Machine learning device, data processing device, inference device, machine learning method, data processing method, and inference method

Abstract

To provide a machine learning device capable of appropriately grasping, from an action of a person, a factor serving as an incentive causing the action to be taken by the person.SOLUTION: A machine learning device 3 comprises a machine learning unit 301 and a learned model storage unit 31. The machine learning unit causes a learning model 12 to learn a correlation between person data and behavioral factor indexes by machine learning by inputting, into the learning model 12, a plurality of sets of learning data 11 composed of person data recording behaviors of persons on the basis of one or more standpoints and behavioral factor indexes indicating factors serving as incentives causing behaviors of persons. The learned model storage unit stores the learning mode 12 caused to learn the correlation by the machine learning unit 301.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a machine learning device, a data processing device, an inference device, a machine learning method, a data processing method, and an inference method. [Background technology]

[0002] Conventionally, by using personal information of a user to provide information tailored to the user, convenience for the user and usefulness for the information provider have been improved. For example, Patent Documents 1 and 2 disclose systems that deliver advertisement information to a user's terminal based on personal information including the user's name, sex, age, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-211851 A [Patent Document 2] JP 2002-366843 A Summary of the Invention [Problem to be solved by the invention]

[0004] The systems disclosed in Patent Documents 1 and 2 are premised on personal information being input by each user, and therefore cannot provide appropriate information to users for whom personal information has not been input. In addition, when a user takes some kind of action, it is considered that the user has inherent behavioral factor indices (e.g., sensibility, etc.) that indicate factors that motivate the user to take that action. However, in the systems disclosed in Patent Documents 1 and 2, such behavioral factor indices are not input as personal information, and therefore it is not possible to provide appropriate information according to the behavioral factor indices inherent in each user.

[0005] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a machine learning device, a data processing device, an inference device, a machine learning method, a data processing method, and an inference method for appropriately grasping the factors that motivate a person to take action from that person's behavior. [Means for solving the problem]

[0006] In order to achieve the above object, a machine learning device according to one aspect of the present invention comprises: a machine learning unit that inputs a plurality of sets of learning data, each set being composed of person data that records a person's behavior based on one or more viewpoints and a behavioral factor index that indicates a factor that motivates the person to perform the behavior, into a learning model, and causes the learning model to learn a correlation between the person data and the behavioral factor index by machine learning; and a learned model memory unit that stores the learned model in which the correlation has been learned by the machine learning unit. Effect of the Invention

[0007] According to a machine learning device according to an embodiment of the present invention, a learning model can be provided that can estimate a behavioral factor index indicating a factor that motivates a person to take action, based on person data that records the person's behavior based on one or more viewpoints. Therefore, by using this learning model, it is possible to appropriately assign (infer) a behavioral factor index inherent to a person (user) without requesting the person (user) to input personal information, for example.

[0008] Other objects, configurations and effects will become apparent from the detailed description of the invention described below. can be. [Brief description of the drawings]

[0009] [Figure 1] 1 is an overall configuration diagram showing an example of a person information management system 1. FIG. [Diagram 2] FIG. 9 is a hardware configuration diagram showing an example of a computer 900. [Diagram 3]FIG. 2 is a data configuration diagram showing an example of a person database 20. [Figure 4] FIG. 2 is a block diagram showing an example of a machine learning device 3. [Diagram 5] FIG. 3 is a diagram showing an example of determination rule data 310. [Figure 6] 2 is a diagram showing an example of learning data 11 and a learning model 12. FIG. [Figure 7] FIG. 3 is a functional explanatory diagram showing an example of a machine learning unit 301. [Figure 8] FIG. 2 is a block diagram showing an example of a data processing device 4. [Figure 9] FIG. 2 is a functional explanatory diagram showing an example of a data processing device 4. [Figure 10] 4 is a flowchart showing an example of a machine learning method performed by the machine learning device 3. [Figure 11] 13 is a flowchart showing an example of a machine learning method by the machine learning device 3 (continuation of FIG. 10). [Figure 12] 13 is a flowchart showing an example of a data processing method performed by the data processing device 4. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment for carrying out the present invention will be described with reference to the drawings. In the following, the scope necessary for the explanation to achieve the object of the present invention will be shown in a schematic manner, and the scope necessary for the explanation of the relevant part of the present invention will be mainly explained, and the parts that are omitted will be explained as being related to the publicly known technology.

[0011] (Personal Information Management System 1) 1 is an overall configuration diagram showing an example of a person information management system 1. The person information management system 1 functions as a system that can provide each person's behavior factor index to various services (product planning, product development, production management, purchasing / order management, sales strategy formulation, marketing, advertising, etc.) by adding behavior factor indexes (tags) that indicate factors that motivate a person to take action to person data that records the person's actions.

[0012] The person data is data that records the behavior of a person based on one or more viewpoints. Examples of viewpoints for recording the behavior of a person include a purchasing behavior when a person purchases a product or service at a store or an e-commerce site, a web browsing behavior when a person browses a website, a web search behavior when a person searches a website, a travel behavior when a person travels by any means of travel (walking, train, car, etc.), and a survey response behavior when a person responds to a survey on a website or in paper. Therefore, the person data includes, for example, a purchase history that records a purchasing behavior, a web browsing history that records a web browsing behavior, a web search history that records a web search behavior, a travel history that records a travel behavior, and a survey response history that records a survey response behavior.

[0013] The behavioral factor index indicates factors that motivate a person to take a particular action. The behavioral factor index has a plurality of types classified by, for example, sensibility, preference, attribute, etc. Examples of sensibility and preference include health consciousness, organic, particular about the place of origin, price (savings) consciousness, luxury consciousness, safety and security, high sensitivity to beauty, cleanliness, Japanese food, Western food, sweet tooth, spicy food, strong taste, sour taste, carbonated food, indoor, outdoor, etc. Examples of attributes related to demographics and lifestyle include age, sex, single, married, with children, own home, rent, own car, keep pets, on a diet, etc.

[0014] The behavioral factor index is assigned to the person data according to the value of the variable, and the variable value is assigned for each type of behavioral factor index. In this embodiment, the case where the behavioral factor index of "health-conscious" is defined by a two-class classification (binary classification of 0 or 1) will be mainly described, and therefore the variable value of the behavioral factor index of "health-conscious" is assigned by "0: not health-conscious" and "1: health-conscious". Note that the behavioral factor index may be defined by a multi-class classification (multi-value classification), and for example, when it is defined by a three-class classification, the variable value of the behavioral factor index of "health-conscious" may be assigned by "-1: not health-conscious", "0: neither", and "1: health-conscious". The definition of the behavioral factor index may be different for each type of behavioral factor index.

[0015] The main components of the person information management system 1 include a database device 2, a machine learning device 3, a data processing device 4, and an operator terminal device 5. Each of the devices 2 to 5 is configured, for example, as a general-purpose or dedicated computer (see FIG. 2 described later), and is connected to a wired or wireless network 6 so as to be able to transmit and receive various data to and from each other. Note that the number of the devices 2 to 5 and the connection configuration of the network 6 are not limited to the example in FIG. 1, and may be changed as appropriate.

[0016] The database device 2 cooperates with an external system (not shown) that records the daily behavior of each person as a behavior history based on one or more viewpoints, and includes a person database 20 that can register the behavior history based on one or more viewpoints as person data for each person. The external system is composed of, for example, a point of sale information management system (POS system), a computer operation log collection system, a navigation system, a questionnaire system, etc. The external system records purchase history, web browsing history, web search history, movement history, questionnaire response history, etc. according to the daily behavior of each person as behavior history, and provides the same to the database device 2 in response to a request from the database device 2, for example.

[0017] The machine learning device 3 operates as a subject of the learning phase of machine learning, and performs machine learning of a learning model 12 by using person data registered in a person database 20 as learning data 11. The learned learning model 12 is provided to the data processing device 4 via a network 6, a recording medium, or the like. As the machine learning method, supervised learning is adopted.

[0018] The data processing device 4 operates as the subject of the inference phase of machine learning, and assigns behavioral factor indices to the person data using the trained learning model 12 generated by the machine learning device 3, and provides the assignment results (in this embodiment, the variable values ​​for each type of behavioral factor index) to the database device 2, the worker terminal device 5, etc.

[0019] The worker terminal device 5 is a terminal device used by a worker 10 who performs annotation work on the learning data 11 when performing machine learning of the learning model 12. The worker terminal device 5 accepts various input operations via a display screen such as an application program or a web browser, and displays various information (e.g., person data, behavioral factor indexes, etc.) via the display screen.

[0020] (Computer 900) 2 is a hardware configuration diagram showing an example of the computer 900. Each of the devices 2 to 5 of the person information management system 1 is configured by a general-purpose or dedicated computer 900.

[0021] As shown in FIG. 2, the computer 900 includes, as its main components, a bus 910, a processor 912, a memory 914, an input device 916, an output device 917, a display device 918, a storage device 920, a communication I / F (interface) unit 922, an external device I / F unit 924, an I / O (input / output) device I / F unit 926, and a media input / output unit. 928. Note that the above components may be omitted as appropriate depending on the application of the computer 900.

[0022] The processor 912 is composed of one or more arithmetic processing devices (such as a central processing unit (CPU), a micro-processing unit (MPU), a digital signal processor (DSP), or a graphics processing unit (GPU)) and operates as a control unit that controls the entire computer 900. The memory 914 stores various data and programs 930, and is composed of, for example, a volatile memory (DRAM, SRAM, etc.) that functions as a main memory, a non-volatile memory (ROM), a flash memory, etc.

[0023] The input device 916 is composed of, for example, a keyboard, a mouse, a numeric keypad, an electronic pen, etc., and functions as an input unit. The output device 917 is composed of, for example, a sound (audio) output device, a vibration device, etc., and functions as an output unit. The display device 918 is composed of, for example, a liquid crystal display, an organic EL display, electronic paper, a projector, etc., and functions as an output unit. The input device 916 and the display device 918 may be integrally configured, such as a touch panel display. The storage device 920 is composed of, for example, a hard disk drive (HDD), a solid state drive (SSD), etc., and functions as a storage unit. The storage device 920 stores various data necessary for the execution of the operating system and the program 930.

[0024] The communication I / F unit 922 is connected to a network 940 (which may be the same as the network 6 in FIG. 1) such as the Internet or an intranet by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from other computers according to a predetermined communication standard. The external device I / F unit 924 is connected to an external device 950 such as a camera, a printer, a scanner, a reader / writer by wire or wirelessly, and functions as a communication unit that transmits and receives data to and from the external device 950 according to a predetermined communication standard. The I / O device I / F unit 926 is connected to an I / O device 960 such as various sensors and actuators, and functions as a communication unit that transmits and receives various signals and data, such as detection signals from sensors and control signals to actuators, between the I / O device 960. The media input / output unit 928 is composed of a drive device such as a DVD (Digital Versatile Disc) drive or a CD (Compact Disc) drive, and reads and writes data from and to a medium (non-temporary storage medium) 970 such as a DVD or CD.

[0025] In the computer 900 having the above configuration, the processor 912 calls up the program 930 stored in the storage device 920 into the memory 914, executes the program, and controls each unit of the computer 900 via the bus 910. The program 930 may be stored in the memory 914 instead of the storage device 920. The program 930 may be recorded in the medium 970 in an installable file format or an executable file format, and provided to the computer 900 via the media input / output unit 928. The program 930 may be provided to the computer 900 by downloading it via the network 940 via the communication I / F unit 922. In addition, the computer 900 may realize various functions realized by the processor 912 executing the program 930, for example, with hardware such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC).

[0026] The computer 900 may be, for example, a desktop computer or a portable computer, and may be any type of electronic device. The computer 900 may be a client computer, a server computer, or a cloud computer.

[0027] (People Database 20) 3 is a data configuration diagram showing an example of the person database 20. The person database 20 is a database for managing person data based on person identification information (user ID) assigned to each person. The person database 20 is composed of, for example, a purchase history table, a web browsing history table, a web search history table, a movement history table, a questionnaire response history table, a behavioral factor index table, product master information, store master information, website master information, and map information.

[0028] The purchase history table has a number of records for recording purchasing behavior, and each record registers a date and time, a product, a price, a store (which may be an e-commerce site), and the like. A product ID is registered for a product, and the product is linked to product master information by the product ID, thereby associating the product with product attributes (product name, product category, etc.) registered in the product master information. A store ID is registered for a store, and the store is linked to store master information by the store product ID, thereby associating the store with store attributes (store name, store category, etc.) registered in the store master information. A product may be assigned a product index (behavioral characteristic index) corresponding to the behavior factor index, and may be registered in the product master information as a part of the product attributes, for example. A store may be assigned a store index (behavioral characteristic index) corresponding to the behavior factor index, and may be registered in the store master information as a part of the store attributes, for example.

[0029] The web browsing history table has a plurality of records for recording web browsing behavior, and each record is registered with a date and time, a website, a browsing time, etc. The web search history table has a plurality of records for recording web search behavior, and each record is registered with a date and time, a search word, etc. A URL for accessing the website is registered with the website, and the URL is linked to the website master information, thereby associating the website with the website attributes (administrator name, website category, etc.) registered in the website master information. Note that the website may be assigned a website index (behavioral feature index) corresponding to the behavior factor index, and may be registered in the website master information as a part of the website attributes, for example.

[0030] The movement history table has a number of records for recording movement behavior, and each record registers date and time, location, means of transportation, etc. For example, a stay point or area where a user stays for a predetermined time or longer is registered as a location, and the stay point or area is linked to map information, thereby associating the location attributes (location name, location category, etc.) and area attributes (area name, area category, etc.) registered in the map information. Note that the location or area may be assigned a location index (behavioral characteristic index) or area index (behavioral characteristic index) corresponding to the behavioral factor index, and may be registered in the map information as a part of the location attribute or area attribute, for example.

[0031] The survey response history table has a plurality of records for recording survey response behavior, and each record registers the date and time, question content, response content, etc.

[0032] The behavioral factor index table has a plurality of records for storing the behavioral factor indexes assigned by the data processing device 4, and each record registers the date and time, the variable value of the behavioral factor index, etc. In the example of Fig. 3, three fields corresponding to three behavioral factor indexes, "health-conscious", "sweet tooth", and "car ownership", are shown, but similar fields are provided for the other behavioral factor indexes, respectively.

[0033] In the person database 20, the information in each table is related by user ID. In this way, person data that records a person's behavior based on multiple viewpoints and behavior factor indexes are managed for each person. The information registered in the person database 20 is configured to be able to perform various statistical processes. The person data may be recorded in association with feature data (in this embodiment, product, store, website, location, and region) to which a behavior feature index (in this embodiment, product index, store index, website index, location index, and region index) that indicates the characteristics of the behavior and corresponds to the behavior factor index is assigned. In addition to the above, the behavior feature index may also be assigned to features such as time period, day of the week, and season. The data configuration of the person database 20 may be changed as appropriate, and data other than the above may be registered, or some of the above data may be omitted.

[0034] (Machine learning device 3) 4 is a block diagram showing an example of a machine learning device 3. The machine learning device 3 includes a control unit 30 configured with a processor or the like, a storage unit 31 configured with an HDD, an SSD, a memory or the like, a communication unit 32 which is a communication interface with the network 6, an input unit 33 configured with a keyboard, a mouse or the like, and a display unit 34 configured with a display or the like. Note that the input unit 33 and the display unit 34 may be omitted.

[0035] The storage unit 31 stores the learning data 11, the learning model 12, the decision rule data 310, and the machine learning program 311, as well as an operating system, other programs, various data, etc. The storage unit 31 functions as a learning data storage unit that stores the learning data 11, and a trained model storage unit that stores the learning model 12.

[0036] 5 is a diagram showing an example of the determination rule data 310. In the determination rule data 310, a determination rule for determining whether or not a person matches a behavior factor index is registered for each type of behavior factor index. The determination rule data 310 is information that can be edited by the worker 10 via the worker terminal device 5.

[0037] The judgment rule is defined for the person data based on at least a part of the viewpoint, and is defined by appropriately combining various numerical calculations with, for example, comparison, ratio, frequency, period, category, behavioral characteristic index (product index, store index, website index, location index, region index), etc. In addition, the judgment rule is defined for each type of behavioral factor index, and in the example of FIG. 5, judgment rules for three behavioral factor indexes, "health-conscious," "sweet tooth," and "car ownership," are defined for "purchase history" based on the viewpoint of "purchase behavior." For example, in the judgment rule for the behavioral factor index of "health-conscious," the purchase amount of each user when purchasing a product with a product category of "health food" or a product index of "health-conscious" in the past year is tallied, and if the total purchase amount is X yen or more, the behavioral factor index of "1: health-conscious" is assigned to the user.

[0038] 5, as the results of the determination according to the determination rules, the personal data of "user A" is assigned the behavioral factor indices "1: health-conscious", "0: not sweet tooth", and "1: car owner", the personal data of "user B" is assigned the behavioral factor indices "0: not health-conscious", "0: not sweet tooth", and "1: car owner", and the personal data of "user C" is assigned the behavioral factor indices "1: sweet tooth", and "0: not car owner". In addition, for the personal data of "user C", "N / A: cannot be determined" is recorded as the result of the determination according to the determination rules for "health-consciousness" because there is not enough purchase history recorded for determining according to the determination rules for "health-consciousness".

[0039] The judgment rule may be defined for person data based on a plurality of viewpoints, for example, for "purchase history", "travel behavior" and "survey response history" based on a plurality of viewpoints, namely "purchase behavior", "travel behavior" and "survey response behavior". Judgment rules may be defined for person data based on different perspectives for each type of behavioral factor indicator. For example, judgment rules for "health consciousness" may be defined for "purchase history" based on "purchase behavior", and judgment rules for "car ownership" may be defined for "travel history" based on "travel behavior".

[0040] As shown in FIG. 4, the control unit 30 executes a machine learning program 311 stored in the storage unit 41, thereby functioning as a data acquisition unit 300 and a machine learning unit 301.

[0041] The data acquisition unit 300 receives a request from, for example, the machine learning unit 301 or the worker terminal device 5, and accesses the person database 20 via the communication unit 32 and the network 6. The data acquisition unit 300 acquires person data for each person from the person database 20, in which the actions of each of a plurality of people are recorded, and provides the person data to the machine learning unit 301 or stores the person data in the storage unit 31.

[0042] The machine learning unit 301 generates learning data 11 composed of person data and behavioral factor indices, and inputs a plurality of sets of the data to the learning model 12, thereby making the learning model 12 learn the correlation between the person data and the behavioral factor indices by machine learning. Then, the machine learning unit 301 stores the learned learning model 12 (specifically, a group of adjusted parameters) in the storage unit 31.

[0043] When inputting the person data constituting the learning data 11a to the input layer 120 of the learning model 12a, the machine learning unit 301 may perform pre-processing by any conversion method such as one-hot encoding or label encoding to convert the person data into predetermined features for inputting to the learning model 12. In this case, the pre-processing may be, for example, converting the person data into features by appropriately combining various numerical calculations with comparison, ratio, frequency, period, category, behavioral feature index (product index, store index, website index, location index, region index), etc. for the purchase history, web browsing history, web search history, movement history, and questionnaire response history included in the person data.

[0044] In particular, when the person data is recorded in association with feature data (product, store, website, location, region) to which behavioral feature indexes (product index, store index, website index, location index, region index) are assigned as shown in FIG. 3, the machine learning unit 301 may perform preprocessing to convert the person data into feature amounts based on the behavioral feature indexes assigned to the feature data included in the person data, and input a plurality of sets of preprocessed learning data 11 consisting of the feature amounts converted from the person data in the preprocessing and the behavioral factor indexes included in the person data to the learning model 12, thereby making the learning model 12 learn the correlation between the person data and the behavioral factor indexes. In this case, the preprocessing may be, for example, based on the product indexes assigned to the products previously purchased by the user, which are recorded as part of the purchase history, by tallying up the purchase amount of the product to which the behavioral factor index of "health conscious" is assigned, the purchase amount of the product to which the behavioral factor index of "sweet tooth" is assigned, the purchase amount of the product to which the behavioral factor index of "car ownership" is assigned, and the like, and converting the tally up purchase amounts into feature amounts by normalizing each of the tally up purchase amounts to a value in the range of 0 to 1.

[0045] 6 is a diagram showing an example of the learning data 11 and the learning model 12. The learning data 11 used for machine learning of the learning model 12 is composed of person data and behavioral factor indices. The learning data 11 is data used as teacher data (training data), verification data, and test data in supervised learning. In addition, the behavioral factor indices are data used as correct answer labels in supervised learning.

[0046] The person data constituting the learning data 11 is acquired from the person database 20. The behavior factor indexes constituting the learning data 11 are determined based on the judgment rules registered in the judgment rule data 310. The mark may be given based on the result of the determination by the user 10 or may be given through annotation work by the user 10.

[0047] The learning model 12 employs, for example, a neural network structure, and includes an input layer 120, an intermediate layer 121, and an output layer 122. Synapses (not shown) that connect each neuron are laid between each layer, and each synapse is associated with a weight.

[0048] The input layer 120 has neurons in a number corresponding to person data (which may be feature amounts converted from person data in preprocessing) as input data, and each value of the person data is input to each neuron. The output layer 122 has neurons corresponding to behavior factor indices as output data, and the result of assigning the behavior factor indices to the person data (inference result) is output as output data (a value in the range of 0 to 1 in this embodiment). The behavior factor indices (correct answer labels) constituting the learning data 11 are compared with the behavior factor indices (inference result) output from the output layer 122, and a process (backpropagation, etc.) is performed to adjust a group of parameters such as the weights of each synapse, thereby performing machine learning of the learning model 12.

[0049] In this embodiment, a case will be described in which the machine learning unit 301 performs machine learning of the learning model 12 for each type of behavioral factor index, and therefore the storage unit 31 stores a plurality of learning models 12 corresponding to each type of behavioral factor index. The learning model 12 shown in FIG. 6 outputs a behavioral factor index of "health-oriented" for person data, and machine learning is performed using learning data 11 consisting of person data and the behavioral factor index of "health-oriented". The learning model 12 may output a plurality of behavioral factor indexes for person data, and may output three behavioral factor indexes, "health-oriented", "sweet tooth", and "car ownership", for example, for person data. In that case, machine learning may be performed using the learning data 11 consisting of person data and the behavioral factor indexes of "health-oriented", "sweet tooth", and "car ownership".

[0050] The number and types of learning models 12 stored in the storage unit 31 after machine learning is performed by the machine learning unit 301 may be changed as appropriate, and multiple learning models 12 with different conditions, such as a machine learning method, a type of data included in the person data, a type of data included in the behavioral factor index, a method of preprocessing the person data, etc. may be stored. In that case, multiple types of learning data 11 having data configurations corresponding to the multiple learning models 12 with different conditions may be used.

[0051] As shown in Figure 4, the machine learning unit 301 includes a first generation processing unit 301a, a first learning processing unit 301b, a second generation processing unit 301c, and a second learning processing unit 301d as units that perform machine learning of the learning model 12.

[0052] Fig. 7 is a functional explanatory diagram showing an example of the machine learning unit 301. Details of the machine learning unit 301 will be described below with reference to Fig. 7, the decision rule data 310 shown in Fig. 5, and the learning data 11 and the learning model 12 shown in Fig. 6.

[0053] The first generation processing unit 301a judges for each person whether or not the person whose behavior is recorded in the person data acquired by the data acquisition unit 300 matches the behavior factor index in accordance with the judgment rules registered in the judgment rule data 310. The first generation processing unit 301a generates learning data 11a by assigning a behavior factor index to the person data based on the judgment result.

[0054] In the example of FIG. 7, the first generation processing unit 301a judges whether each person (users A to C) meets the criteria of “health-consciousness” according to the judgment rules (see FIG. 5) defined for the purchase record, Based on the judgment result, the learning data 11a is generated by assigning a behavioral factor index (provisional answer label) of "1: health-conscious" to the personal data of "user A" and "0: not health-conscious" to the personal data of "user B." Note that for the personal data of "user C" who cannot be judged according to the judgment rule of "health-conscious" due to reasons such as insufficient recording of purchase history, a behavioral factor index (provisional answer label) is not assigned, and therefore the learning data 11a based on the personal data of "user C" is not generated.

[0055] The first learning processing unit 301b uses the learning data 11a generated by the first generation processing unit 301a to make the learning model 12a learn the correlation between the person data and the behavioral factor index. At this time, the person data constituting the learning data 11a includes not only the purchase history but also the web browsing history, the web search history, the movement history, and the questionnaire response history, as shown in Fig. 6. The first learning processing unit 301b may use the pre-processed learning data 11a to make the learning model 12a learn the correlation between the person data (feature amount) and the behavioral factor index.

[0056] The second generation processing unit 301c receives a correction instruction for the behavior factor index output from the learning model 12a by inputting person data (which may be a feature converted from the person data in preprocessing) to the learning model 12a in which correlations have been learned by the first learning processing unit 301b. Specifically, as described above, the second generation processing unit 301c displays the behavior factor index output from the learning model 12a for each person in association with the person data, and receives a correction instruction for the displayed behavior factor index by the worker 10 performing annotation work using the worker terminal device 5. Then, the second generation processing unit 301c generates learning data 11b by assigning the behavior factor index to the person data based on the reception result.

[0057] In the example of FIG. 7, the second generation processing unit 301c inputs the person data of each person (users A to C) into the learning model 12a, and outputs "0.9" for "user A", "0.6" for "user B", and "0.8" for "user C" as behavior factor indices (inference results). When the second generation processing unit 301c displays an evaluation list in which the behavior factor indices (inference results) output from the learning model 12a are associated with the person data on the worker terminal device 5, the worker 10 visually checks the person data of "user B" and, if the worker 10 determines that "1: health-conscious" is more appropriate than "0: not health-conscious" assigned as a provisional correct label by the first generation processing unit 301a, issues a correction instruction to assign "1: health-conscious" to "user B". Furthermore, if the worker 10 checks the personal data of "user C" and determines that "0: not health-conscious" is more appropriate than "1: health-conscious", the worker 10 issues a correction instruction to assign "0: not health-conscious" to "user C". Then, the second generation processing unit 301c receives these correction instructions and, based on the reception result, assigns "1: health-conscious" to the personal data of "user B" and "0: not health-conscious" to the personal data of "user C" as behavior factor indices (corrected correct labels), thereby generating the learning data 11b.

[0058] When performing machine learning of the learning model 12a corresponding to the preprocessed learning data 11a, the second generation processing unit 301c may input the feature amount (preprocessed person data) converted from the person data in the preprocessing to the learning model 12a in which the correlation has been learned by the first learning processing unit 301b, and receive a correction instruction for at least one of the behavior factor index output from the learning model 12a and the behavior feature index assigned to the feature data included in the person data. Specifically, as described above, the second generation processing unit 301c displays the behavior factor index output from the learning model 12a and the behavior feature index assigned to the feature data included in the person data for each person in association with the person data, and the worker 10 performs annotation work using the worker terminal device 5 to correct the displayed behavior factors. The second generation processing unit 301c receives an instruction to modify at least one of the cause index and the behavioral feature index. The second generation processing unit 301c may generate learning data 11b by adding the behavioral factor index to the person data based on the received instruction, and may also modify the behavioral feature index added to the feature data.

[0059] The second learning processing unit 301d uses at least the learning data 11b generated by the second generation processing unit 301c to make the learning model 12b learn the correlation between the person data and the behavioral factor index. At this time, the second learning processing unit 301d may further use not only the learning data 11b generated by the second generation processing unit 301c but also the learning data 11a generated by the first generation processing unit 301a (in the example of FIG. 7, the learning data 11a based on the person data of "user A"). Note that the second learning processing unit 301d may use the pre-processed learning data 11b to make the learning model 12b learn the correlation between the person data (feature amount) and the behavioral factor index.

[0060] In the machine learning unit 301, when the second learning processing unit 301d makes the learning model 12b learn the correlation between the person data and the behavior factor index, if the predetermined learning end condition is not satisfied, the second generation processing unit 301c may repeatedly execute a process of inputting person data to the learning model 12b made to learn by the second learning processing unit 301d instead of the first learning processing unit 301b, accepting a correction instruction for the behavior factor index output from the learning model 12b, and generating learning data 11b by adding the behavior factor index to the person data based on the acceptance result, and a process of the second learning processing unit 301d making the learning model 12b learn using at least the learning data 11b generated by the second generation processing unit 301c, until the learning end condition is satisfied. Whether the learning end condition is satisfied is determined, for example, according to whether the accuracy of the learning model 12 has reached a predetermined accuracy or whether the number of learning data 11 has reached a predetermined upper limit.

[0061] In addition, when performing machine learning of the learning model 12b corresponding to the preprocessed learning data 11b, if a predetermined learning end condition is not satisfied when the second learning processing unit 301d causes the learning model 12b to learn the correlation between the person data (feature amount) and the behavior factor index, the machine learning unit 301 causes the second generation processing unit 301c to input the feature amount (preprocessed person data) converted from the person data in the preprocessing to the learning model 12b trained by the second learning processing unit 301d instead of the first learning processing unit 301b, thereby generating the behavior factor index output from the learning model 12b. and the processing for correcting the behavioral feature index assigned to the feature data included in the person data, and generating learning data 11b by assigning a behavioral factor index to the person data based on the acceptance result, and correcting the behavioral feature index assigned to the feature data; and the processing for training a learning model 12b using at least the learning data 11b generated by the second generation processing unit 301c, which is the preprocessed learning data 11b, may be repeatedly executed until a learning end condition is satisfied.

[0062] (Data processing device 4) Fig. 8 is a block diagram showing an example of the data processing device 4. Fig. 9 is a functional explanatory diagram showing an example of the data processing device 4. The data processing device 4 includes a control unit 40 constituted by a processor or the like, a storage unit 41 constituted by an HDD, an SSD, a memory or the like, a communication unit 42 which is a communication interface with the network 6, an input unit 43 constituted by a keyboard, a mouse or the like, and a display unit 44 constituted by a display or the like. Note that the input unit 43 and the display unit 44 may be omitted.

[0063] The storage unit 41 stores the learning model 12 and the data processing program 410, as well as an operating system, other programs, various data, etc. 1 functions as a trained model storage unit that stores a trained learning model 12.

[0064] The control unit 40 executes a data processing program 410 stored in the storage unit 41 to function as a data acquisition unit 400, an index assignment unit 401, and an output processing unit 402.

[0065] The data acquiring unit 400, for example, receives a request from the index assigning unit 401 or the worker terminal device 5, and accesses the person database 20 via the communication unit 42 and the network 6. The data acquiring unit 400 acquires person data from the person database 20, provides the data to the index assigning unit 401, or stores the data in the storage unit 41.

[0066] The index assigning unit 401 inputs the person data (which may be a feature converted from the person data in preprocessing) acquired by the data acquiring unit 400 into the trained learning model 12 stored in the storage unit 41, and assigns the behavioral factor index (inference result) output from the learning model 12 to the person data. At that time, as post-processing of the behavioral factor index (inference result) output as output data (a value in the range of 0 to 1 in this embodiment) from the learning model 12, the index assigning unit 401 compares the value of the variable of the behavioral factor index ("0.9" in FIG. 9) with a predetermined threshold (for example, "0.5"), and assigns a behavioral factor index of "1: health-conscious" to the person data if the value is equal to or greater than the threshold, and assigns a behavioral factor index of "0: not health-conscious" to the person data if the value is less than the threshold. In addition, when inputting person data to the input layer 120 of the learning model 12, the index assignment unit 401 may perform preprocessing similar to that of the machine learning unit 301, so as to input the preprocessed person data, i.e., the features converted from the person data in the preprocessing, to the learning model 12.

[0067] In this embodiment, a case will be described in which the learning model 12 is generated for each type of behavioral factor index by the machine learning device 3, and therefore a plurality of learning models 12 corresponding to each type of behavioral factor index are stored in the storage unit 41. The learning model 12 shown in Fig. 9 outputs a behavioral factor index of "health-oriented" for person data. Therefore, the index assigning unit 401 assigns to the person data also behavioral factor indices other than "health-oriented" that are output by inputting the person data to the plurality of learning models 12 corresponding to each type of behavioral factor index, as in Fig. 9.

[0068] The output processing unit 402 performs output processing for outputting the behavior factor index assigned by the index assigning unit 401. The output processing unit 402 may provide the behavior factor index to the database device 2, for example, to register it in a behavior factor index table of the person database 20, or may provide it to the worker terminal device 5 to present it to the worker. In this case, the output processing unit 402 may output the behavior factor index (a value in the range of 0 to 1) before post-processing is performed in addition to or instead of the behavior factor index (0 or 1) after post-processing is performed by the index assigning unit 401.

[0069] The number of learning models 12 stored in the storage unit 41 is not limited to one, and multiple learning models 12 with different conditions, such as a machine learning method, a type of data included in the person data, a type of data included in the behavioral factor index, a method of preprocessing the person data, etc., may be stored and selectively available. The learning model 12 may be stored in a storage unit of an external computer (for example, a server-type computer or a cloud-type computer), and in that case, the data processing device 4 may access the external computer.

[0070] (Machine learning methods) 10 and 11 are flowcharts showing an example of a machine learning method performed by the machine learning device 3. In the following, a case will be described in which the worker 10 performs a series of learning instruction operations including annotation work using the worker terminal device 5, and the machine learning device 3 performs machine learning of the learning model 12 by referring to the person database 20 and the decision rule data 310.

[0071] First, in step S100, the worker terminal device 5 receives a learning instruction including a learning condition for performing machine learning of the learning model 12 from the worker 10, and then in step S101 transmits the learning instruction to the machine learning device 3. As the learning condition, for example, a person data acquisition condition when acquiring person data for generating the learning data 11 from the person database 20 and a learning end condition are specified.

[0072] Next, in step S110, when the machine learning device 3 receives the learning instruction for the learning model 12 sent in step S101, the data acquisition unit 300 accesses the person database 20 to acquire person data that satisfies the learning conditions of the learning model 12 included in the learning instruction, and acquires multiple person data corresponding to multiple people.

[0073] Next, in step S120, the first generation processing unit 301a judges for each of the plurality of person data acquired in step S110 whether or not the behavior of each person matches the behavior factor index in accordance with the judgment rules registered in the judgment rule data 310. Then, in step S121, the first generation processing unit 301a assigns a behavior factor index to the person data for each person based on the judgment result, thereby generating a plurality of sets of learning data 11a.

[0074] Next, in step S130, the first learning processing unit 301b causes the learning model 12a to learn the correlation between the person data and the behavioral factor indexes, using the multiple sets of learning data 11a (which may be preprocessed learning data 11a) generated in step S121.

[0075] Next, in step S140, the second generation processing unit 301c inputs the multiple person data acquired in step S110 to the learning model 12a trained in step S130, and creates an evaluation list consisting of the person data and the behavior factor indexes by associating the behavior factor indexes output from the learning model 12a as the inference results with the person data. Then, in step S141, the second generation processing unit 301c transmits the evaluation list to the worker terminal device 5.

[0076] Next, in step S142, when the worker terminal device 5 receives the evaluation list transmitted in step S141, it displays the evaluation list on the display screen of the worker terminal device 5. At that time, the worker terminal device 5 displays person data corresponding to the people displayed in the evaluation list on the display screen of the worker terminal device 5 as reference information for the worker 10 to perform the annotation work.

[0077] The display screen may display, for example, purchase history, web browsing history, web search history, movement history, and questionnaire response history included in the person data, and may further display feature data associated with each history (in this embodiment, product, store, website, location, and region) and behavior feature indexes assigned to the feature data (in this embodiment, product index, store index, website index, location index, and region index). For example, a list of products purchased by each person and product attributes of each product may be displayed, and a product index currently assigned to the product (for example, "luxury orientation") may be displayed as part of the product attributes. In addition, the display screen may display not only raw data of the person data but also information obtained by performing a predetermined statistical process on the person data, and the display contents and the statistical process contents may be changeable based on the operation of the operator 10. In addition, the evaluation list on the display screen may display not only the inference results by the learning model 12a in step S140 but also the judgment results by the judgment rules in step S120.

[0078] Then, in step S143, when the worker terminal device 5 receives a correction instruction for the behavioral factor index on the display screen from the worker 10, in step S144, the worker terminal device 5 transmits the correction instruction to the machine learning device 3. The example of FIG. 11 illustrates a case in which, as in FIG. 7, a correction instruction to assign "1: health-conscious" to "User B" and a correction instruction to assign "0: not health-conscious" to "User C" are received by the worker terminal device 5 as annotation work by the worker 10.

[0079] Next, in step S145, when the second generation processing unit 301c receives the correction instruction transmitted in step S144 and accepts the correction instruction for the behavior factor index output in step S140, the second generation processing unit 301c generates a plurality of sets of learning data 11b by assigning the behavior factor index to the person data based on the acceptance result. The worker terminal device 5 may accept the correction instruction for the behavior characteristic index from the worker 10 on the display screen, and in that case, the second generation processing unit 301c may update (correct) the person database 20 by correcting the behavior characteristic index in the characteristic data registered in the person database 20 based on the acceptance result. The correction instruction for the behavior characteristic index (here, the product index) may be, for example, an additional product index of "healthy use" to a product currently assigned with a behavior characteristic index of "luxury orientation" or may be an instruction to delete the product index of "luxury orientation".

[0080] Next, in step S150, the second learning processing unit 301d uses the learning data 11b generated in step S145 (which may be the preprocessed learning data 11b) to make the learning model 12b learn the correlation between the person data and the behavior factor index. At this time, the second learning processing unit 301d may use not only the learning data 11b generated in step S145, but also the learning data 11a generated in step S121. Note that, when the person database 20 is updated based on a correction instruction for the behavior feature index, the preprocessing is performed on the learning data 11b in a state where the updated person database 20 is reflected.

[0081] Next, in step S160, the machine learning unit 301 determines whether or not a predetermined learning end condition is satisfied when the second learning processing unit 301d causes the learning model 12b to learn the correlation between the person data and the behavioral factor index.

[0082] Then, when the machine learning unit 301 determines that the predetermined learning end condition is not satisfied ("No" in step S160), the process returns to step S140. At that time, in step S140, the second generation processing unit 301c inputs the multiple person data acquired in step S110 to the learning model 12b trained by the second learning processing unit 301d in step S150, instead of the learning model 12a trained by the first learning processing unit 301b in step S130, and the machine learning unit 301 repeatedly executes the subsequent processes (steps S140 to S150) until it is determined in step S160 that the learning end condition is satisfied.

[0083] On the other hand, if the machine learning unit 301 determines that the predetermined learning end condition is met ("Yes" in step S160), then in step S170, the learning model 12b at that time is stored in the storage unit 31 as the trained learning model 12. This ends the series of processes shown in Figures 10 and 11. In the machine learning method, step S110 corresponds to a data acquisition step, steps S120 to S160 correspond to a machine learning step, and step S170 corresponds to a trained model storage step.

[0084] In the above description, the series of processes shown in FIG. 10 and FIG. 11 are executed when the worker terminal device 5 receives a learning instruction from the worker 10. 10 and 11 may be executed when the machine learning unit 301 determines that a predetermined execution condition (for example, when a certain time has elapsed since the previous execution or when the person database 20 is updated) is satisfied. In the above description, the machine learning unit 301 returns to step S140 when it determines that a predetermined learning end condition is not satisfied ("No" in step S160). However, the machine learning unit 301 may return to step S120, or may switch between returning to step S120 and returning to step S140 based on an instruction from the operator 10 or a determination by the machine learning unit 301. When returning to step S120, the operator 10 may change, for example, a determination rule for a behavioral factor index, and the machine learning unit 301 may execute the process from step S120 onward based on the changed determination rule.

[0085] As described above, the machine learning device 3 and the machine learning method according to the present embodiment can provide a learning model 12 capable of estimating a behavioral factor index indicating factors that motivate a person to take action, based on person data that records the person's actions from one or more perspectives. Therefore, by using this learning model 12 in the data processing device 4, it is possible to appropriately assign (infer) a behavioral factor index inherent to the person (user) without requesting the person (user) to input personal information.

[0086] Furthermore, the machine learning unit 301 performs machine learning using the learning data 11a generated based on the judgment result according to the judgment rules, and performs machine learning in stages using the learning data 11b generated based on the acceptance result of the correction instruction for the behavioral factor index, thereby making the learning model 12 learn the correlation between the person data and the behavioral factor index. Therefore, since the machine learning of the learning model 12 is performed in stages through the automatic generation of the learning data 11a according to the judgment rules and the correction of the learning data 11b through the annotation work of the worker 10, it is possible to generate a highly accurate learning model 12 while reducing the workload of the worker 10 in the annotation work.

[0087] Furthermore, the machine learning unit 301 updates the behavioral feature index assigned to the feature data in the person database 20 based on the result of receiving the instruction to modify the behavioral feature index, thereby performing machine learning in a state that reflects the updated person database 20. Therefore, through the modification of the behavioral feature index by the annotation work of the worker 10, the accuracy of the behavioral feature index assigned to the feature data included in the person data is improved, so that a more accurate learning model 12 can be generated.

[0088] (Data processing method) 12 is a flowchart showing an example of a data processing method by the data processing device 4. The following describes a case where the worker 10 performs a tag assignment instruction operation using the worker terminal device 5, and the data processing device 4 refers to the person database 20 and the trained learning model 12, and assigns behavioral factor indices to the person data.

[0089] First, in step S200, the worker terminal device 5 receives an assignment instruction including an assignment condition for assigning a behavioral factor index from the worker 10, and then in step S201 transmits the assignment instruction to the machine learning device 3. As the assignment condition, for example, a person data acquisition condition for acquiring, from the person database 20, person data to which a behavioral factor index is to be assigned is specified.

[0090] Next, in step S210, when the data processing device 4 receives the instruction to assign the behavioral factor index transmitted in step S201, the data acquisition unit 400 accesses the person database 20 to acquire person data that satisfies the condition for assigning the behavioral factor index included in the assignment instruction, and acquires, for example, multiple person data corresponding to multiple persons.

[0091] Next, in step S220, the index assigning unit 401 inputs the plurality of person data acquired in step S110 to the learning model 12, and assigns the behavioral factor indexes output from the learning model 12 as the inference results to the person data. In the example of FIG. 12, the index assigning unit 401 assigns "1: health-conscious" to "user P" and "user R" as the behavioral factor index of "health-consciousness" after post-processing based on the comparison between the variable value of the behavioral factor index and a predetermined threshold value, and assigns "0: not health-conscious" to "user Q". Note that the assignment list may include the behavioral factor index (value in the range of 0 to 1) before the post-processing is performed in addition to or instead of the behavioral factor index (0 or 1) after the post-processing is performed by the index assigning unit 401.

[0092] Next, in step S230, the output processing unit 402 provides the behavioral factor indexes (the assignment list in FIG. 12) assigned to the person data in step S220 to, for example, the database device 2, so that the behavioral factor indexes are registered in the behavioral factor index table of the person database 20. This ends the series of processes shown in FIG. 12. In the data processing method, step S210 corresponds to a data acquisition step, step S220 corresponds to an index assignment step, and step S230 corresponds to an output processing step.

[0093] In the above, it has been described that the series of processes shown in FIG. 12 are executed when the worker terminal device 5 receives an assignment instruction from the worker 10. However, the series of processes shown in FIG. 12 may be executed when the data processing device 4 determines that a predetermined execution condition is satisfied (for example, when a certain time has elapsed since the previous execution or when the person database 20 is updated).

[0094] As described above, the data processing device 4 and data processing method according to this embodiment can appropriately assign (infer) behavioral factor indexes inherent to a person (user) without requiring the person to input personal information. The behavioral factor indexes assigned to each person can be processed by predetermined statistical processing, visualization processing, etc., and used in various services (product planning, product development, production management, purchase and order management, sales strategy formulation, marketing, advertising, etc.).

[0095] (Other embodiments) The present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit and scope of the present invention, all of which are included in the technical concept of the present invention.

[0096] In the above embodiment, the database device 2, the machine learning device 3, and the data processing device 4 have been described as being configured as separate devices, but these three devices may be configured as a single device, or any two of these three devices may be configured as a single device. In addition, at least one of the machine learning device 3 and the data processing device 4 may be incorporated in the operator terminal device 5.

[0097] In the above embodiment, a case has been described in which a neural network is adopted as a learning model for implementing machine learning by the machine learning unit 301, but other machine learning models may be adopted. Examples of other machine learning models include tree models such as decision trees and regression trees, ensemble learning models such as bagging and boosting, and neural network models (deep neural networks) such as recurrent neural networks, convolutional neural networks, and LSTM. learning), hierarchical clustering, non-hierarchical clustering, k-nearest neighbors, k-means Examples of such models include clustering models such as the average mean square method, multivariate analysis models such as principal component analysis, factor analysis, and logistic regression, and support vector machines.

[0098] (Inference device, inference method, or inference program) The present invention can be provided not only in the form of the data processing device 4 (data processing method or data processing program) according to the above embodiment, but also in the form of an inference device (inference method or inference program). In this case, the inference device (inference method or inference program) can include a memory and a processor, and the processor executes a series of processes. The series of processes includes a data acquisition process (data acquisition step) for acquiring person data, and an inference process (inference step) for inferring a behavioral factor index for the person data when the person data is acquired in the data acquisition process.

[0099] Providing it in the form of an inference device (inference method or inference program) makes it easier to apply it to various devices compared to implementing the data processing device 4. It can be naturally understood by those skilled in the art that when the inference device (inference method or inference program) infers a behavioral factor index, the inference method implemented by the index assignment unit may be applied using a trained learning model generated by the machine learning device 3 and the machine learning method according to the above embodiment. [Explanation of symbols]

[0100] 1...Personal information management system, 2...Database device, 3...Machine learning device, 4...data processing device, 5...operator terminal device, 6...network, 10...worker, 11, 11a, 11b...learning data, 12, 12a, 12b…Learning model, 20…Person database, 30: control unit, 31: memory unit, 32: communication unit, 33: input unit, 34: display unit, 40: control unit, 41: storage unit, 42: communication unit, 43: input unit, 44: display unit, 120...input layer, 121...intermediate layer, 122...output layer, 300: Data Acquisition Section, 301: Machine Learning Section, 301a...first generation processing unit, 301b...first learning processing unit, 301c...second generation processing unit, 301d...second learning processing unit, 310: Decision rule data, 311: Machine learning program, 400: data acquisition unit, 401: index assignment unit, 402: output processing unit, 410...Data processing program

Claims

1. a machine learning unit that inputs a plurality of sets of learning data, each set being composed of person data that records a person's behavior based on one or more viewpoints and a behavioral factor index that indicates a factor that motivates the person to perform the behavior, into a learning model, and causes the learning model to learn a correlation between the person data and the behavioral factor index by machine learning; A trained model storage unit that stores the trained model in which the correlation is trained by the machine learning unit, The machine learning unit is a first generation processing unit that generates the learning data by determining, for each of the persons, whether or not the person matches the behavioral factor index in accordance with a determination rule defined for the person data based on at least a part of the viewpoint, and assigning the behavioral factor index to the person data based on the determination result; a first learning processing unit that causes the learning model to learn the correlation by using the learning data generated by the first generation processing unit; a second generation processing unit that receives a correction instruction for the behavior factor index output from the learning model by inputting the person data into the learning model in which the correlation has been learned by the first learning processing unit, and generates the learning data by assigning the behavior factor index to the person data based on the reception result; A second learning processing unit that causes the learning model to learn the correlation by using the learning data generated by at least the second generation processing unit. Machine learning device.

2. The second generation processing unit, inputting the person data into the learning model in which the correlation has been learned by the first learning processing unit, thereby causing the behavioral factor indexes output from the learning model to be associated with the person data and displayed for each person, and receiving the modification instruction for the displayed behavioral factor indexes; The machine learning device according to claim 1 .

3. The second generation processing unit, The person data is input to the learning model in which the correlation is learned by the first learning processing unit. and displaying the behavioral factor indexes output from the learning model for each person by inputting the data and corresponding to information obtained by performing a predetermined statistical process on the person data, and receiving the instruction to modify the displayed behavioral factor indexes. The machine learning device according to claim 1 .

4. The second generation processing unit, the behavior factor index assigned to the person data by the first generation processing unit based on the determination result, and the behavior factor index output from the learning model in which the correlation has been learned by the first learning processing unit by inputting the person data into the learning model, are displayed for each person in association with the person data, and the modification instruction is received for the displayed behavior factor index; The machine learning device according to claim 1 .

5. The machine learning unit is When the second learning processing unit has learned the correlation in the learning model, if a predetermined learning end condition is not satisfied, the second generation processing unit inputs the person data into the learning model in which the correlation has been learned by the second learning processing unit instead of the first learning processing unit, accepts the correction instruction for the behavior factor index output from the learning model, and generates the learning data by assigning the behavior factor index to the person data based on the acceptance result; and the second learning processing unit repeatedly executes a process of having the learning model learn the correlation using at least the learning data generated by the second generation processing unit until the learning end condition is satisfied. The machine learning device according to claim 1 .

6. a machine learning unit that inputs a plurality of sets of learning data, each set being composed of person data that records a person's behavior based on one or more viewpoints and a behavioral factor index that indicates a factor that motivates the person to perform the behavior, into a learning model, and causes the learning model to learn a correlation between the person data and the behavioral factor index by machine learning; A trained model storage unit that stores the trained model in which the correlation is trained by the machine learning unit, The person data is a behavioral characteristic index corresponding to the behavior factor index is recorded in association with the assigned characteristic data, The machine learning unit is a pre-processing is performed to convert the personal data into features for input to the learning model based on the behavioral feature index assigned to the feature data included in the personal data, and a plurality of sets of pre-processed learning data, which are composed of the features converted from the personal data in the pre-processing and the behavioral factor index included in the personal data, are input to the learning model, thereby causing the learning model to learn the correlation; The machine learning unit is a first generation processing unit that generates the learning data by determining, for each of the persons, whether or not the person matches the behavioral factor index in accordance with a determination rule defined for the person data based on at least a part of the viewpoint, and assigning the behavioral factor index to the person data based on the determination result; a first learning processing unit that causes the learning model to learn the correlation by using the learning data generated by the first generation processing unit and the learning data after the preprocessing; The feature amount converted from the person data in the preprocessing is input to the learning model in which the correlation is learned by the first learning processing unit, and the behavior factor index output from the learning model and the preprocessing data assigned to the feature data included in the person data are output. a second generation processing unit that receives a correction instruction for at least one of the behavioral feature index and the behavior factor index, and generates the learning data by assigning the behavior factor index to the person data based on the reception result, and corrects the behavioral feature index assigned to the feature data; a second learning processing unit that causes the learning model to learn the correlation by using the learning data generated by at least the second generation processing unit, the learning data being the preprocessed learning data; Machine learning device.

7. The second generation processing unit, inputting the feature amount converted from the person data in the preprocessing into the learning model in which the correlation has been learned by the first learning processing unit, the behavior factor index output from the learning model and the behavior feature index assigned to the feature data included in the person data are displayed for each person in association with the person data, and the modification instruction is received for at least one of the displayed behavior factor index and the behavior feature index. The machine learning device according to claim 6.

8. The second generation processing unit, the feature amount converted from the person data in the preprocessing is input to the learning model in which the correlation has been learned by the first learning processing unit, and the behavior factor index output from the learning model and the behavior feature index assigned to the feature data included in the person data are displayed for each person in association with information obtained by performing a predetermined statistical process on the person data, and the modification instruction is received for at least one of the displayed behavior factor index and the behavior feature index. The machine learning device according to claim 6.

9. The second generation processing unit, the behavior factor index assigned to the person data by the first generation processing unit based on the determination result, and the feature amount converted from the person data in the preprocessing are input to the learning model in which the correlation has been learned by the first learning processing unit, and the behavior factor index output from the learning model and the behavior feature index assigned to the feature data included in the person data are associated with the person data and displayed for each person, and the modification instruction is received for at least one of the behavior factor index and the behavior feature index. The machine learning device according to claim 6.

10. The machine learning unit is When the second learning processing unit has learned the correlation in the learning model, if a predetermined learning end condition is not satisfied, the second generation processing unit receives the modification instruction for at least one of the behavior factor index output from the learning model, in which the correlation has been learned by the second learning processing unit instead of the first learning processing unit, by inputting the feature amount converted from the person data in the preprocessing, and the behavior feature index assigned to the feature data included in the person data, and generates the learning data by assigning the behavior factor index to the person data based on the reception result, and modifies the behavior feature index assigned to the feature data; and the second learning processing unit repeatedly executes a process of having the learning model learn the correlation using at least the learning data generated by the second generation processing unit and the learning data after the preprocessing, until the learning end condition is satisfied. The machine learning device according to any one of claims 6 to 9.

11. a machine learning process for inputting a plurality of sets of learning data, each set being composed of person data that records a person's behavior based on one or more viewpoints and a behavioral factor index that indicates a factor that motivates the person to perform the behavior, into a learning model, thereby causing the learning model to learn a correlation between the person data and the behavioral factor index by machine learning; A trained model storage step of storing the trained model in a trained model storage unit, the trained model having the correlation trained by the machine learning step; The machine learning process includes: a first generation process step of judging for each of the persons whether or not the person matches the behavioral factor index in accordance with a judgment rule defined for the person data based on at least a part of the viewpoint, and generating the learning data by assigning the behavioral factor index to the person data based on the judgment result; a first learning process step of causing the learning model to learn the correlation by using the learning data generated by the first generation process step; a second generation processing step of receiving a correction instruction for the behavior factor index output from the learning model by inputting the person data into the learning model that has learned the correlation by the first learning processing step, and generating the learning data by assigning the behavior factor index to the person data based on the reception result; and a second learning process step of causing the learning model to learn the correlation by using the learning data generated at least by the second generation process step. Machine learning methods.

12. a machine learning process for inputting a plurality of sets of learning data, each set being composed of person data that records a person's behavior based on one or more viewpoints and a behavioral factor index that indicates a factor that motivates the person to perform the behavior, into a learning model, thereby causing the learning model to learn a correlation between the person data and the behavioral factor index by machine learning; A trained model storage step of storing the trained model in a trained model storage unit, the trained model having the correlation trained by the machine learning step; The person data is a behavioral characteristic index corresponding to the behavior factor index is recorded in association with the assigned characteristic data, The machine learning process includes: a pre-processing is performed to convert the personal data into features for input to the learning model based on the behavioral feature index assigned to the feature data included in the personal data, and a plurality of sets of pre-processed learning data, which are composed of the features converted from the personal data in the pre-processing and the behavioral factor index included in the personal data, are input to the learning model, thereby causing the learning model to learn the correlation; The machine learning process includes: a first generation process step of judging for each of the persons whether or not the person matches the behavioral factor index in accordance with a judgment rule defined for the person data based on at least a part of the viewpoint, and generating the learning data by assigning the behavioral factor index to the person data based on the judgment result; a first learning process step of causing the learning model to learn the correlation by using the learning data generated by the first generation process step and the learning data after the preprocessing; The feature amount converted from the person data in the preprocessing is input to the learning model that has learned the correlation in the first learning processing step, and a correction instruction is received for at least one of the behavior factor index output from the learning model and the behavior feature index assigned to the feature data included in the person data, and the learning data is generated by assigning the behavior factor index to the person data based on the reception result. and a second generation process step of correcting the behavioral feature index assigned to the feature data. and a second learning process step of causing the learning model to learn the correlation by using the learning data generated by at least the second generation process step, the learning data being the preprocessed learning data. Machine learning methods.

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