JP7615086B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

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

A technology has been disclosed that transmits location information from a communication device with low power consumption.

JP 2020-042681 A

However, in the above conventional technology, the manually-operated vehicle merely transmits communication device information, including current location information, to the communication partner in sequence at predetermined transmission intervals using low power wide area wireless communication (LPWA). Normally, the communication device does not consume much power just by acquiring location information, but consumes a large amount of power when transmitting location information to the outside (especially when moving at high speed). Even if the amount of power consumed per transmission is small, when location information is repeatedly transmitted, the amount of power consumed increases depending on the number of transmissions. A manually-operated vehicle such as the one exemplified in the above conventional technology may not have a shortage of power capacity, but it is difficult to say that the power capacity is sufficient for a terminal device (handheld device) such as a smartphone.

In recent years, from the standpoint of protecting personal information, there has been a trend for the specifications of terminal devices such as smartphones to place stricter restrictions on the external transmission of location information from personal terminal devices, making it extremely difficult for businesses to obtain location information from personal terminal devices one by one. For example, the option to "always allow" the use of location information from personal terminal devices is disappearing. There is also the problem that location information cannot be transmitted when an application is running in the background due to control from the OS (Operating System).

The inventors are therefore considering a method of embedding (vectorizing) location information and transmitting it. However, because the embedding method and parameters change depending on how the embedded information (embedding information) is to be utilized, embedding is not possible if the goal (purpose of use) is not decided. Furthermore, there is an issue that embedding information collected for a specific purpose cannot be used for other purposes.

The present application has been made in consideration of the above, and aims to generate embedding information that can be used for multiple purposes by combining multiple formats of embedding information in advance.

The information processing device according to the present application includes an acquisition unit that acquires location information, a conversion unit that converts raw data of the location information without rounding into a plurality of vectorized data using a plurality of methods, a combination unit that combines the plurality of vectorized data, and a transmission unit that transmits the combined vectorized data.

According to one aspect of the embodiment, multiple types of embedding information can be combined in advance to generate embedding information that can be used for multiple purposes.

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

Below, the information processing device, information processing method, and information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing device, information processing method, and information processing program according to the present application are not limited to these embodiments. In addition, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[1. Overview of information processing method]
First, an overview of an information processing method performed by an information processing device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. Note that Fig. 1 describes an example in which a terminal device embeds and transmits location information.

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) in a wired or wireless manner so as to be able to communicate with each other.

The terminal device 10 is a smart device such as a smartphone or tablet used by a user U (user), and is a mobile terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). The terminal device 10 has a screen such as a liquid crystal display with a touch panel function, and accepts various operations on displayed data such as content, such as tapping, sliding, scrolling, etc., performed by the user with a finger or a stylus. An operation performed on an area of the screen where content is displayed may be considered to be an operation on the content. 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 location information and converts (vectorizes) the acquired location information into a vector. That is, the terminal device 10 performs so-called embedding and generates an embedding vector based on the location information. Then, the terminal device 10 transmits the embedding vector based on the location information to the information providing device 100.

The information providing device 100 is an information processing device that receives an embedding vector based on location 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 location information, and is realized by a server device, a cloud system, etc.

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

Conventionally, the terminal device 10 transmits location information to the information providing device 100. However, in order to respect the privacy of the user U and to reduce the amount of data communication, the terminal device 10 performs decimal processing (rounding) on the location information, rounding the location information and converting it to coarse location information before transmitting it. For example, when the location information is latitude and longitude information in GPS (Global Positioning System) coordinates, the values after the first decimal place in the decimal angle of the latitude and longitude are rounded up or down (rounding is also possible) to convert it into location information with a rough value.

In this case, when the information providing device 100 tries to estimate the behavior of the user U based on the location information, the rounded location information is a rough value, so although it can identify a large area such as a region or a large facility, it may not be able to identify a small area such as an adjacent small building or room (and/or the difference between indoors and outdoors). Therefore, it is possible to estimate the general behavior of the user U, but it may not be possible to estimate the detailed behavior of the user U. For example, if small buildings A and B (e.g., a convenience store and a fast food restaurant) are built adjacent to each other within the range of the location indicated by the rounded location information of the user U, it may not be possible to identify from the location information whether the user U is in building A or building B, or whether he is inside or outside the building (or site). What the information providing device 100 (server side) really wants to know is not the location information of the user U itself, but the behavior of the user U indicated by the location information (i.e., the context of the user U).

This time, when the terminal device 10 converts its own location information into a vector (vectorization), it converts the raw data of the location information before performing fractional processing (rounding processing) into a vector. Therefore, the terminal device 10 can generate an embedding vector after identifying small places such as adjacent small buildings and rooms (and/or the difference between indoors and outdoors). 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 obtain more specific and detailed information about the behavior of the user U from the terminal device 10 than by receiving conventional rounded location information. In addition, since the information providing device 100 (server side) defines the vector value of the embedding vector and its meaning, it is safe because a third party cannot understand what the vector value means even if they only look at the vector value of the embedding vector.

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

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

Then, the terminal device 10 stores the acquired location information (step S2).

Then, the terminal device 10 embeds the location information using multiple different methods, converts (vectorizes) the multiple location information into one vector for each method, and generates multiple embedding vectors (embedding vectors) generated using different methods based on the same multiple location information (step S3).

Then, the terminal device 10 combines the multiple embedding vectors generated using different methods based on the same multiple pieces of location 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 of the user U (location, behavior, movement, etc.) based on the embedding vector received from the terminal device 10 (step S7).

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

For example, in step S1, the terminal device 10 owned by each of the multiple users U located in each region acquires its own location information using a positioning technology such as GNSS (Global Navigation Satellite System) or indoor positioning technology. At this time, the terminal device 10 acquires time information indicating the time when the location information was acquired together with its own location information.

Next, in step S2, the terminal device 10 accumulates the acquired location information as location history (e.g., location history, behavior history, etc.).

Next, in step S3, the terminal device 10 generates sequence data (time-series data) by arranging the accumulated location information in a chronological order, and when performing embedding for each sequence data, converts one piece of sequence data into multiple different vectors using multiple different methods to generate an embedding vector. That is, the terminal device 10 converts the location information into multiple different vectors using different methods to generate multiple different embedding vectors based on the same location information. At this time, the terminal device 10 converts multiple pieces of location information into a single multidimensional vector collectively in each method. For example, if the terminal device 10 acquires location information every minute, it arranges the location information for each minute in a chronological order and generates a single embedding vector from the location information for 30 minutes in each time zone (for example, 8:01 to 8:30).

For example, the terminal device 10 generates sequence data (time series data) in which the location information for each minute in a 30-minute period is arranged in a time series. Alternatively, the terminal device 10 generates sequence data in which the location information for the first minute in a 30-minute period and the minute-by-minute differences from that location information are arranged in a time series. 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 dataset and converts the sequence data into a multidimensional real-valued vector using a machine learning technique using a neural network. That is, the terminal device 10 inputs the sequence data into each of a number of different vector conversion models generated through machine learning using, for example, a recurrent neural network (RNN) or a long short-term memory (LSTM), and obtains a number of different embedding vectors as the respective outputs. The terminal device 10 may be provided with a number of different vector conversion models from the information providing device 100.

Note that the RNN and LSTM may be neural networks based on the mechanism of attention. Attention can handle data such as sentences in which the order of precedence and followance is important. The terminal device 10 may also use a similar natural language processing model. By using such a model to generate embedding vectors from sequence data, it is possible to generate embedding vectors that place more emphasis on the order of information.

Alternatively, the terminal device 10 may convert the sequence data into multiple different embedding vectors according to multiple different conversion rules (rules) (rule-based).

However, sequence data is merely one example. In reality, for example, raw location information data may be input directly into the model in chronological order.

Here, when the terminal device 10 embeds based on the location information data in accordance with the above-mentioned vector conversion model or a specific conversion rule, the output value becomes a vector expression. When these output vectors are arranged in chronological order, the user's movement route is shown. For example, although the specific latitude and longitude are unknown, it is possible to show that the user moved from point A to point B in the vector space. In other words, the result of embedding by the terminal device 10 using the location information (the output value) is the embedding vector. This embedding vector is a value that indicates the user's behavior (a value that indicates the context).

The timing at which the terminal device 10 performs the embedding is arbitrary. For example, the accumulated location information may be embedded when the transmission of location information from the terminal device 10 to the information providing device 100 is permitted. Alternatively, the accumulation of location information may start when an application (app) is launched, and the accumulated location information may be embedded when the use of the app is terminated. Alternatively, the accumulated location information may be embedded when the accumulated number of pieces of location information reaches a predetermined number (e.g., 100 pieces). Alternatively, the accumulated location information may be embedded when the user moves from a place where the user had been staying (when the location information changes significantly).

Next, in step S4, the terminal device 10 combines the embedding results (vectors) of different versions (multiple patterns). Note that the embedding results of different versions are generated using different models, conversion rules, etc.

For example, among the multidimensional embedding vectors of 20 or more dimensions, the embedding results of 100m mesh are placed in the 1st to 10th elements, and the embedding results of 500m mesh are placed in the 11th to 20th elements. In other words, embedding results of multiple different patterns are combined. This makes it possible to generate vectors indicating the behavior of user U in 100m units and vectors indicating the behavior of user U in 500m units. When embedding is performed, movements in 100m units can be recognized with only the 100m mesh vector, but large movements in 500m units are difficult to recognize. By adding the 500m mesh vector, large movements in 500m units can also be recognized. Note that 100m mesh and 500m mesh are merely examples. For example, embedding results of 10m mesh, 1km mesh, etc. may be placed. Also, embedding results of regional meshes may be placed.

Alternatively, among the multidimensional embedding vectors of 20 or more dimensions, a vector indicating the destination of the user U may be placed in the first to tenth elements, and a vector indicating the means of transportation of the user U may be placed in the eleventh to twentieth elements. For example, a vector indicating "heading home or not", a vector indicating "heading to work or not", a vector indicating "heading to store 1 or not" may be placed in the first element, a vector indicating "traveling on foot or not", a vector indicating "traveling by car or not", a vector indicating "traveling by train or not" may be placed in the eleventh element, a vector indicating "traveling by train or not" may be placed in the twelfth element, a vector indicating "traveling by train or not" may be placed in the thirteenth element. The means of transportation can be determined from the travel speed and travel route based on the position information. Furthermore, vectors indicating the time period, day of the week, holidays, etc. may be placed in the 21st element and after. In this way, by combining different embedding results, the elements of the multidimensional embedding vector may be divided into a predetermined number of elements according to the type of vector, and vectors indicating highly versatile information that can be used for multiple purposes may be placed.

The terminal device 10 may also 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 each combined embedding result.

The terminal device 10 may also add noise to the embedding vector. For example, the terminal device 10 places a noise vector such as dummy data in a predetermined element (which may be multiple elements) of the multidimensional embedding vector in order to prevent personal information such as location information from being identified from the embedding vector. At this time, the terminal device 10 may define the element in the multidimensional embedding vector in which the noise vector is placed as a data type. Note that the server side (information providing device 100) may remove noise from the embedding vector, or may execute the embedding process while including noise.

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

The timing at which the terminal device 10 transmits the combined embedding vector to the information providing device 100 is arbitrary. For example, it may be a timing at which transmission of location information from the terminal device 10 to the information providing device 100 is permitted. It may also be when an application (app) is started or in use, at a specified time, or at a specified cycle (every two hours, etc.). The information providing device 100 may also request the terminal device 10 to transmit the embedding vector.

Next, in step S6, the information providing device 100 receives the combined embedding vectors from the terminal devices 10 owned by each of the multiple users U located in each region via the network N.

Next, in step S7, the information providing device 100 decomposes the combined embedding vectors and places embedding vectors generated by the same method in the same specific space. By placing the embedding vectors in the specific space, the information providing device 100 can clearly grasp the similarities and changes in the user U's own behavior, and/or the similarities and differences between the behavior of the user U and other users. For example, the terminal device 10 performs embedding on location information (latitude, longitude, etc.), and therefore generates embedding vectors of the same value when the users are in the same position. The information providing device 100 places embedding vectors of the same value in the same position in the specific space.

In this case, the information providing device 100 is not mapped (associated) with a location on a map (latitude, longitude, etc.), and therefore cannot identify a specific location, but it is possible to confirm specific actions of user U, such as that user U moved from point A to point B during this time period, then moved to point C, and then returned to point A again. Furthermore, the information providing device 100 can confirm the fact of user U's movement without using location information.

As described above, in this embodiment, when transmitting the user's location information, the terminal device does not transmit the acquired location information to the receiving side (server side) as is, but embeds the location information before transmitting it to the receiving side. Since embedded information is difficult to restore on the receiving side, it becomes difficult to identify the exact location information, and the problem of personal information is solved. In other words, not only is the amount of information reduced, but it is also secure because the server side cannot restore the raw data detected by the terminal side (GPS location information itself). In addition, by embedding and transmitting the acquired location information collectively, the problem of battery consumption is also solved.

Furthermore, by generating embedding information by combining multiple different embedding results generated by different methods based on the same location information, the embedding information can be used for multiple purposes. For example, if the content (use) to be estimated on the server side has not been decided, data can be embedded in multiple formats such as NPP (Next Place Prediction), natural language processing of POI (Point of Interest) (Bag-of-words, etc.), or data type.

1-4. Example 2
Next, a 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 location 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 location information to the information providing device 100.

For example, when there were no restrictions on the transmission of location information (when location information could be transmitted freely), the terminal side would transmit an average of about 17 pieces of location information to the server side per day if "always permitted", but recently restrictions have become stricter and the terminal side only transmits about 3 pieces of location information to the server side. Therefore, the terminal device 10 may generate and combine multiple embedding vectors obtained by vectorizing 14 of the 17 pieces of location information using multiple different methods, and transmit these to the server side together with the remaining three pieces of location information. In other words, the terminal device 10 may transmit embedding information obtained by vectorizing and combining some of the location information using multiple different methods, and the remaining location information itself. The remaining location information is the number of pieces of location information that the terminal side can transmit.

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

This may also be implemented in combination with the above-mentioned Example 1. For example, in step S7, when the information providing device 100 places the embedding vector in a specific space, it can map the position in the specific space to a position on the map (latitude, longitude, etc.) by using three points of position information to map the specific space and the map. This allows the information providing device 100 to estimate the location to a certain extent, although not completely.

In addition, since the terminal device 10 performs embedding on the location information (latitude, longitude, etc.), an embedding vector of the same value is generated if the user is in the same location. Therefore, the information providing device 100 can also estimate that the user U is staying in the same place based on embedding vectors of the same value. As a result, even without identifying a specific location, the information providing device 100 can estimate that the user U is "at home" if the user U routinely stays in the same place at night for a long time, and that the user U is "at work" if the user U routinely stays in a different place during the day for a long time.

1-5. Example 3
Next, a 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 may generate an embedding vector that indicates the behavior of user U (user context) represented by two or more pieces of location information as vector values based on sequence data in which the location information is arranged in chronological order.

For example, the terminal device 10 inputs sequence data into a vector conversion model generated through machine learning using, for example, an RNN or LSTM, and obtains an embedding vector as the output. At this time, even if the data is the same, the RNN or LSTM outputs differently depending on the order in which it is input. For example, if the user moves in the order of "Position 1" → "Position 2" → "Position 3", it outputs "Went to work" (outbound), and if the user moves in the order of "Position 3" → "Position 2" → "Position 1", it outputs "Went home" (return). 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 in position information.

In this way, when an embedding vector is generated from sequence data using LSTM, it is possible to generate an embedding vector indicating whether or not the user performed a specified action from the action of user U indicated by the sequence data (user U's context). For example, if the sequence data of location information indicates that user U moved from an area where his/her home is located to an area where his/her workplace is located, the terminal device 10 can generate an embedding vector in which the element corresponding to the action of "going to work" takes a specified value.

Note that RNN and LSTM may be neural networks based on the attention mechanism. The terminal device 10 may also 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, if the first acquired location information #1 is within a predetermined range from the user U's home, the next acquired location information #2 is within a predetermined range from the station, and the last acquired location information #3 is within a predetermined range from the workplace, the terminal device 10 may generate an embedding vector in which an element corresponding to the action of "going to work" takes a predetermined value.

However, sequence data is merely one example. In reality, for example, raw location information data may be input directly into the model in chronological order.

In addition, if there is no significant change in the location information acquired over a 30-minute period, for example, the terminal device 10 may calculate the average or median of the location information acquired over that 30-minute period, use this as the location information for that 30-minute period, and generate an embedding vector from this location information.

Then, in step S7, the information providing device 100 decomposes the combined embedding vector, and determines the behavior (user context) of user U, such as "at home," "went to work," "went shopping," or "traveled by train," based on the vector values of the decomposed embedding vector. At this time, the information providing device 100 may determine the behavior for each time period based on the values indicated by multiple elements of the multidimensional embedding vector. For example, when the embedding vector includes a value indicating "went out in the morning" and a value indicating "went to work," the information providing device 100 may determine that the user "went to work in the morning."

Here, the values of the elements of the embedding vector correspond to the actions of user U. For example, in the case of a three-dimensional vector, the value of the first element may indicate "whether or not the user went to work", the value of the second element may indicate "whether or not the user ate out", and the value of the third element may indicate "whether or not the user took the train". 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 practice, the embedding vector is not limited to a one-hot vector.

The values of the elements of the embedding vector may correspond to multiple actions of user U. For example, the value of the first element may indicate "whether or not the user went to work and ate out," and the value of the second element may indicate "whether or not the user did not go to work and ate out."

The values of multiple elements included in a certain range of the embedding vector may correspond to the actions of user U. For example, the values of the first three elements (top three digits) may be specified so that "100" indicates "went to work" and "101" indicates "went to work by train."

The value of each element of the embedding vector may be an evaluation value. For example, the values of the first three elements (top three digits) may be specified as "000" to indicate "not moving", and "100" to indicate "moved 100 kilometers". The value of any element of the embedding vector may be a value indicating the moving direction. For example, the values of the fourth to seventh elements may be specified as "0000" to indicate the moving direction is "north", "0001" to indicate the moving direction is "north-northeast", "0010" to indicate the moving direction is "northeast", "0011" to indicate the moving direction is "east-northeast", "0100" to indicate the moving direction is "east", "1000" to indicate the moving direction is "south", and "1100" to indicate the moving direction is "west". The value of each element of the embedding vector may be a score for a healthy lifestyle.

The values of the elements of the embedding vector can be set arbitrarily when training the model or creating the conversion rules. For example, the server side can set the values arbitrarily as appropriate depending on the circumstances, such as what kind of information about user U's behavior the server side wants to obtain and use in the service it provides to the user U.

Here, the vector conversion model and the predetermined conversion rules for converting the location information into an embedding vector are simple models and conversion rules for converting sequence data of the location information into vector values indicating a specific action of the user U, such as "at home," "went to work," "went shopping," or "traveled by train." For example, when the information providing device 100 wants to know whether the user U has taken a specific action, it provides the terminal device 10 of the user U with a vector conversion model and the predetermined conversion rules for converting the location information related to the action into an embedding vector.

2. Example of information processing system configuration
Next, a configuration of an information processing system 1 including an information providing device 100 according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of a configuration of the information processing system 1 according to an embodiment. As shown in Fig. 2, the information processing system 1 according to an embodiment includes a terminal device 10 and an information providing device 100. These various devices are connected to each other via a network N so as to be able to communicate with each other by wire or wirelessly. The network N is, for example, a local area network (LAN) or a wide area network (WAN) such as the Internet.

Furthermore, the number of devices included in the information processing system 1 shown in FIG. 2 is not limited to that shown. For example, in FIG. 2, only one terminal device 10 is shown to simplify the illustration, but this is merely an example and is not limiting, and there may be two or more devices.

The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 is a smart device such as a smartphone or a 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, smart glasses, etc.

In addition, the terminal device 10 can connect to the network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local 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, etc. The information providing device 100 may be realized by cloud computing.

3. Example of terminal device configuration
Next, the configuration of the terminal device 10 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration 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.

(Communication unit 11)
The communication unit 11 is connected to a network N (see FIG. 2 ) by wire or wirelessly, and transmits and receives information to and from the information providing device 100 via the network N. For example, the communication unit 11 is realized by a network interface card (NIC), 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 electro-luminescent display (OLED). The display unit 12 is also a touch panel display, but is not limited to this.

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

(Positioning unit 14)
The positioning unit 14 receives signals (radio waves) transmitted from satellites of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10 based on the received signals. That is, the positioning unit 14 measures the position of the terminal device 10. Note that the GPS is merely an example of a GNSS (Global Navigation Satellite System).

The positioning unit 14 can also measure the position using various methods other than GPS. For example, the positioning unit 14 may measure the position using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction, etc., as described below.

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

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

(geomagnetic positioning)
In addition, the positioning unit 14 locates the position of the terminal device 10 based on a geomagnetic pattern of a structure that has been measured in advance and a geomagnetic sensor provided in the terminal device 10 .

(RFID positioning)
Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, in stores, etc., or has a function of reading an RFID tag, the location of use is recorded together with information on a payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to measure the location of the terminal device 10. Furthermore, the location may be measured by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

If necessary, the positioning unit 14 may use one or a combination of the positioning means described above to determine the position of the terminal device 10.

(Sensor unit 20)
The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. 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, an air pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

The above-mentioned sensors 21 to 28 are merely examples and are not limiting. In other words, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects the physical movement of the terminal device 10, such as the direction of movement, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects the physical movement of the terminal device 10, such as the tilt in three axial directions, based on the angular velocity of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

Since the terminal device 10 is equipped with the above-mentioned acceleration sensor 21, gyro sensor 22, air pressure sensor 23, etc., it is possible to determine the position of the terminal device 10 using technology such as Pedestrian Dead-Reckoning (PDR) that utilizes these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using positioning systems such as GPS.

For example, the number of steps, walking speed, and distance walked can be calculated using a pedometer that uses the acceleration sensor 21. In addition, the gyro sensor 22 can be used to know the user U's direction of travel, line of sight, and body inclination. In addition, the altitude and floor on which the user U's terminal device 10 is located can be known from the air pressure detected by the air pressure sensor 23.

The air temperature sensor 24 detects, for example, the air temperature around the terminal device 10. The sound sensor 25 detects, for example, sound around the terminal device 10. The light 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 around the terminal device 10.

The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the environment and situation around the terminal device 10 by detecting air pressure, temperature, sound, and illuminance, and capturing images of the surroundings. In addition, it is possible to improve the accuracy of the location information of the terminal device 10 based on the environment and situation around the terminal device 10.

(Control unit 30)
The control unit 30 includes, for example, a microcomputer having a central processing unit (CPU), a read only memory (ROM), a RAM, input/output ports, and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). 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 combination unit 36.

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

The transmitting unit 31 also transmits the embedding vector converted from the location information by the converting unit 35 to the information providing device 100 via the communication unit 11. At this time, the transmitting unit 31 may transmit the location information at that time together with the embedding vector.

(Receiving unit 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 a vector conversion model, a predetermined conversion rule, and the like provided from the information providing device 100 via the communication unit 11.

(Processing Unit 33)
The processing unit 33 controls the entire terminal device 10, including the display unit 12, etc. For example, the processing unit 33 can output various pieces of information transmitted by the transmission unit 31 and various pieces of information received by the reception unit 32 from the information providing device 100 to the display unit 12 for display.

(Acquisition unit 34)
The acquisition unit 34 constantly/periodically/at a predetermined timing acquires the location information of the terminal device 10. For example, the acquisition unit 34 acquires the location information of the terminal device 10 measured by the positioning unit 14. The acquisition unit 34 also estimates and acquires the location information of the terminal device 10 based on various information detected by the sensors 21 to 28.

The acquisition unit 34 also stores (saves) the acquired location information in the storage unit 40. For example, the acquisition unit 34 accumulates the acquired location information as a location history (e.g., a location history, an action history, etc.).

The acquisition unit 34 also acquires the vector conversion model and the specified conversion rules, etc., received by the receiving unit 32 from the information providing device 100, and stores (preserves) them in the memory unit 40.

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

When the conversion unit 35 acquires location information every minute, it arranges the location information for each minute in chronological order, and generates an embedding vector from the location information for 30 minutes (e.g., 8:01 to 8:30) in each time period. For example, the conversion unit 35 generates sequence data in which the location information for each minute in a 30-minute period is arranged in chronological order. Alternatively, the conversion unit 35 generates sequence data in which the location information for the first minute in a 30-minute period and the minute-by-minute differences from that location information are arranged in chronological order. The conversion unit 35 then generates an embedding vector based on the sequence data.

For example, the conversion unit 35 uses the sequence data as a dataset and converts the sequence data into a multidimensional real-valued vector using a machine learning technique using a neural network. That is, the conversion unit 35 inputs the sequence data into a vector conversion model generated through machine learning using, for example, a recurrent neural network (RNN) or a long short-term memory (LSTM), and obtains an embedding vector as an output. The conversion unit 35 may receive the vector conversion model from the information providing device 100.

Note that the RNN and LSTM may be neural networks based on the attention mechanism. The conversion unit 35 may also use a similar natural language processing model.

Alternatively, the conversion unit 35 may convert the sequence data into multiple different embedding vectors according to multiple different conversion rules (rule-based).

However, sequence data is merely one example. In reality, for example, raw location information data may be input directly into the model in chronological order.

(Joint portion 36)
The combining unit 36 combines multiple embedding vectors. Here, the combining unit 36 combines embedding results (vectors) of different versions (multiple patterns). Note that the embedding results of different versions are generated using different models, conversion rules, etc.

The combining unit 36 may also add (combine) additional information to the embedding vector. For example, the combining unit 36 may add information defining a data type to the embedding vector. In other words, the combining unit 36 also functions as an adding unit that adds additional information to the embedding vector.

The combining unit 36 may also add noise to the embedding vector. For example, the combining unit 36 adds noise such as dummy data to the embedding vector. In this case, the combining unit 36 may define the arrangement of the dummy data in the elements of the embedding vector as a data type. In other words, the combining unit 36 also functions as an adding unit that adds noise to the embedding vector.

(Memory unit 40)
The storage unit 40 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk drive (HDD), a solid state drive (SSD), or an optical disk. Various programs and various data are stored in the storage unit 40. Here, the storage unit 40 stores, 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 position information of the terminal device 10 measured by the positioning unit 14. In addition, the storage unit 40 stores various information processed by the processing unit 33 and various information received by the receiving unit 32. In addition, 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, a configuration of the information providing device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration 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 network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 2) in a wired or wireless manner.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a 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, a predetermined conversion rule, etc. 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 "recipient".

"Conversion ID" indicates identification information for identifying a vector conversion model or specific conversion rules for converting position information into an embedding vector.

In addition, "conversion information" refers to a file that digitizes a vector conversion model identified by a conversion ID, a specific conversion rule, etc. Note that the vector conversion model and the specific conversion rule may be generated based on an existing location history (or hypothetical location information) that accumulates location information collected in the past, for example.

In addition, "recipient" indicates the terminal device 10 to which the vector conversion model identified by the conversion ID and the specified conversion rule are provided. For example, a terminal ID may be stored in "recipient". There may be multiple terminal devices 10 to which the data is provided. In other words, multiple terminal IDs may be stored in "recipient". Note that if there is no need to specify the terminal device 10 to which the data is provided, the "recipient" item may not be included.

For example, in the example shown in FIG. 5, the vector conversion model and the specified conversion rules, which are the conversion information "conversion information #1" identified by the conversion ID "T1", are provided to the destination "destination #1".

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

(Vector information database 122)
The vector information database 122 stores various information related to 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".

"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 by the server side, or may be the contact information of the terminal device 10 (telephone number, email address, etc.). Note that if there is no need to identify the terminal device 10, the "Terminal ID" item may be omitted.

In addition, "embedding vector" indicates an embedding vector received from the terminal device 10. Note that there may be multiple "embedding vectors" for one terminal device 10. For example, if the terminal device 10 generates an embedding vector every 30 minutes, the embedding vector for every 30 minutes is stored.

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

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

The vector information database 122 may store various information depending on the purpose, not limited to the above. For example, the vector information database 122 may store identification information (vector ID, etc.) for identifying each embedding vector on a received data basis. The vector information database 122 may also store information indicating the date and time when each embedding vector was received. The vector information database 122 may also store information regarding rules for interpreting embedding vectors. The vector information database 122 may also store conversion information such as a vector conversion model and a predetermined conversion rule.

(Control unit 130)
Returning to Fig. 4, the description will be continued. The control unit 130 is a controller, and is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or the like, executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information providing device 100 using a storage area such as a RAM as a working area. In the example shown in Fig. 4, the control unit 130 has a collection unit 131, a determination unit 132, a generation unit 133, and a provision unit 134.

(Collection Unit 131)
The collection unit 131 collects embedding vectors from the individual terminal devices 10. That is, the collection unit 131 receives the combined embedding vector from the terminal devices 10 via the communication unit 110.

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

The determination unit 132 also places the embedding vectors in a specific space and determines the similarity or change in the user U's own behavior, and/or the similarity or difference between the behavior of the user U and other users. For example, the determination unit 132 decomposes the combined embedding vectors and places embedding vectors generated by the same method in the same specific space. Note that, in practice, all decomposed embedding vectors may be placed in the same specific space, regardless of the difference in the method.

When noise is added to the embedding vector, the determination unit 132 may remove the noise from the embedding vector, or may process the embedding vector while keeping the noise included.

(Generation unit 133)
The generation unit 133 generates a vector conversion model and/or predetermined conversion rules for converting location information into an embedding vector based on an existing location history (or hypothetical location information) that accumulates location information collected in the past.

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

Here, the generation unit 133 generates a vector conversion model by machine learning using a recurrent neural network (RNN) or a long short-term memory (LSTM). Note that the model can be of any type and format. For example, the generation unit 133 may use a support vector machine (SVM) or a deep neural network (DNN) as a model. Here, the DNN may be a convolutional neural network (CNN) or an RNN. The model may also be a model realized by combining multiple models, such as a model that combines a CNN and an RNN.

Note that the RNN and LSTM may be neural networks based on the attention mechanism. The generation unit 133 may also use a similar natural language processing model.

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

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

5. Processing Procedure
Next, a process performed 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 the process performed according to the embodiment. The process 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 generates a vector conversion model and predetermined conversion rules for converting location information into an embedding vector based on an existing location history that accumulates location information of the terminal device 10 collected in the past (step S101). For example, the generation unit 133 generates a vector conversion model that outputs an embedding vector when location information is input by having the model learn a pair of location information and an embedding vector as correct answer data.

Then, the providing unit 134 of the information providing device 100 provides the generated vector conversion model, the predetermined conversion rule, etc. to the terminal device 10 of the user U (step S102). That is, the providing unit 134 transmits the generated vector conversion model, the predetermined conversion rule, etc. 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 and the specified conversion rules, etc. provided by the information providing device 100 (step S103).

Then, the acquisition unit 34 of the terminal device 10 acquires the location information of the terminal device 10 constantly/periodically/at a predetermined timing (step S104). For example, the acquisition unit 34 acquires the location information of the terminal device 10 measured by the positioning unit 14. The acquisition unit 34 also estimates and acquires the indoor location 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 location information as a location history (e.g., a location history, an action history, etc.) (step S105). For example, the acquisition unit 34 stores (saves) the acquired location information in the storage unit 40.

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

Then, the conversion unit 35 generates multiple different embedding vectors using different methods based on the sequence data (step S107).

For example, the conversion unit 35 inputs sequence data to each of a number of different vector conversion models generated through machine learning using, for example, a recurrent neural network (RNN) or a long short-term memory (LSTM), and obtains a number of different embedding vectors as the respective outputs. Note that the RNN and LSTM may be neural networks based on an attention mechanism. The conversion unit 35 may also use a similar natural language processing model.

Alternatively, the conversion unit 35 may analyze the sequence data and convert the sequence data into multiple different embedding vectors according to multiple different conversion rules (rule-based).

However, sequence data is merely one example. In reality, for example, raw location information data may be input directly into the model in chronological order.

Then, the combining unit 36 combines the multiple embedding vectors (step S108).

Here, the combining unit 36 combines embedding results (vectors) of different versions (multiple patterns). The different versions of the embedding results are generated using different models, conversion rules, etc. The combining unit 36 may also add (combine) additional information to the embedding vector. For example, the combining unit 36 may add information that defines a data type to the embedding vector. The combining unit 36 may also add (combine) noise to the embedding vector.

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

Then, the collection unit 131 of the information providing device 100 collects the embedding vectors from each terminal device 10 (step S110). That is, the collection 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 vectors and determines the behavior of user U (location, behavior, movement, etc.) (step S111).

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

At this time, the determination unit 132 may decompose the combined embedding vector, place the embedding vectors generated by the same method in the same specific space, and determine the similarity or change in the user U's own behavior, and/or the similarity or difference between the behavior of the user U and 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 perform analysis of the embedding vector while keeping the noise included.

The generation unit 133 of the information providing device 100 may also update the vector conversion model, the specified conversion rules, etc., based on the embedding vector received from the terminal device 10.

6. Modifications
The terminal device 10 and the information providing device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, modifications of the embodiment will be described below.

In the above embodiment, the information providing device 100 may determine the user U's actions, such as "went to the mountains," "went to the sea," "went to the countryside," "went abroad," etc., based on the vector value of the combined embedding vector. It may also determine the user U's actions, such as "went on a trip," "went on a business trip," etc. It may also determine the user U's actions, such as "went on a day trip," "stayed overnight," etc.

In the above embodiment, if the terminal device 10 does not want to inform the information providing device 100 (server side) of detailed information regarding the behavior of the user U, the terminal device 10 may perform fractional processing (rounding processing) of the location information when storing the location information or prior to embedding, and round the location information to convert it into location information with low accuracy. For example, when the location information is latitude and longitude information of GPS (Global Positioning System) coordinates, the terminal device 10 rounds up or down (rounding up or down is also possible) the decimal points of the latitude and longitude decimal angles (minutes or less in the degree-minute-second method) to convert it into location information with a rough value. This makes it possible to limit the content of information regarding the behavior of the user U that is transmitted from the terminal device 10 to the information providing device 100 (server side). In addition, the amount of information of the location information as learning data for machine learning can be reduced, and the processing load can be reduced.

In addition, in the above embodiment, the value of each element of the embedding vector is expressed as "0" and "1" for convenience, but in reality, it may be a number greater than "2", a multi-digit number, or a number with an integer part and a decimal part. In other words, it is sufficient if it is a real number. This makes it possible to express multiple pieces of information or detailed information in each element of a multidimensional embedding vector.

In addition, in the above embodiment, the terminal device 10 accumulates the acquired position information as a position history (e.g., location history, behavior history, etc.), but if accumulation of the position history is restricted or prohibited, the terminal device 10 may accumulate the position information in a vectorized state. For example, the terminal device 10 may convert the position information into a vector value indicating "at home," "at work," "at a store," "outdoors," etc., based on the location indicated by each piece of position information, and accumulate the converted vector. Then, if the same vector value occurs consecutively along a time series, they may be collectively treated as an embedding vector.

In addition, in the above embodiment, the terminal device 10 or the information providing device 100 may individually adjust the vector conversion model or the predetermined conversion rule for converting the position information into an embedding vector according to the characteristics and circumstances of each user U. For example, the vector conversion model or the predetermined conversion rule may be customized.

In the above embodiment, a vector conversion model for converting location information into an embedding vector may be generated using federated learning or the like. For example, in federated learning, each terminal device 10 performs machine learning on the vector conversion model using location information as learning data, and transmits difference data between the original model and the learned model to the information providing device 100. The information providing device 100 receives the difference data from each terminal device 10, integrates the difference data, learns, and generates a common model. Then, the information providing device 100 provides each terminal device 10 with parameters of the generated common model, etc. 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 that consideration can be given to the personal information and privacy of the user.

7. Effects
As described above, the information processing device (terminal device 10) according to the present application includes an acquisition unit 34 that acquires location information, a conversion unit 35 that converts the location information into a plurality of vectorized data, a combination unit 36 that combines the plurality of vectorized data, and a transmission unit 31 that transmits the combined vectorized data. For example, the conversion unit 35 converts the location information into a plurality of vectorized data using a plurality of vector conversion models obtained by machine learning. Alternatively, the conversion unit 35 converts the location information into a plurality of vectorized data according to a plurality of conversion rules. In addition, the combination unit 36 may add (combine) additional information (e.g., data type, etc.) to the combined vectorized data. Furthermore, the combination unit 36 may add (combine) noise to the combined vectorized data.

This allows the information processing device to combine embedding information in multiple formats in advance to generate embedding information that can be used for multiple purposes.

8. Hardware Configuration
Moreover, the terminal device 10 and the information providing device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration as shown in Fig. 8, for example. The information providing device 100 will be described below as an example. Fig. 8 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I/F (Interface) 1060, an input I/F 1070, and a network I/F 1080 are connected by a bus 1090.

The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized, for example, by 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 primarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), a HDD (Hard Disk Drive), a SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may be a removable storage medium such as a USB memory or a SD (Secure Digital) memory card. The secondary storage device 1050 may be a cloud storage device (online storage device), a 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 types of information, such as a display, projector, printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). The input I/F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, keypad, button, scanner, etc., and is realized by a USB, etc.

In addition, 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.

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

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

The network I/F 1080 receives data from other devices via the network N and sends it to the computing device 1030, and also transmits data generated by the computing device 1030 to other devices 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 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I/F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with other devices via the network I/F 1080 and use the functions and data of a program by calling them from other programs of the other devices.

[9. Other]
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. The above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-described components can be appropriately combined. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

For example, the information providing device 100 described above may be realized by multiple server computers, and depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

The above-described embodiments and variations can be combined as appropriate to the extent that they do not cause inconsistencies in the processing content.

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

REFERENCE SIGNS LIST 1 Information processing system 10 Terminal device 14 Positioning unit 30 Control unit 31 Transmitting unit 32 Receiving unit 33 Processing unit 34 Acquiring unit 35 Converting unit 36 Combining unit 100 Information providing device 110 Communication unit 120 Storage unit 121 Conversion information database 122 Vector information database 130 Control unit 131 Collection unit 132 Determination unit 133 Generation unit 134 Provision unit

Claims (12)

An acquisition unit for acquiring location information;
a conversion unit that converts raw data of position information that has not been subjected to rounding into a plurality of vectorized data by a plurality of methods;
A combining unit that combines a plurality of the vectorized data;
A transmitter for transmitting the combined vectorized data;
An information processing device comprising: The combining unit adds additional information to the combined vectorized data.
2. The information processing apparatus according to claim 1, The combining unit adds noise to the combined vectorized data.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. The conversion unit converts a plurality of pieces of position information into a single piece of vectorized data in each method.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. The conversion unit converts sequence data in which a plurality of pieces of position information are arranged in a time series into the multidimensional vectorized data in each of the methods.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. The conversion unit converts the location information into a plurality of the vectorized data using a plurality of vector conversion models obtained by machine learning.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. The conversion unit converts the position information into a plurality of the vectorized data in accordance with a plurality of conversion rules.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. The conversion unit converts a part of the plurality of pieces of position information into a plurality of vectorized data,
The combining unit combines a plurality of the vectorized data converted from a part of the position information,
The transmission unit transmits the combined vectorized data and the remaining position information.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. The vectorized data is indicative of user behavior.
3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer. a collection unit that collects from the terminal device combined vectorized data obtained by combining a plurality of vectorized data obtained by converting raw data of location information that has not been subjected to rounding by a plurality of methods;
A determination unit that determines a user's behavior based on the combined vectorized data;
An information processing device comprising: An information processing method executed by an information processing device,
An acquisition step of acquiring location information;
A conversion step of converting raw data of position information that has not been subjected to rounding into a plurality of vectorized data by a plurality of methods;
A combining step of combining a plurality of the vectorized data;
a transmitting step of transmitting the combined vectorized data;
13. An information processing method comprising: An acquisition procedure for acquiring location information;
A conversion procedure for converting raw location information data that has not been subjected to rounding into multiple vectorized data using multiple methods;
a combining step of combining a plurality of the vectorized data;
a transmitting step of transmitting the combined vectorized data;
An information processing program for causing a computer to execute the above.

Images