Information processing device, distribution method and distribution program

Abstract

To improve accuracy of information provision.SOLUTION: An 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 a plurality of vectors corresponding to a plurality of users, 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 contents of information to be provided to the user group based on information on logged-in users among users included in the user group identified by the identification unit 153. The distribution unit 155 distributes the information on the determined contents to terminals owned by each user of the user group.SELECTED DRAWING: Figure 5

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 who visit each region for the purpose of guiding them to tourist spots, promoting products, etc. For example, as a technology for providing information to people who visit a certain region, there is a conventional technology that performs machine learning of a machine learning model based on learning data that defines in advance the relationship between the tendency of the person's context (riding a train, running a marathon, etc.) and the information to be provided to the person, and uses 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] JP 2020-154864 A 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 consideration of the above, and has an object 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 decision unit, and a distribution unit. The acquisition unit acquires a plurality of vectors corresponding to a plurality of users, which are 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 plurality of vectors. The decision unit 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. The distribution unit distributes the information of the determined content to the terminals owned by each of the user group. Effect of the Invention

[0007] According to one aspect of the embodiment, an effect is achieved in that the accuracy of information provision can be improved. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing the configuration of an information processing system according to this embodiment. [Diagram 2] FIG. 2 is a diagram for explaining the processing of the information processing device according to the present embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of a data structure of the determination table. [Figure 4] FIG. 4 is a diagram showing the configuration of a user terminal according to this embodiment. [Diagram 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 a data structure of the user management table. [Figure 7] FIG. 7 is a diagram illustrating an example of a 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 showing an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the information processing device, the distribution method, and the 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, the distribution method, and the distribution program according to the present application are not limited to these embodiments. In addition, each embodiment can be appropriately combined as long as the processing contents are not contradictory. In addition, the same parts in each of the following embodiments are given the same reference numerals, and duplicated 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 has 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 be included. In the following description, when the user terminals 10a to 10d are not particularly distinguished from each other, the user terminals 10a to 10d are collectively referred to as "user terminal 10." The users 3a to 3d are 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 the various sensors. The sensor data is data in which time is associated with various sensor values. For example, the various sensor values ​​include illuminance, sound, position, air pressure, speed, images, etc.

[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 multiple vector data output when multiple sensor data generated under similar circumstances are input are similar to each other. For example, when 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 vectors that are similar to each other.

[0016] Furthermore, when user 3c and user 3d are in the same area (an airport) and are performing similar actions (for example, pulling a carrying 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 vectors that are similar to each other.

[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 for identifying the user terminal 10 is added to the vector data. In the following description, information for uniquely identifying the user terminal 10 is 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-mentioned 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 a user terminal 10 that has accepted a login. The information processing device 100 also acquires vector data in real time from all user terminals 10 that exist in a pre-specified area, 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 determined information content to the user terminal 10. An example of the processing of the information processing device 100 will be described below.

[0020] Fig. 2 is a diagram for explaining the processing of the information processing device 100 according to this embodiment. In Fig. 2, as an example, vector data acquired by the information processing device 100 from the user terminals 10 included in the specified area are 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, the term "user corresponding to vector data" refers to a user 3 who uses the user terminal 10 that is the source of the vector data. For example, if the user terminal 10 that is the source of the vector data v1a is the user terminal 10a shown in FIG. 1, the user 3 of the vector data v1a is the user 3a.

[0022] The information processing device 100 classifies similar vector data into the same group by performing clustering or the like on a plurality of 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 the vector data v1a-v1g included in group G1, the users corresponding to the vector data v1a-v1c are assumed to be logged-in users (hereinafter, appropriately, referred to as logged-in users). Among the multiple users corresponding to the vector data v2a-v2h included in group G2, the users corresponding to the vector data v2b, v2e, and v2g are assumed to be logged-in users. Among the multiple users corresponding to the vector data v3a-v3f included in group G3, the user corresponding to the vector data v3a is assumed to be 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-v1g included in the group G1 based on the attribute data of the logged-in user corresponding to the vector data v1a-v1c. In the following description, the user terminals 10 of the multiple users corresponding to the vector data v1a-v1g included in the group G1 are referred to as "user terminals 10 of 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 user 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 are 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-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-v3f included in group G3 are 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 described below. Fig. 3 is a diagram showing an example of the data structure of the decision table. As shown in Fig. 3, the 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 zone is the current time zone 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 an average value, median value, or the like of sensor data acquired from the user terminal 10 of each logged-in user.

[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 included in "9:00 a.m. to 5:00 p.m." 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 in group G1 to be "advertising data for gym," and distributes the "advertising data for gym" to all user terminals 10 in group G1.

[0031] For example, it is assumed that the "annual income" included in the attribute data of the logged-in users in group G2 is "above threshold value," and the time period acquired from the timer is "9:00 AM to 5:00 PM." It is also assumed that the sensor data of the user terminal 10 used by the logged-in users 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 in group G2 to be "beauty advertisement data," and distributes the "beauty advertisement data" to all user terminals 10 in group G2.

[0032] For example, it is assumed that the "annual income" included in the attribute data of the logged-in users included in group G3 is "above threshold value," and the time period acquired from the timer is "9:00 a.m. to 5:00 p.m." Furthermore, it is assumed that the sensor data of the user terminal 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 the content of the information to be distributed to the user terminals 10 in group G3 to be "advertising data for cruise ships," and distributes the "advertising data for cruise ships" to all user terminals 10 in 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 attribute data of a logged-in user among multiple users included in the same group, and distributes the determined distribution data. This can improve 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 described later uses the communication unit 11 to transmit and receive information to and from the information processing device 100.

[0036] The input unit 12 is a device for inputting various 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, a touch panel, etc. The user 3 operates the input unit 12 to access the information processing device 100 and perform a login operation or the like.

[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 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. The various sensor values ​​include illuminance, sound, position, air pressure, speed, images, and the like.

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

[0042] The control unit 16 is a controller, and is realized, for example, 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) inside the user terminal 10 using a RAM as a working area. The control unit 16 is also a controller, and is realized, for example, 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 may be other configurations 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, a password, terminal identification data, etc., which 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 the terminal identification data to the vector data generated by the generation unit 16b, and transmits the vector data with the terminal identification data assigned to it to the information processing device 100. In the following description, it is assumed that the terminal identification data is assigned to the vector data, although this will not be described in detail. The transmission unit 16c transmits the vector data to the information processing device 100, regardless of whether or not the user has logged in to the information processing device 100.

[0049] Furthermore, the transmission unit 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 transmission unit 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 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, advertisement 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 has 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 by wire or wirelessly, 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. 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 explained in FIG.

[0058] User management table 142 is a table that holds various information related to users. Fig. 6 is a diagram showing an example of the data structure of user management table 142. As shown in Fig. 6, user management table 142 associates user IDs, login flags, terminal identification data, names, 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 a login operation, and is 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, and hobbies. In the example shown in FIG. 6, occupation, annual income, and hobbies are shown as attribute data, but the attribute data may include other attributes.

[0060] Each data in the user management table 142 is registered in advance by a user. Note that the user may operate the user terminal 10 to access the information processing device 100 and update his / her own data (terminal identification data, name, attribute data) as appropriate.

[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 other than the vector table 143.

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

[0064] The control unit 150 has a service providing unit 151, an acquiring unit 152, a specifying unit 153, a deciding unit 154, and a distributing unit 155, and realizes or executes the functions and actions of the processes described below. Note that the internal configuration of the control unit 150 is not limited to the configuration shown in Fig. 5, and may be other configurations 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 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 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 classifies similar vector data into the same group by performing clustering on all vector data registered in the vector table 143. 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 in FIG. 2. The identification unit 153 outputs the clustering result to the determination unit 154.

[0068] In the clustering result, terminal identification data corresponding to the vectors included in each group is set for each group. 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 terminals 10 (users 3) classified into the same group based on the attribute data of the logged-in users of the user terminals 10 classified into the same group. An example of the process 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 user 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 user 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 data to be delivered based on the acquired current time, the attribute data corresponding to the logged-in user of group G1, the vector data, and the determination table 141. For example, when 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 data to be "gym advertisement data."

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

[0074] The determination unit 154 repeatedly executes 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 an average value, a median value, or the like of sensor data acquired from the user terminal 10 of each logged-in user.

[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 a 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 of 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 executes 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 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 process (step S107, Yes), it proceeds to step S101. On the other hand, if the information processing device 100 does not continue the process (step S107, No), it ends the process.

[0082] [5. Hardware Configuration] The user terminal 10 or the information processing device 100 according to the embodiment described above is realized by a computer 80 having a configuration 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 a 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, a 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 a program 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 is started 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 data generated via the input / output interface 86 to the output devices.

[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 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 a program loaded on the RAM 82 to realize the function 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, these programs may be obtained from another device via the network N.

[0088] [6. Other] In addition, 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 by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0089] 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 according to 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 functions, the configuration can be flexibly changed by calling an external platform using an API (Application Programming Interface) or network computing.

[0091] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0092] For example, the sensor data is data including sensor values ​​such as illuminance, sound, position, air 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 the position and air pressure among the sensor values ​​such as illuminance, sound, position, air pressure, speed, and image may be used.

[0093] Furthermore, while the above-described information processing device 100 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 decision unit 154, and the distribution unit 155. The acquisition unit 152 acquires a plurality of vectors corresponding to a plurality of users, which are 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 decision unit 154 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 153. The distribution unit 155 distributes the determined information to the terminals owned by each of the user group. This can improve 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 acquiring unit 152 further acquires sensor information measured by the terminal of the logged-in user, and the deciding unit 154 further uses the acquired sensor information to decide the content of the information to be provided to the user group. This can further improve the accuracy of the information provided.

[0097] The sensor information acquired by the acquisition unit 152 includes at least one sensor value from among illuminance, sound, position, air 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 embodiment 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 various modifications and improvements based on the knowledge of those skilled in the art, including the forms described in the Disclosure of the Invention section.

[0100] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, an acquisition section 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 Sensor Group 15,140 storage section 15a Sensor Data 15b Machine Learning Models 16,150 Control section 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 part 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 user group having similar vectors based on the plurality of vectors; a determination unit that determines 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; 13. 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 according to 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. The information processing apparatus according to claim 1 , wherein the determination unit determines the content of the information to be provided to the user group by further using time information.

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; A 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 comprising:

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; A step of identifying a group of users having similar vectors based on the plurality of vectors; a determination step of determining contents 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 on the determined content to the terminals possessed by each of the user group. A distribution program characterized by:

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