Information processing device, distribution method, and distribution program

Abstract

To improve the accuracy of information provided. [Solution] The information processing device 100 according to the present application includes an acquisition unit 152, an identification unit 153, a determination unit 154, and a distribution unit 155. The acquisition unit 152 acquires multiple vectors corresponding to multiple users, each generated using a machine learning model trained to generate similar vectors from sensor information of similar situations. The identification unit 153 identifies a user group with similar vectors based on the multiple vectors. The determination unit 154 determines the content of information to be provided to the user group based on information on logged-in users included in the user group identified by the identification unit 153. The distribution unit 155 distributes the determined information content to the devices owned by each member of the user group.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a distribution method, and a distribution program. [Background technology]

[0002] Information is provided to people visiting various regions for the purpose of guiding them to tourist spots, promoting products, etc. For example, one conventional technology for providing information to people visiting a certain region involves running machine learning on a machine learning model based on learning data that defines in advance the relationship between the person's context (such as riding a train or running a marathon) and the information to be provided to the person, and then using the machine learning model to identify and deliver the information to be provided to the person. [Prior art documents] [Non-patent literature]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-154864 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-mentioned conventional technology, there is a time lag between the person's situation used during machine learning and the person's situation at the time the information is provided, so there is room for improvement in terms of improving the accuracy of information provision.

[0005] The present application has been made in view of the above, and aims to provide an information processing device, a learning method, and a learning program that can improve the accuracy of information provision. [Means for solving the problem]

[0006] The information processing device according to the present application includes an acquisition unit, an identification unit, a determination unit, and a distribution unit. The acquisition unit acquires multiple vectors corresponding to multiple users, each generated using a machine learning model trained to generate similar vectors from sensor information of similar situations. The identification unit identifies a user group having similar vectors based on the multiple vectors. The determination unit determines the content of information to be provided to the user group based on information on logged-in users included in the user group identified by the identification unit. The distribution unit distributes the determined information content to devices owned by each member of the user group. [Effects of the Invention]

[0007] According to one aspect of the embodiment, it is possible to improve the accuracy of information provision. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing the configuration of an information processing system according to this embodiment. [Figure 2] FIG. 2 is a diagram for explaining the processing of the information processing device according to this embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the data structure of the determination table. [Figure 4] FIG. 4 is a diagram showing the configuration of a user terminal according to this embodiment. [Figure 5] FIG. 5 is a diagram showing the configuration of an information processing device according to this embodiment. [Figure 6] FIG. 6 illustrates an example of the data structure of the user management table. [Figure 7] FIG. 7 is a diagram illustrating an example of the data structure of the vector table. [Figure 8] FIG. 8 is a flowchart showing the processing procedure of the information processing device according to this embodiment. [Figure 9] FIG. 9 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, the information processing device, distribution method, and distribution program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, distribution method, and distribution program according to the present application are not limited to these embodiments. Furthermore, the embodiments can be appropriately combined as long as the processing content is not contradictory. Furthermore, the same components in the following embodiments are designated by the same reference numerals, and redundant explanations will be omitted.

[0010] 1. Configuration of Information Processing System 1 First, an example of the configuration of an information processing system 1 according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing the configuration of the information processing system 1 according to this embodiment. For example, the information processing system 1 includes user terminals 10a, 10b, 10c, and 10d, and an information processing device 100. The user terminals 10a to 10d and the information processing device 100 are connected to each other via a network 2.

[0011] The user terminals 10a to 10d are terminal devices used by users. The user terminals 10a to 10d correspond to tablet terminals, smartphones, notebook PCs (Personal Computers), etc. For example, it is assumed that users 3a, 3b, 3c, and 3d use user terminals 10a, 10b, 10c, and 10e, respectively.

[0012] The user terminals 10a to 10d may be connected to the network 2 via a base station or the like. Although the user terminals 10a to 10d are shown in FIG. 1, other user terminals may also be included. In the following description, when there is no need to distinguish between the user terminals 10a to 10d, the user terminals 10a to 10d will be collectively referred to as "user terminal 10." The users 3a to 3d will be collectively referred to as "user 3."

[0013] The user 3 operates the user terminal 10 to log in to a website provided by the information processing device 100 and receive various services. The user terminal 10 also includes various sensors and generates sensor data in real time using these sensors. The sensor data is data that associates time with various sensor values. For example, the various sensor values ​​include illuminance, sound, position, atmospheric pressure, speed, and images.

[0014] The user terminal 10 has a machine learning model that has been distributed in advance. This machine learning model is a model (NN) that has been trained in advance with sensor data as input data and vector data as output (correct answer label).<Neural Network> The machine learning model is trained so that when it receives input of multiple sensor data generated under similar circumstances, it outputs multiple vector data that are similar to each other. For example, if the cosine similarity between a vector and another vector is equal to or greater than a threshold, the vector and the other vector are similar to each other.

[0015] For example, if user 3a and user 3b are in the same area (a busy shopping district) and are performing similar actions (for example, standing still and operating user terminal 10), the vector data generated based on the sensor data of user terminal 10a and the vector data generated based on the sensor data of user terminal 10b will be similar vectors.

[0016] Furthermore, if user 3c and user 3d are in the same area (an airport) and are performing similar actions (for example, pulling a carry cart), the vector data generated based on the sensor data of user terminal 10c and the vector data generated based on the sensor data of user terminal 10d will be similar vectors.

[0017] The user terminal 10 generates vector data in real time by inputting sensor data acquired in real time into a machine learning model. The user terminal 10 transmits the vector data generated in real time to the information processing device 100, regardless of whether the user 3 has logged in. Information identifying the user terminal 10 is attached to the vector data. In the following description, information that uniquely identifies the user terminal 10 will be referred to as "terminal identification data." The terminal identification data is the IP address, device ID, cookie, etc. of the user terminal 10.

[0018] While the user terminal 10 is logged in to a website provided by the information processing device 100 by an operation of the user 3, the user terminal 10 transmits sensor data in addition to the above vector data to the information processing device 100. Terminal identification data is attached to the sensor data.

[0019] The information processing device 100 is a server device that provides a website and the like and provides various services to user terminals 10 that have logged in. The information processing device 100 also acquires vector data in real time from all user terminals 10 that are present in a predetermined area that has been designated in advance, regardless of whether they have logged in. The information processing device 100 determines the content of information to be provided to the user 3 based on the acquired vector data, and delivers the information of the determined content to the user terminal 10. An example of processing by the information processing device 100 will be described below.

[0020] 2 is a diagram for explaining the processing of the information processing device 100 according to this embodiment. As an example, in FIG. 2, vector data acquired by the information processing device 100 from user terminals 10 included in a specified area are represented as vector data v1a, v1b, v1c, v1d, v1e, v1f, v1g, v2a, v2b, v2c, v2d, v2e, v2f, v2g, v2h, v2a, v2b, v2c, v2d, v2e, and v2f.

[0021] In the following description, when we refer to a "user corresponding to vector data," we mean a user 3 who uses the user terminal 10 that is the sender of the vector data. For example, if the user terminal 10 that is the sender of vector data v1a is the user terminal 10a shown in FIG. 1, the user 3 of the vector data v1a is user 3a.

[0022] The information processing device 100 classifies similar vector data into the same group by performing clustering or the like on multiple vector data. In the example shown in Fig. 2, the information processing device 100 classifies vector data v1a to v1g into group G1. The information processing device 100 classifies vector data v2a to v2h into group G2. The information processing device 100 classifies vector data v3a to v3f into group G3.

[0023] For example, among the multiple users corresponding to vector data v1a to v1g included in group G1, the users corresponding to vector data v1a to v1c are defined as logged-in users (hereinafter referred to as logged-in users where appropriate). Among the multiple users corresponding to vector data v2a to v2h included in group G2, the users corresponding to vector data v2b, v2e, and v2g are defined as logged-in users. Among the multiple users corresponding to vector data v3a to v3f included in group G3, the user corresponding to vector data v3a is defined as the logged-in user.

[0024] The information processing device 100 determines the content of information to be distributed to the user terminals 10 of the multiple users corresponding to the vector data v1a to v1g included in the group G1 based on the attribute data of the logged-in user corresponding to the vector data v1a to v1c. In the following description, the user terminals 10 of the multiple users corresponding to the vector data v1a to v1g included in the group G1 will be referred to as "user terminals 10 of the group G1."

[0025] The information processing device 100 determines the content of information to be distributed to the user terminals 10 of the multiple users corresponding to the vector data v2a to v2h included in group G2 based on the attribute data of the logged-in users corresponding to the vector data v2b, v2e, and v2g. In the following description, the user terminals 10 of the multiple users corresponding to the vector data v2a to v2h included in group G2 will be referred to as "user terminals 10 of group G2."

[0026] The information processing device 100 determines the content of information to be distributed to the user terminals 10 of the multiple users corresponding to the vector data v3a to v3f included in group G3 based on the attribute data of the logged-in user corresponding to the vector data v3a. In the following description, the user terminals 10 of the multiple users corresponding to the vector data v3a to v3f included in group G3 will be referred to as "user terminals 10 of group G3."

[0027] For example, when determining the content of information to be distributed, the information processing device 100 may use a decision table, which will be described below. Fig. 3 is a diagram showing an example of the data structure of the decision table. As shown in Fig. 3, decision table 141 associates item numbers, occupations, annual incomes, time periods, temperatures, speeds, locations, and distribution data.

[0028] The item number is a number that identifies each record in the decision table 141. The occupation and annual income are the occupation and annual income of the logged-in user, and are set in the attribute data of the logged-in user. The time period is the current time period obtained from a timer or the like. The temperature, speed, and location are information (sensor data) obtained from the user terminal 10 of the logged-in user, and correspond to the temperature, speed, and location information measured by the user terminal 10 of the logged-in user. The distribution data indicates data to be distributed to all user terminals 10 belonging to the corresponding group.

[0029] When multiple logged-in users are included in the same group, the information processing device 100 may identify and use attribute data common to the multiple logged-in users. When multiple logged-in users are included in the same group, the information processing device 100 may identify attribute data by majority vote or the like. When multiple logged-in users are included in the same group, the information processing device 100 may use the average value, median value, or the like of sensor data acquired from the user terminals 10 of the logged-in users.

[0030] For example, it is assumed that the attribute data of the logged-in users included in group G1 is set to "occupation: office worker" and the current time period acquired from the timer is between "9:00 AM and 5:00 PM." Furthermore, it is assumed that the sensor data of the user terminals 10 used by the logged-in users included in group G1 indicates that the temperature is "less than C1 degrees," the speed is "between V1 and V2," and the location is "downtown A." In this case, the information processing device 100 determines the content of the information to be distributed to the user terminals 10 of group G1 to be "advertising data for a gym," and distributes the "advertising data for a gym" to all user terminals 10 in group G1.

[0031] For example, suppose that the "annual income" included in the attribute data of the logged-in users included in group G2 is "above threshold" and the time period acquired from the timer is "9:00 AM to 5:00 PM." Furthermore, suppose that the sensor data of the user terminals 10 used by the logged-in users included in group G2 indicates that the temperature is "between C2 and C3 degrees," the speed is "less than V1 (stopped)," and the location is "downtown A." In this case, the information processing device 100 determines the content of the information to be distributed to the user terminals 10 of group G2 to be "beauty advertisement data," and distributes the "beauty advertisement data" to all user terminals 10 in group G2.

[0032] For example, suppose that the "annual income" included in the attribute data of the logged-in users included in group G3 is "above a threshold value," and the time period acquired from the timer is "9:00 AM to 5:00 PM." Furthermore, suppose that the sensor data of the user terminals 10 used by the logged-in users included in group G3 indicates that the temperature is "between C2 and C3 degrees," the speed is "between V1 and V2," and the location is "downtown A." In this case, the information processing device 100 determines that the content of the information to be distributed to the user terminals 10 of group G3 is "advertising data for cruise ships," and distributes the "advertising data for cruise ships" to all user terminals 10 of group G3.

[0033] As described above, the information processing device 200 classifies vector data acquired in real time from the user terminal 10 into groups for each similar vector data, regardless of whether the user is logged in. Then, the information processing device 200 determines distribution data to be provided to users in the same group based on the attribute data of the logged-in user among multiple users included in the same group, and distributes the determined distribution data. This improves the accuracy of information provision.

[0034] 2. Configuration of User Terminal 10 Next, an example of the configuration of the user terminal 10 (for example, the user terminals 10a to 10d) shown in Fig. 1 will be described. Fig. 4 is a diagram showing the configuration of the user terminal 10 according to this embodiment. As shown in Fig. 4, the user terminal 10 has a communication unit 11, an input unit 12, a display unit 13, a sensor group 14, a storage unit 15, and a control unit 16.

[0035] The communication unit 11 is realized by a NIC (Network Interface Card) or the like. The communication unit 11 is wirelessly connected to the network 2 via a base station or the like, and transmits and receives information to and from various other devices. For example, the control unit 16, which will be described later, transmits and receives information to and from the information processing device 100 using the communication unit 11.

[0036] The input unit 12 is a device for inputting various types of information to the control unit 16 of the user terminal 10. The input unit 12 is an input device such as a keyboard, a mouse, or a touch panel. The user 3 operates the input unit 12 to access the information processing device 100 and perform operations such as logging in.

[0037] The display unit 13 is a device for displaying information output from the control unit 16. The display unit 13 is a display device such as a display or a touch panel. For example, the display unit 13 displays a screen of a website provided by the information processing device 100, a login screen, and advertisement data determined by the information processing device 100 through the above-mentioned processing.

[0038] The sensor group 14 is a plurality of sensors for measuring the above-mentioned sensor data. The sensor group 14 repeatedly executes a process of storing the measured sensor data as sensor data 15a in the storage unit 15. For example, the sensor group 14 includes an illuminance sensor, an acoustic sensor, a GPS (Global Positioning System), an air pressure sensor, a temperature sensor, an acceleration sensor, a gyro sensor, an image sensor, and the like.

[0039] The storage unit 15 is realized by a semiconductor memory element such as a flash memory, or a storage device such as a hard disk, etc. For example, the storage unit 15 has sensor data 15a and a machine learning model 15b.

[0040] The sensor data 15a is data measured by the sensor group 14. For example, the sensor data 15a is data in which time is associated with various sensor values, such as illuminance, sound, position, atmospheric pressure, speed, and image.

[0041] Machine learning model 15b is a machine learning model distributed in advance. When sensor data is input, machine learning model 15b outputs vector data. Other descriptions regarding machine learning model 15b are the same as the descriptions regarding the machine learning models described above.

[0042] The control unit 16 is a controller, and is realized by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs stored in a storage device (for example, the storage unit 15) within the user terminal 10 using RAM as a work area. The control unit 16 is also a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0043] The control unit 16 has an access unit 16a, a generation unit 16b, a transmission unit 16c, and a display processing unit 16d, and realizes or executes the functions and actions of the processes described below. Note that the internal configuration of the control unit 16 is not limited to the configuration shown in Fig. 4, and other configurations may be used as long as they perform the processes described below.

[0044] When the access unit 16a receives an access request to the information processing device 100 from the user 3 operating the input unit 12, the access unit 16a accesses the information processing device 100 and receives various services from the information processing device 100. When the access unit 16a receives a login operation to the information processing device 100 from the user 3 operating the input unit 12, the access unit 16a transmits login data to the information processing device 100. For example, the login data includes a user ID, password, terminal identification data, etc. that identify the user 3.

[0045] On the other hand, when the access unit 16a receives a logoff operation for the information processing device 100 from the user 3 operating the input unit 12, the access unit 16a transmits logoff data to the information processing device 100. For example, the logoff data includes a user ID for identifying the user 3, a logoff request, terminal identification data, and the like.

[0046] The user terminal 10 is in a state of being logged in to the information processing device 100 during the period from when the access unit 16a transmits the login data to the information processing device 100 until when the access unit 16a transmits the logoff data to the information processing device 100.

[0047] The generator 16b generates vector data by inputting the sensor data 15a to the machine learning model 15b. The generator 16b outputs the generated vector data to the transmitter 16c. For example, the generator 16b repeatedly executes the above process at predetermined time intervals.

[0048] The transmission unit 16c assigns terminal identification data to the vector data generated by the generation unit 16b, and transmits the vector data with the assigned terminal identification data to the information processing device 100. Although not described below, it is assumed that the vector data has been assigned terminal identification data. The transmission unit 16c transmits the vector data to the information processing device 100 regardless of whether or not a user has logged in to the information processing device 100.

[0049] Furthermore, the transmitter 16c transmits the sensor data 15a in addition to the vector data to the information processing device 100 via the access unit 16a while the user terminal 10 is logged in to the information processing device 100. That is, while the user terminal 10 is logged in to the information processing device 100, the transmitter 16c transmits the sensor data 15a to the information processing device 100 together with the vector data.

[0050] The display processing unit 16d outputs and displays various types of information transmitted from the information processing device 100 on the display unit 13. For example, the display processing unit 16d receives a website screen, a login screen, advertising data, and the like transmitted from the information processing device 100, and displays them on the display unit 13.

[0051] 3. Configuration of Information Processing Device 100 Next, an example of the configuration of the information processing device 100 shown in Fig. 1 will be described. Fig. 5 is a diagram showing the configuration of the information processing device 100 according to this embodiment. As shown in Fig. 5, the information processing device 100 includes a timer 105, a communication unit 110, an input unit 120, a display unit 130, a storage unit 140, and a control unit 150.

[0052] The timer 105 is a device that outputs the current time to the control unit 150 .

[0053] The communication unit 210 is realized by a NIC or the like. The communication unit 110 is connected to a network via a wired or wireless connection and transmits and receives information to and from various other devices. For example, the control unit 150 (described later) uses the communication unit 110 to transmit and receive information to and from the user terminal 10.

[0054] The input unit 120 is a device for inputting various types of information to the control unit 150 of the information processing device 100. The input unit 120 is an input device such as a keyboard, a mouse, or a touch panel.

[0055] The display unit 130 is a device for displaying information output from the control unit 150. The display unit 130 is a display device such as a display or a touch panel.

[0056] The storage unit 140 is realized by a semiconductor memory element (non-volatile memory) such as a flash memory, or a storage device such as a hard disk, etc. For example, the storage unit 140 has a determination table 141, a user management table 142, and a vector table 143.

[0057] The decision table 141 is used when deciding information to be delivered to the user terminal 10. The explanation regarding the decision table 141 is the same as that given in FIG.

[0058] The user management table 142 is a table that holds various types of information related to users. Fig. 6 is a diagram showing an example of the data structure of the user management table 142. As shown in Fig. 6, the user management table 142 associates a user ID, a login flag, terminal identification data, a name, and attribute data.

[0059] The user ID is information that uniquely identifies a user. The login flag is a flag that indicates whether the corresponding user is logged in or not. When the user is logged in, the login flag is "on", and when the user is logged off, the login flag is "off". The terminal identification data is information that identifies the user terminal 10 used when the user performed the login operation, and includes the IP address, device ID, cookies, etc. of the user terminal 10. The name is the name of the user. The attribute data includes information such as occupation, annual income, hobbies, etc. In the example shown in Figure 6, occupation, annual income, and hobbies are shown as attribute data, but the attribute data may also include other attributes.

[0060] Each data item in the user management table 142 is registered in advance by the user. The user may operate the user terminal 10 to access the information processing device 100 and update their own data (terminal identification data, name, attribute data) as needed.

[0061] The vector table 143 is a table that holds vector data acquired in real time from all user terminals 10 included in a pre-specified area. Fig. 7 is a diagram showing an example of the data structure of the vector table 143. In the example shown in Fig. 7, the vector table 143 associates terminal identification data, vector data, and sensor data.

[0062] As described above, the information processing device 100 acquires vector data from all user terminals 10 within the specified area and registers the vector data in the vector table 143. Since vector data and sensor data are transmitted from the user terminal 10 of the logged-in user, the information processing device registers the vector data and sensor data in the vector table 143. The information processing device 100 may register the sensor data in a table separate from the vector table 143.

[0063] The control unit 150 is a controller, and is realized by a processor such as a CPU or an MPU, for example, executing various programs stored in a storage device (for example, the storage unit 140) inside the information processing device 100 using a RAM as a work area. The control unit 150 is also a controller, and is realized by an integrated circuit such as an ASIC or an FPGA.

[0064] Control unit 150 has service providing unit 151, acquisition unit 152, identification unit 153, determination unit 154, and distribution unit 155, and realizes or executes the functions and actions of the processes described below. Note that the internal configuration of control unit 150 is not limited to the configuration shown in Fig. 5, and other configurations may be used as long as they perform the processes described below.

[0065] The service providing unit 151 receives access from the user terminal 10, displays a website, etc., and provides various services. When the service providing unit 151 receives access from the user terminal 10, it displays a login screen, etc., and receives login data from the user terminal 10. Based on the login data and the user management table 142, the service providing unit 151 sets a login flag corresponding to the logged-in user to "on." On the other hand, when the service providing unit 151 receives log-off data from the user terminal 10, it sets the corresponding login flag to "off."

[0066] The acquisition unit 152 acquires vector data from all user terminals 10 within the specified area and registers the data in the vector table 143. Since vector data and sensor data are transmitted from the user terminal 10 of the logged-in user, the acquisition unit 152 registers the vector data and sensor data in the vector table 143. The acquisition unit 152 updates the vector table 143 every time it acquires vector data and sensor data.

[0067] The identification unit 153 performs clustering on all vector data registered in the vector table 143, thereby classifying similar vector data into the same group. For example, the identification unit 153 may use a K-Means method or the like when performing clustering. The clustering performed by the identification unit 153 corresponds to the clustering described with reference to FIG. 2. The identification unit 153 outputs the clustering result to the determination unit 154.

[0068] In the clustering result, for each group, terminal identification data corresponding to the vectors included in the corresponding group is set. For example, in the example shown in Fig. 2, terminal identification data corresponding to vector data v1a to v1g is set in association with group G1. Terminal identification data corresponding to vector data v2a to v2h is set in association with group G2. Terminal identification data corresponding to vector data v3a to v3f is set in association with group G3.

[0069] The determination unit 154 determines distribution data to be provided to the user terminal 10 (user 3) classified into the same group based on the attribute data of the logged-in user of the user terminal 10 classified into the same group. An example of the processing of the determination unit 154 will be described below.

[0070] The determination unit 154 acquires multiple terminal identification data classified into a certain group (here, group G1) from the clustering result, and compares the acquired terminal identification data with the user management table 142 to identify the terminal identification data of the logged-in users of group G1.

[0071] The determination unit 154 acquires attribute data corresponding to the terminal identification data of the logged-in users of group G1 from the user management table 142. The determination unit 154 acquires sensor data corresponding to the terminal identification data of the logged-in users of group G1 from the vector table 143. In addition, the determination unit 154 acquires the current time from the timer 105.

[0072] The determination unit 154 determines the distribution data based on the acquired current time, attribute data corresponding to the logged-in user of group G1, vector data, and the determination table 141. For example, if the attribute data corresponding to the logged-in user of group G1 includes "office worker," the current time is within the time period "9:00 AM to 5:00 PM," and the temperature, speed, and location <position> (sensor data) are less than C1 degrees, between V1 and V2, and downtown A, respectively, the determination unit 154 determines the distribution data to be "gym advertising data."

[0073] The determination unit 154 outputs to the distribution unit 155 information on the determination result that associates the terminal identification data included in the group G1 with the determined distribution data “gym advertisement data”.

[0074] The determination unit 154 repeats the above process for the other groups (groups G2 and G3).

[0075] When multiple logged-in users are included in the same group, the determination unit 154 may identify and use attribute data common to the multiple logged-in users. When multiple logged-in users are included in the same group, the determination unit 154 may identify attribute data by majority vote or the like. When multiple logged-in users are included in the same group, the determination unit 154 may use the average value, median value, or the like of sensor data acquired from the user terminals 10 of the respective logged-in users.

[0076] The distribution unit 155 acquires the determination result from the determination unit 154, and distributes the determined distribution data for each group.

[0077] 4. Processing Procedure of Information Processing Device 100 Next, an example of the processing procedure of the information processing device 100 according to this embodiment will be described. Fig. 8 is a flowchart showing the processing procedure of the information processing device 100 according to this embodiment. As shown in Fig. 8, the service providing unit 151 of the information processing device 100 updates the login flag in the user management table 142 every time login data or logoff data is received from the user terminal 10 (step S101).

[0078] The acquisition unit 152 of the information processing device 100 acquires vector data from all user terminals 10 included in the specified area and registers the data in the vector table 143 (step S102).

[0079] The identifying unit 153 of the information processing device 100 performs clustering on all the vector data registered in the vector table 143, thereby classifying similar vector data into the same group (step S103).

[0080] The determination unit 154 of the information processing device 100 acquires attribute data, sensor data, and the current time of a logged-in user from among multiple users included in the group (step S105). The determination unit 154 determines distribution data to be distributed to the user terminals 10 of the group based on the acquired attribute data, sensor data, current time, and the determination table 141 (step S105).

[0081] The distribution unit 155 of the information processing device 100 distributes the determined distribution data to all user terminals 10 included in the group (step S106). If the information processing device 100 continues the processing (step S107, Yes), it proceeds to step S101. On the other hand, if the information processing device 100 does not continue the processing (step S107, No), it ends the processing.

[0082] [5. Hardware Configuration] The user terminal 10 or the information processing device 100 according to the above-described embodiment is realized by a computer 80 configured as shown in Fig. 7, for example. The information processing device 100 will be described below as an example. Fig. 9 is a hardware configuration diagram showing an example of the computer 80 that realizes the functions of the information processing device 100 according to the embodiment. The computer 80 has a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, an HDD (Hard Disk Drive) 84, a communication interface (I / F) 85, an input / output interface (I / F) 86, and a media interface (I / F) 87.

[0083] The CPU 81 operates and controls each part based on programs stored in the ROM 83 or the HDD 84. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 starts up, programs that depend on the hardware of the computer 80, and the like.

[0084] The HDD 84 stores programs executed by the CPU 81, data used by such programs, etc. The communication interface 85 receives data from other devices via the network and sends it to the CPU 81, and transmits data generated by the CPU 81 to other devices via the network N.

[0085] The CPU 81 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 86. The CPU 81 acquires data from the input devices via the input / output interface 86. The CPU 81 also outputs generated data to the output devices via the input / output interface 86.

[0086] The media interface 87 reads a program or data stored in a recording medium 88 and provides it to the CPU 81 via the RAM 82. The CPU 81 loads the program or data from the recording medium 88 onto the RAM 82 via the media interface 87 and executes the loaded program. The recording medium 88 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0087] For example, when the computer 80 functions as the information processing device 100 according to the embodiment, the CPU 81 of the computer 80 executes programs loaded onto the RAM 82 to realize the functions of the control unit 150. In addition, the HDD 84 stores data in the storage unit 140. The CPU 81 of the computer 80 reads and executes these programs from the recording medium 88, but as another example, the CPU 81 may obtain these programs from another device via the network N.

[0088] [6. Other] 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. Furthermore, 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.

[0089] Furthermore, the components of each device shown in the figure are conceptual functional components and do 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.

[0090] For example, the information processing device 100 described above may be realized by multiple server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

[0091] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0092] For example, the sensor data is data including sensor values ​​such as illuminance, sound, position, atmospheric pressure, speed, and image, and the user terminal 10 generates vector data by inputting the sensor data to a trained machine learning model 15b, but which sensor value is to be emphasized is set during machine learning of the machine learning model 15b. For example, a machine learning model in which machine learning is performed focusing only on position and atmospheric pressure among sensor values ​​such as illuminance, sound, position, atmospheric pressure, speed, and image may be used.

[0093] Furthermore, although the information processing device 100 described above receives vector data from the user terminal 10, the information processing device 100 may hold a machine learning model and generate vector data from sensor data transmitted from the user terminal 10.

[0094] [7. Effects] As described above, the information processing device 200 according to the embodiment includes the acquisition unit 152, the identification unit 153, the determination unit 154, and the distribution unit 155. The acquisition unit 152 acquires a plurality of vectors corresponding to a plurality of users, each of which is generated using a machine learning model trained to generate similar vectors from sensor information of similar situations. The identification unit 153 identifies a user group having similar vectors based on the plurality of vectors. The determination unit 154 determines the content of information to be provided to the user group based on information on logged-in users among the users included in the user group identified by the identification unit 153. The distribution unit 155 distributes the determined information to the devices owned by each of the user group. This improves the accuracy of information provision.

[0095] Furthermore, when there are multiple logged-in users, the determination unit 154 identifies common information among the information of the multiple logged-in users and determines the content of the information to be provided to the user group based on the identified common information. This makes it possible to determine the information to be distributed even when multiple logged-in users are included in the same group.

[0096] The acquisition unit 152 also acquires sensor information measured by the terminal of the logged-in user, and the determination unit 154 further uses the acquired sensor information to determine the content of the information to be provided to the user group, thereby further improving the accuracy of the information provided.

[0097] Furthermore, the sensor information acquired by the acquisition unit 152 includes at least one sensor value from among illuminance, sound, position, atmospheric pressure, speed, and image, and the determination unit 154 determines the content of information to be provided to the user group based on the sensor information acquired by the acquisition unit 152. This can further improve the accuracy of information provision.

[0098] Furthermore, the determination unit 154 further uses the time information to determine the content of the information to be provided to the user group, thereby making it possible to provide appropriate information according to the time period.

[0099] The above describes the embodiments of the present application in detail based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art.

[0100] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, an acquisition unit can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0101] 10 User terminal 11,110 Communications Department 12,120 Input section 13,130 Display section 14 Sensors 15,140 storage unit 15a Sensor Data 15b Machine Learning Models 16,150 Control unit 16a Access section 16b Generator 16c Transmitter 16d Display processing section 100 Information processing device 105 Timer 141 Decision Table 142 User Management Table 143 Vector Table 151 Service Provision Department 152 Acquisition Department 153 Specific section 154 Decision Section 155 Distribution Department

Claims

1. an acquisition unit that acquires a plurality of vectors corresponding to a plurality of users, the vectors being generated using a machine learning model trained to generate similar vectors from sensor information of similar situations; an identification unit that identifies a group of users having similar vectors based on the plurality of vectors; a determination unit that determines the content of information to be provided to the user group based on information of logged-in users among the users included in the user group identified by the identification unit; a distribution unit that distributes information about the determined content to terminals owned by each of the user group; An information processing device comprising:

2. The information processing device according to claim 1, characterized in that, when there are multiple logged-in users, the determination unit identifies common information among the information of the multiple logged-in users, and determines the content of the information to be provided to the group of users based on the identified common information.

3. The information processing device according to claim 1, characterized in that the acquisition unit further acquires sensor information measured by the terminal of the logged-in user, and the determination unit further uses the acquired sensor information to determine the content of the information to be provided to the user group.

4. The information processing device described in claim 3, characterized in that the sensor information acquired by the acquisition unit includes at least one sensor value of illuminance, sound, position, air pressure, speed, and image, and the determination unit determines the content of the information to be provided to the user group based on the sensor information acquired by the acquisition unit.

5. 2. The information processing apparatus according to claim 1, wherein the determination unit further uses time information to determine the content of the information to be provided to the user group.

6. 1. A computer-implemented distribution method comprising: an acquisition step of acquiring a plurality of vectors corresponding to a plurality of users, the vectors being generated using a machine learning model trained to generate similar vectors from sensor information of similar situations; an identification step of identifying a group of users having similar vectors based on the plurality of vectors; a determination step of determining the content of information to be provided to the user group based on information of logged-in users among the users included in the user group identified in the identification step; a distribution step of distributing information on the determined content to terminals owned by each of the user group. A distribution method characterized by:

7. an acquisition step of acquiring a plurality of vectors corresponding to a plurality of users, the vectors being generated using a machine learning model trained to generate similar vectors from sensor information of similar situations; an identification step of identifying a group of users having similar vectors based on the plurality of vectors; a determination step of determining the content of information to be provided to the user group based on information of logged-in users among the users included in the user group identified in the identification step; a distribution procedure for distributing the information of the determined content to the terminals possessed by each of the user group, and causing the computer to execute the procedure. A distribution program characterized by:

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