embedding vector, and the user U "is at home", "goes to work", and "goes to shop" according to the vector value of the decomposed embedding vector. , "Moving by train" and other actions (user context) are determined. At this time, the information providing device 100 may determine the behavior for each time zone based on the values indicated by the plurality of elements of the multidimensional embedding vector. For example, when the information providing device 100 includes a value indicating "going out in the morning" and a value indicating "going to work" in the embedding vector, the information providing device 100 determines that "going to work in the morning". You may.

Here, the value of the element of the embedding vector corresponds to the behavior of the user U. For example, in the case of a three-dimensional vector, the value of the first element indicates "whether or not you went to work", the value of the second element indicates "whether or not you ate out", and the value of the third element indicates "on the train". It may be specified to indicate "whether or not". In this case, the embedding vector may be a One-hot vector. A one-hot vector is a vector such as (1,0,0) in which one component is 1 and the remaining components are all 0. However, in reality, the embedding vector is not limited to the One-hot vector.

Further, the value of the element of the embedding vector may correspond to a plurality of actions of the user U. For example, it is stipulated that the value of the first element indicates "whether or not he went to work and ate out", and the value of the second element indicates "whether or not he did not go to work and ate out". You may.

The values of a plurality of elements included in a certain range of the embedding vector may correspond to the behavior of the user U. For example, if the value up to the third element (upper three digits) is "100", it may indicate "going to work", and if it is "101", it may indicate "going to work by train".

Further, the value of each element of the embedding vector may be an evaluation value. For example, if the value up to the third element (upper three digits) is "000", it may be specified to indicate "not moved", and if it is "100", it may be specified to indicate "moved 100 kilometers". Further, the value of any element of the embedding vector may be a value indicating a moving direction. For example, if the values from the 4th element to the 7th element are "0000", it means that the moving direction is "north", if it is "0001", it means that the moving direction is "north-northeast", and if it is "0010". "0011" indicates that the movement direction is "northeast", "0011" indicates that the movement direction is "east-northeast", "0100" indicates that the movement direction is "east", and "1000" indicates that the movement direction is "east". May indicate "south", and "1100" may indicate that the direction of movement is "west". Further, the value of each element of the embedding vector may be a score of a healthy life.

It is assumed that any setting can be applied to the value of the element of the embedding vector at the time of learning the model or creating the conversion rule. For example, it is assumed that an arbitrary setting can be appropriately made depending on the circumstances such as what kind of behavior information the server side wants to acquire from the user U side and use it for the service provided to the user U.

Here, the vector conversion model for converting the position information into the embedding vector, the predetermined conversion rule, and the like, the user U "is at home", "goes to work", and "goes shopping" for the sequence data of the position information. , "Move by train", etc. A concise model, conversion rule, etc. that aims to convert to a vector value indicating a specific action. For example, when the information providing device 100 wants to know whether or not the user U has taken a specific action, the information providing device 100 provides a vector conversion model, a predetermined conversion rule, or the like for converting the position information related to the action into an embedding vector. Provided to the terminal device 10 of U.

[2. Information processing system configuration example]
Next, the configuration of the information processing system 1 including the information providing device 100 according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 2, the information processing system 1 according to the embodiment includes a terminal device 10 and an information providing device 100. These various devices are connected so as to be communicable by wire or wirelessly via the network N. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

Further, the number of each device included in the information processing system 1 shown in FIG. 2 is not limited to that shown in the figure. For example, in FIG. 2, for simplification of the illustration, only one terminal device 10 is shown, but this is merely an example and is not limited, and may be two or more.

The terminal device 10 is an information processing device used by the user U. For example, the terminal device 10 is a smart device such as a smartphone or tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a car navigation system, a wearable device such as a smart watch or a head-mounted display (Wearable Device). , Smart glasses, etc.

Further, the terminal device 10 includes a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: 5th generation mobile communication system), Bluetooth (registered trademark), and wireless LAN (Local). It can be connected to the network N via short-range wireless communication such as Area Network) and communicate with the information providing device 100.

The information providing device 100 is, for example, a PC, a server device, a mainframe, a workstation, or the like. The information providing device 100 may be realized by cloud computing.

[3. Configuration example of terminal device]
Next, the configuration of the terminal device 10 will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the terminal device 10. As shown in FIG. 3, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40. Be prepared.

(Communication unit 11)
The communication unit 11 is connected to the network N (see FIG. 2) by wire or wirelessly, and transmits / receives information to / from the information providing device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, or the like.

(Display unit 12)
The display unit 12 is a display device that displays various information such as position information. For example, the display unit 12 is a liquid crystal display (LCD) or an organic EL display (Organic Electro-Luminescent Display). Further, the display unit 12 is a touch panel type display, but the display unit 12 is not limited thereto.

(Input unit 13)
The input unit 13 is an input device that receives various operations from the user U. The input unit 13 has, for example, a button for inputting characters, numbers, and the like. When the display unit 12 is a touch panel type display, a part of the display unit 12 functions as an input unit 13. The input unit 13 may be a microphone or the like that accepts voice input from the user U. The microphone may be wireless.

(Positioning unit 14)
The positioning unit 14 receives a signal (radio wave) transmitted from a GPS (Global Positioning System) satellite, and based on the received signal, position information (for example, latitude) indicating the current position of the terminal device 10 which is its own device. And longitude). That is, the positioning unit 14 positions the terminal device 10. GPS is only an example of GNSS (Global Navigation Satellite System).

In addition, the positioning unit 14 can position the position by various methods other than GPS. For example, the positioning unit 14 may position the position by using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction or the like as described below.

(Wi-Fi positioning)
For example, the positioning unit 14 positions the position of the terminal device 10 by using the Wi-Fi (registered trademark) communication function of the terminal device 10 or the communication network provided by each communication company. Specifically, the positioning unit 14 determines the position of the terminal device 10 by performing Wi-Fi communication or the like and measuring the distance to a nearby base station or access point.

(Beacon positioning)
Further, the positioning unit 14 may position the position by using the Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 positions the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

(Geomagnetic positioning)
Further, the positioning unit 14 positions the position of the terminal device 10 based on the geomagnetic pattern of the structure measured in advance and the geomagnetic sensor included in the terminal device 10.

(RFID positioning)
Further, for example, when the terminal device 10 has an RFID (Radio Frequency Identification) tag function equivalent to that of a non-contact IC card used at a station ticket gate or a store, or has a function of reading an RFID tag. In this case, the used position is recorded together with the information that the terminal device 10 has made a payment or the like. The positioning unit 14 may position the position of the terminal device 10 by acquiring such information. Further, the position may be determined by an optical sensor included in the terminal device 10, an infrared sensor, or the like.

The positioning unit 14 may position the position of the terminal device 10 by using one or a combination of the above-mentioned positioning means, if necessary.

(Sensor unit 20)
The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be a wired connection or a wireless connection. For example, the sensor unit 20 may be a detection device other than the terminal device 10, such as a wearable device. In the example shown in FIG. 3, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, a pressure sensor 23, a temperature sensor 24, a sound sensor 25, an optical sensor 26, a magnetic sensor 27, and an image sensor ( It is equipped with a camera) 28.

It should be noted that the above-mentioned sensors 21 to 28 are merely examples and are not limited thereto. That is, the sensor unit 20 may be configured to include a part of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or in place of the sensors 21 to 28. ..

The acceleration sensor 21 is, for example, a 3-axis acceleration sensor, and detects the physical movement of the terminal device 10 such as the moving direction, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects the physical movement of the terminal device 10 such as the inclination in the triaxial direction based on the angular velocity of the terminal device 10 and the like. The barometric pressure sensor 23 detects, for example, the barometric pressure around the terminal device 10.

Since the terminal device 10 includes the above-mentioned acceleration sensor 21, gyro sensor 22, pressure sensor 23, etc., technologies such as pedestrian autonomous navigation (PDR) using each of these sensors 21 to 23, etc. It becomes possible to determine the position of the terminal device 10 by using. This makes it possible to acquire indoor position information, which is difficult to acquire with a positioning system such as GPS.

For example, a pedometer using an acceleration sensor 21 can calculate the number of steps, the walking speed, and the walking distance. Further, the gyro sensor 22 can be used to know the traveling direction of the user U, the direction of the line of sight, and the inclination of the body. Further, from the atmospheric pressure detected by the atmospheric pressure sensor 23, it is possible to know the altitude at which the terminal device 10 of the user U exists and the number of floors of the floor.

The air temperature sensor 24 detects, for example, the air temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The optical sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image of the surroundings of the terminal device 10.

The pressure sensor 23, the temperature sensor 24, the sound sensor 25, the optical sensor 26, and the image sensor 28 described above detect the pressure, temperature, sound, and illuminance, respectively, and capture an image of the surroundings to obtain the terminal device 10. It is possible to detect the surrounding environment and situation of. Further, it is possible to improve the accuracy of the position information of the terminal device 10 from the surrounding environment and the situation of the terminal device 10.

(Control unit 30)
The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, an input / output port, and various circuits. Further, the control unit 30 may be configured by hardware such as an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmission unit 31, a reception unit 32, a processing unit 33, an acquisition unit 34, a conversion unit 35, and a coupling unit 36.

(Transmitting unit 31)
For example, the transmission unit 31 can transmit various information input by the user U using the input unit 13, various information processed by the processing unit 33, and the like to the information providing device 100 via the communication unit 11. ..

Further, the transmission unit 31 transmits the embedding vector converted from the position information by the conversion unit 35 to the information providing device 100 via the communication unit 11. At this time, the transmission unit 31 may transmit the position information at that time together with the embedding vector.

(Receiver 32)
The receiving unit 32 receives various information provided from the information providing device 100 via the communication unit 11. For example, the receiving unit 32 receives the vector conversion model provided by the information providing device 100, a predetermined conversion rule, and the like via the communication unit 11.

(Processing unit 33)
The processing unit 33 controls the entire terminal device 10 including the display unit 12 and the like. For example, the processing unit 33 can output various information transmitted by the transmitting unit 31 and various information received by the receiving unit 32 from the information providing device 100 to the display unit 12 for display.

(Acquisition unit 34)
The acquisition unit 34 acquires the position information of the terminal device 10 at a constant / periodic / predetermined timing. For example, the acquisition unit 34 acquires the position information of the terminal device 10 positioned by the positioning unit 14. Further, the acquisition unit 34 estimates and acquires the position information of the terminal device 10 based on various information detected by the sensors 21 to 28.

Further, the acquisition unit 34 stores (saves) the acquired position information in the storage unit 40. For example, the acquisition unit 34 stores the acquired position information as a position history (for example, location history, action history, etc.).

Further, the acquisition unit 34 acquires the vector conversion model, the predetermined conversion rule, and the like received from the information providing device 100 by the reception unit 32, and stores (saves) them in the storage unit 40.

(Conversion unit 35)
The conversion unit 35 converts (vectorizes) the position information into a plurality of different vectors by different methods. For example, the conversion unit 35 performs embedding and converts the position information acquired by the acquisition unit 34 into a plurality of different vectors by different methods.

When the conversion unit 35 has acquired the position information every minute, the conversion unit 35 arranges the position information every minute in chronological order and starts from the position information for 30 minutes (for example, 8:01 to 8:30) in each time zone. Generate an embedding vector. For example, the conversion unit 35 generates sequence data in which position information for each minute in 30 minutes is arranged in chronological order. Alternatively, the conversion unit 35 generates sequence data in which the position information for the first minute in 30 minutes and the difference for each minute with respect to the position information are arranged in chronological order. Then, the conversion unit 35 generates an embedding vector based on the sequence data.

For example, the conversion unit 35 uses the sequence data as a data set and converts the sequence data into a multidimensional real-valued vector by using a machine learning method using a neural network or the like. That is, the conversion unit 35 inputs sequence data to a vector conversion model generated through machine learning using, for example, RNN (Recurrent Neural Network) or LSTM (Long short-term memory), and acquires an embedding vector as an output. do. The conversion unit 35 may receive the vector conversion model from the information providing device 100.

The RNN and LSTM may be a neural network based on an attention mechanism. Further, the conversion unit 35 may use a similar natural language processing model.

Alternatively, the conversion unit 35 may convert the sequence data into a plurality of different embedding vectors according to a plurality of different conversion rules (on a rule basis).

However, the sequence data is only an example. Actually, for example, the raw data of the position information may be input to the model in order in chronological order as it is.

(Joint portion 36)
The joining portion 36 joins a plurality of embedding vectors. Here, the joining portion 36 joins the embedding results (vectors) of different versions (plurality of patterns). It should be noted that different versions of the embedding results are generated using different models, conversion rules, and the like.

Further, the coupling unit 36 may add (combine) additional information to the embedding vector. For example, the joining unit 36 may add information that defines a data type (Data Type) to the embedding vector. That is, the connecting unit 36 also functions as an imparting unit that imparts additional information to the embedding vector.

Further, the coupling portion 36 may add noise to the embedding vector. For example, the coupling portion 36 adds noise such as dummy data to the embedding vector. At this time, the coupling unit 36 may define the arrangement of dummy data in the elements of the embedding vector as a data type. That is, the coupling portion 36 also functions as an imparting portion that imparts noise to the embedding vector.

(Memory unit 40)
The storage unit 40 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or an optical disk. To. Various programs, various data, and the like are stored in the storage unit 40. Here, the storage unit 40 is stored by, for example, various information input by the user U using the input unit 13, various information detected by the sensors 21 to 28 mounted on or connected to the terminal device 10, and the positioning unit 14. The position information and the like of the positioned terminal device 10 are stored. Further, various information processed by the processing unit 33 and various information received by the receiving unit 32 are stored. Further, the storage unit 40 stores a vector conversion model for converting position information into an embedding vector, a predetermined conversion rule, and the like.

[4. Configuration example of information processing device]
Next, the configuration of the information providing device 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram showing a configuration example of the information providing device 100 according to the embodiment. As shown in FIG. 4, the information providing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Further, the communication unit 110 is connected to the network N (see FIG. 2) by wire or wirelessly.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 4, the storage unit 120 has a conversion information database 121 and a vector information database 122.

(Conversion information database 121)
The conversion information database 121 stores various information related to a vector conversion model for converting position information into an embedding vector, predetermined conversion rules (rules), and the like. FIG. 5 is a diagram showing an example of the conversion information database 121. In the example shown in FIG. 5, the conversion information database 121 has items such as "conversion ID", "conversion information", and "provider destination".

The "conversion ID" indicates identification information for identifying a vector conversion model for converting position information into an embedding vector, a predetermined conversion rule, and the like.

Further, the "conversion information" indicates a file in which a vector conversion model identified by the conversion ID, a predetermined conversion rule, and the like are converted into data. The vector conversion model, predetermined conversion rules, and the like may be generated based on, for example, an existing position history (or hypothetical position information) in which position information collected in the past is accumulated.

Further, the “providing destination” indicates a terminal device 10 that is a providing destination of a vector conversion model identified by a conversion ID, a predetermined conversion rule, and the like. For example, the terminal ID may be stored in the "provider destination". The number of terminal devices 10 to be provided may be plural. That is, a plurality of terminal IDs may be stored in the "provider destination". If it is not necessary to specify the terminal device 10 to be provided, the item of "provided destination" may be omitted.

For example, in the example shown in FIG. 5, the vector conversion model, the predetermined conversion rule, etc., which is the conversion information “conversion information # 1” identified by the conversion ID “T1”, is provided to the destination “provider # 1”. Show that you are.

The conversion information database 121 is not limited to the above, and various information may be stored depending on the purpose. For example, the conversion information database 121 may store detailed information such as a vector conversion model identified by the conversion ID and a predetermined conversion rule.

(Vector information database 122)
The vector information database 122 stores various information regarding the embedding vector received from the terminal device 10. FIG. 6 is a diagram showing an example of the vector information database 122. In the example shown in FIG. 6, the vector information database 122 has items such as “terminal ID”, “embedding vector”, and “determination result”.

The "terminal ID" indicates identification information for identifying the terminal device 10 that is the source of the embedding vector. The "terminal ID" may be an arbitrary number assigned on the server side, or may be contact information (telephone number, e-mail address, etc.) of the terminal device 10. If it is not necessary to identify the terminal device 10, the item of "terminal ID" may be omitted.

Further, the "embedding vector" indicates an embedding vector received from the terminal device 10. In addition, a plurality of "embedding vectors" may exist for one terminal device 10. For example, when the terminal device 10 generates an embedding vector every 30 minutes, the embedding vector every 30 minutes is stored.

Further, the "determination result" indicates the behavior (location, behavior, movement, etc.) of the user U determined based on the embedding vector.

For example, in the example shown in FIG. 6, in the embedding vector "vector # 11" received from the terminal device 10 identified by the terminal ID "terminal # 1", the action of the user U of the terminal device 10 is the determination result "determination result #". 11 ”indicates that the action is indicated by.

The vector information database 122 is not limited to the above, and various information may be stored depending on the purpose. For example, the vector information database 122 may store identification information (vector ID or the like) for identifying individual embedding vectors in units of received data. Further, the vector information database 122 may store information indicating the date and time when each embedding vector is received. The vector information database 122 may also store information about rules for interpreting the embedding vector. Further, the vector information database 122 may store conversion information such as a vector conversion model and a predetermined conversion rule.

(Control unit 130)
Returning to FIG. 4, the explanation will be continued. The control unit 130 is a controller, and is an information providing device 100 by means of, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like. It is realized by executing various programs (corresponding to an example of an information processing program) stored in the internal storage device of the ASIC with a storage area such as a RAM as a work area. In the example shown in FIG. 4, the control unit 130 includes a collection unit 131, a determination unit 132, a generation unit 133, and a providing unit 134.

(Collecting unit 131)
The collecting unit 131 collects the embedding vector from each terminal device 10. That is, the collecting unit 131 receives the combined embedding vector from the terminal device 10 via the communication unit 110.

(Judgment unit 132)
The determination unit 132 analyzes the collected embedding vector and determines the behavior (location, behavior, movement, etc.) of the user U. For example, the determination unit 132 decomposes the combined embedding vector and determines the behavior of the user U based on the vector value of the decomposed embedding vector.

Further, the determination unit 132 arranges the embedding vector in a specific space, and determines the similarity or change of the behavior of the user U itself, and / or the similarity or difference of the behavior between the user U and another user. For example, the determination unit 132 decomposes the combined embedding vector and arranges the embedding vector generated by the same method in the same specific space. Actually, regardless of the difference in the method, all the decomposed embedding vectors may be arranged in the same specific space.

When noise is added to the embedding vector, the determination unit 132 may remove the noise from the embedding vector or may execute the processing of the embedding vector while including the noise.

(Generation unit 133)
The generation unit 133 has a vector conversion model for converting the position information into an embedding vector and a predetermined conversion rule (rule) based on the existing position history (or assumed position information) that has accumulated the position information collected in the past. ) Etc. are generated.

For example, the generation unit 133 generates a vector conversion model that outputs an embedding vector when the position information is input by letting the model learn the set of the position information and the embedding vector as correct answer data.

Here, the generation unit 133 generates a vector conversion model by machine learning using an RNN (Recurrent Neural Network), an LSTM (Long short-term memory), or the like. As the model, any type and any type of model can be adopted. For example, the generation unit 133 may adopt an SVM (Support Vector Machine) or a DNN (Deep Neural Network) as a model. Here, the DNN may be a CNN (Convolutional Neural Network) or an RNN. Further, the model may be a model realized by combining a plurality of models, for example, a model in which a CNN and an RNN are combined.

The RNN and LSTM may be a neural network based on an attention mechanism. Further, the generation unit 133 may use a similar natural language processing model.

The learning is, for example, deep learning using DNN. Data mining and other machine learning algorithms may also be used. The generation unit 133 learns the model by the various learning methods described above.

(Providing section 134)
The providing unit 134 provides the terminal device 10 of the user U with the generated vector conversion model, predetermined conversion rules, and the like. That is, the providing unit 134 transmits the generated vector conversion model, a predetermined conversion rule, and the like to the terminal device 10 of the user U via the communication unit 110.

[5. Processing procedure]
Next, the processing procedure by the terminal device 10 and the information providing device 100 according to the embodiment will be described with reference to FIG. 7. FIG. 7 is a sequence diagram showing a processing procedure according to the embodiment. The processing procedure shown below is repeatedly executed by the control unit 30 of the terminal device 10 or the control unit 130 of the information providing device 100.

As shown in FIG. 7, the generation unit 133 of the information providing device 100 converts the position information into an embedding vector based on the existing position history in which the position information of the terminal device 10 collected in the past is accumulated. A conversion model, a predetermined conversion rule, and the like are generated (step S101). For example, the generation unit 133 generates a vector conversion model that outputs an embedding vector when the position information is input by letting the model learn the set of the position information and the embedding vector as correct answer data.

Then, the providing unit 134 of the information providing device 100 provides the generated vector conversion model, a predetermined conversion rule, and the like to the terminal device 10 of the user U (step S102). That is, the providing unit 134 transmits the generated vector conversion model, a predetermined conversion rule, and the like to the terminal device 10 of the user U via the communication unit 110.

Then, the acquisition unit 34 of the terminal device 10 acquires the vector conversion model, the predetermined conversion rule, and the like provided by the information providing device 100 (step S103).

Then, the acquisition unit 34 of the terminal device 10 acquires the position information of the terminal device 10 at a constant / periodic / predetermined timing (step S104). For example, the acquisition unit 34 acquires the position information of the terminal device 10 positioned by the positioning unit 14. Further, the acquisition unit 34 estimates and acquires the indoor position information of the terminal device 10 based on various information detected by the sensors 21 to 28.

Then, the acquisition unit 34 accumulates the acquired position information as a position history (for example, location history, action history, etc.) (step S105). For example, the acquisition unit 34 stores (saves) the acquired position information in the storage unit 40.

Then, the conversion unit 35 generates sequence data in which the position information is arranged in time series (step S106).

Then, the conversion unit 35 generates a plurality of different embedding vectors by different methods based on the sequence data (step S107).

For example, the conversion unit 35 inputs sequence data to each of a plurality of different vector conversion models generated through machine learning using, for example, RNN (Recurrent Neural Network) or LSTM (Long short-term memory), and each of them. Get multiple different embedding vectors as the output of. The RNN and LSTM may be a neural network based on an attention mechanism. Further, the conversion unit 35 may use a similar natural language processing model.

Alternatively, the conversion unit 35 may analyze the sequence data and convert the sequence data into a plurality of different embedding vectors according to a plurality of different conversion rules (on a rule basis).

However, the sequence data is only an example. Actually, for example, the raw data of the position information may be input to the model in order in chronological order as it is.

Then, the joining portion 36 joins a plurality of embedding vectors (step S108).

Here, the joining portion 36 joins the embedding results (vectors) of different versions (plurality of patterns). It should be noted that different versions of the embedding results are generated using different models, conversion rules, and the like. Further, the coupling unit 36 may add (combine) additional information to the embedding vector. For example, the joining unit 36 may add information that defines a data type (Data Type) to the embedding vector. Further, the coupling portion 36 may add (combine) noise to the embedding vector.

Then, the transmission unit 31 of the terminal device 10 transmits the embedding vector coupled by the coupling unit 36 to the information providing device 100 via the communication unit 11 (step S109).

Then, the collecting unit 131 of the information providing device 100 collects the embedding vector from each terminal device 10 (step S110). That is, the collecting unit 131 receives the combined embedding vector from the terminal device 10 via the communication unit 110.

Then, the determination unit 132 of the information providing device 100 analyzes the collected embedding vector and determines the behavior (location, behavior, movement, etc.) of the user U (step S111).

For example, the determination unit 132 decomposes the combined embedding vector and determines the behavior of the user U based on the vector value of the decomposed embedding vector.

At this time, the determination unit 132 decomposes the combined embedding vector, arranges the embedding vector generated by the same method in the same specific space, and has similarities and changes in the behavior of the user U itself, and / or with the user U. You may determine the similarity or difference in behavior with other users.

When noise is added to the embedding vector, the determination unit 132 of the information providing device 100 may remove the noise from the embedding vector, or may execute the analysis of the embedding vector with the noise included. ..

Further, the generation unit 133 of the information providing device 100 may update the vector conversion model, a predetermined conversion rule, or the like based on the embedding vector received from the terminal device 10.

[6. Modification example]
The terminal device 10 and the information providing device 100 described above may be implemented in various different forms other than the above-described embodiment. Therefore, a modified example of the embodiment will be described below.

In the above embodiment, the information providing device 100 uses the vector value of the combined embedding vector, and the user U "goes to the mountain", "goes to the sea", "goes to a rural area", and "goes to a foreign country". You may judge the behavior such as. Further, the user U may determine an action such as "going on a trip" or "on a business trip". Further, the user U may determine an action such as "day trip" or "stayed".

Further, in the above embodiment, when the terminal device 10 does not want to notify the information providing device 100 (server side) of detailed information regarding the behavior of the user U, when accumulating the position information or prior to embedding, the terminal device 10 is positioned. The information may be rounded (rounded), and the position information may be rounded and converted into coarse position information. For example, when the position information is the latitude / longitude information of GPS (Global Positioning System) coordinates, the terminal device 10 rounds up the value below the decimal point (minutes or less in the degree, minute, and second system) at the decimal angle of the latitude and longitude. Alternatively, it is rounded down (rounding is also possible) and converted into rough value position information. As a result, it is possible to limit the content of the information transmitted from the terminal device 10 to the information providing device 100 (server side) regarding the information regarding the behavior of the user U. In addition, the amount of position information as learning data for machine learning can be reduced, and the processing load can be reduced.

Further, in the above embodiment, the value of each element of the embedding vector is represented by "0" and "1" for convenience, but in reality, a numerical value of "2" or more, a numerical value of a plurality of digits, or a numerical value of a plurality of digits is used. , It may be a numerical value having an integer part and a decimal part. That is, it may be a real number. As a result, a plurality of information and detailed information can be expressed in each element of the multidimensional embedding vector.

Further, in the above embodiment, the terminal device 10 stores the acquired position information as a position history (for example, location history, action history, etc.), but when the storage of the position history is restricted or prohibited. , May be accumulated in a vectorized state. For example, the terminal device 10 sets the location information as a vector value indicating "at home", "at work", "at the store", "outdoor", etc., based on the location indicated by the individual location information. It may be converted to and accumulated. Then, when the same vector values are continuous along the time series, they may be collectively used as an embedding vector.

Further, in the above embodiment, the terminal device 10 or the information providing device 100 individually sets a vector conversion model for converting position information into an embedding vector, a predetermined conversion rule, and the like according to the nature and situation of each user U. May be adjusted to. For example, a vector conversion model, a predetermined conversion rule, or the like may be customized.

Further, in the above embodiment, a vector conversion model for converting position information into an embedding vector may be generated by using federated learning or the like. For example, in federated learning, each terminal device 10 performs machine learning on a vector conversion model using position information as training data, and provides information providing device 100 with difference data between the original model and the trained model. Send to. The information providing device 100 receives the difference data from each terminal device 10, integrates and learns them, and generates a common model. Then, the information providing device 100 provides each terminal device 10 with the parameters and the like of the generated common model. In this way, by using federated learning, it is possible to perform learning without providing the location information of the terminal, which is learning data, to the server side, so it is possible to consider the personal information and privacy of the user. can.

[7. effect〕
As described above, the information processing device (terminal device 10) according to the present application has a plurality of different acquisition units 34 for acquiring position information and a conversion unit 35 for converting position information into a plurality of different vectors by different methods. It includes a connecting unit 36 for connecting vectors and a transmitting unit 31 for transmitting the combined vectors. For example, the conversion unit 35 converts the position information into a plurality of different vectors by using a plurality of different vector conversion models obtained by machine learning. Alternatively, the conversion unit 35 converts the position information into a plurality of different vectors according to a plurality of different conversion rules. Further, the joining unit 36 may add (join) additional information (for example, a data type, etc.) to the joined vector. Further, the coupling portion 36 may add (combine) noise to the coupled vector.

As a result, the information processing apparatus can generate embedding information that can be used for multiple purposes by connecting a plurality of forms of embedding information in advance.

[8. Hardware configuration]
Further, the terminal device 10 and the information providing device 100 according to the above-described embodiment are realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the information providing device 100 will be described as an example. FIG. 8 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to the output device 1010 and the input device 1020, and the arithmetic device 1030, the primary storage device 1040, the secondary storage device 1050, the output I / F (Interface) 1060, the input I / F 1070, and the network I / F 1080 are bused. It has a form connected by 1090.

The arithmetic unit 1030 operates based on a program stored in the primary storage device 1040 or the secondary storage device 1050, a program read from the input device 1020, or the like, and executes various processes. The arithmetic unit 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that temporarily stores data used by the arithmetic unit 1030 for various calculations. Further, the secondary storage device 1050 is a storage device in which data used by the calculation device 1030 for various calculations and various databases are registered, and is a ROM (Read Only Memory), an HDD (Hard Disk Drive), and an SSD (Solid). State Drive), flash memory, etc. The secondary storage device 1050 may be an internal storage or an external storage. Further, the secondary storage device 1050 may be a removable storage medium such as a USB memory or an SD (Secure Digital) memory card. Further, the secondary storage device 1050 may be a cloud storage (online storage), NAS (Network Attached Storage), a file server, or the like.

The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various information such as a display, a projector, and a printer. For example, USB (Universal Serial Bus) or DVI. (Digital Visual Interface), HDMI (Registered Trademark) (High Definition Multimedia Interface), etc. are realized by standard connectors. Further, the input I / F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, a keyboard, a keypad, a button, a scanner, and the like, and is realized by, for example, USB.

Further, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

Further, the output device 1010 and the input device 1020 may be integrated like a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as input / output I / F.

The input device 1020 is, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like.

The network I / F 1080 receives data from another device via the network N and sends it to the arithmetic unit 1030, and also transmits the data generated by the arithmetic unit 1030 to the other device via the network N.

The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040, and executes the loaded program.

For example, when the computer 1000 functions as the information providing device 100, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the primary storage device 1040. Further, the arithmetic unit 1030 of the computer 1000 may load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. Further, the arithmetic unit 1030 of the computer 1000 may cooperate with other devices via the network I / F 1080 to call the functions and data of the program from other programs of the other devices and use them.

[9. others〕
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. Further, the above-mentioned components include those that can be easily assumed by those skilled in the art, those that are substantially the same, that is, those in a so-called equal range. Furthermore, the components described above can be combined as appropriate. Further, various omissions, replacements or changes of the components can be made without departing from the gist of the above-described embodiment.

Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown in the figure.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of them may be functionally or physically distributed / physically in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, the above-mentioned information providing device 100 may be realized by a plurality of server computers, or may be realized by calling an external platform or the like by API (Application Programming Interface), network computing, or the like depending on the function. Can be changed flexibly.

Further, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not contradict each other.

Further, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 10 Terminal equipment 14 Positioning unit 30 Control unit 31 Transmission unit 32 Reception unit 33 Processing unit 34 Acquisition unit 35 Conversion unit 36 Coupling unit 100 Information provider 110 Communication unit 120 Storage unit 121 Conversion information database 122 Vector information database 130 Control unit 131 Collection unit 132 Judgment unit 133 Generation unit 134 Providing unit

Claims (12)

The acquisition unit that acquires location information and
A conversion unit that converts the position information into a plurality of different vectors by different methods,
A bond that joins a plurality of different vectors,
A transmitter that transmits the combined vector,
An information processing device characterized by being equipped with. The joint portion adds additional information to the combined vector.
The information processing apparatus according to claim 1. The coupling portion adds noise to the coupled vector.
The information processing apparatus according to claim 1 or 2. In each method, the conversion unit collectively converts a plurality of the position information into a single vector.
The information processing apparatus according to any one of claims 1 to 3. In each method, the conversion unit converts sequence data in which a plurality of the position information are arranged in time series into the multidimensional vector.
The information processing apparatus according to any one of claims 1 to 4. The conversion unit converts the position information into a plurality of different vectors by using a plurality of different vector conversion models obtained by machine learning.
The information processing apparatus according to any one of claims 1 to 5. The conversion unit converts the position information into a plurality of different vectors according to a plurality of different conversion rules.
The information processing apparatus according to any one of claims 1 to 6. The conversion unit converts some of the above-mentioned position information into a plurality of different vectors by different methods among the plurality of the above-mentioned position information.
The connecting portion joins a plurality of different vectors converted from a part of the position information.
The transmitting unit transmits the combined vector and the remaining position information.
The information processing apparatus according to any one of claims 1 to 7. The vector indicates the user's behavior.
The information processing apparatus according to any one of claims 1 to 8. A collection unit that collects a combination vector that combines multiple different vectors obtained by converting the same position information by different methods from a terminal device.
A determination unit that determines the user's behavior based on the connection vector,
An information processing device characterized by being equipped with. It is an information processing method executed by an information processing device.
The acquisition process to acquire location information and
A conversion process that converts the position information into a plurality of different vectors by different methods,
A joining process that joins a plurality of different vectors,
The transmission step of transmitting the combined vector and
An information processing method characterized by including. The acquisition procedure to acquire location information and
A conversion procedure for converting the position information into a plurality of different vectors by different methods, and
A joining procedure for joining a plurality of different vectors and
The transmission procedure for transmitting the combined vector and the transmission procedure.
Information processing program to make a computer execute.

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