Information processing device, information processing method, and program

Abstract

Previously, it was not easy to obtain a person's credit information. [Solution] An information processing device 1 includes a personal information receiving unit 121 that receives personal information including a personal image, a credit acquisition unit 132 that provides image-related information based on the personal image received by the personal information receiving unit to a learning model and acquires credit information, which is information regarding the person's credit, and a credit output unit 141 that outputs the credit information acquired by the credit acquisition unit 132.This makes it possible to easily acquire a person's credit information using a personal image.

Description

[Technical Field]

[0001] The present invention relates to an information processing device or the like that uses a person's image to acquire and output credit information about the person. [Background technology]

[0002] Previously, benchmarks for predicting credit scores using machine learning techniques have been implemented for credit risk assessment on peer-to-peer (P2P) social lending platforms (see Non-Patent Document 1). This conventional technology is capable of outputting whether or not a person will repay the loan using the credit history of the person being assessed, information on the loan application, the status of the loan, etc. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Vincenzo Moscato and Antonio Picariello and Giancarlo Sperli:A benchmark of machine learning approaches for credit score prediction(2021) Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the prior art, it was not easy to obtain a person's credit information. [Means for solving the problem]

[0005] The information processing device of the first invention is an information processing device that includes a personal information receiving unit that receives personal information including a personal image, a credit acquisition unit that provides image-related information based on the personal image received by the personal information receiving unit to a learning model and acquires credit information, which is information regarding the person's credit, and a credit output unit that outputs the credit information acquired by the credit acquisition unit.

[0006] With this configuration, credit information of a person can be easily obtained using an image of the person.

[0007] In addition, the information processing device of the second invention is an information processing device in which, compared to the first invention, the credit acquisition unit is equipped with a characteristic value acquisition means that uses a person image accepted by the person information acceptance unit to acquire each personality characteristic value of 2 or more, which is a score of a characteristic related to the person's personality, and a credit acquisition means that acquires credit information using the 2 or more personality characteristic values ​​acquired by the characteristic value acquisition means.

[0008] With this configuration, credit information of a person can be easily obtained using an image of the person.

[0009] In addition, the information processing device of the third invention is an information processing device in which, compared to the second invention, the characteristic value acquisition means provides a person image accepted by the person information acceptance unit to a first learning model and acquires each personality characteristic value of 2 or more, which is a score of a characteristic related to the person's personality, from the first learning model.

[0010] With this configuration, credit information of a person can be easily obtained using an image of the person.

[0011] In addition, the information processing device of the fourth invention is an information processing device in which, compared to the second invention, the characteristic value acquisition means provides a person image accepted by the person information acceptance unit to a first learning model and acquires from the first learning model each personality characteristic value of two or more which is a score of a characteristic related to the person's personality, and the credit acquisition means provides a group of person explanatory variables including the two or more personality characteristic values ​​acquired by the characteristic value acquisition means to a second learning model and acquires credit information from the second learning model.

[0012] With this configuration, credit information of a person can be easily obtained using an image of the person.

[0013] Furthermore, the information processing device of the fifth invention is an information processing device that, compared to any one of the first to fourth inventions, further includes an attribute value acquisition unit that acquires one or more person attribute values ​​of a person, and the credit acquisition unit also uses the one or more person attribute values ​​to acquire credit information.

[0014] With this configuration, appropriate credit information of a person can be easily obtained using a person image and one or more person attribute values. [Effects of the Invention]

[0015] According to the information processing device of the present invention, credit information of a person can be easily obtained using an image of the person.

[0016] The above effects are not necessarily limiting, and any of the effects shown in this specification or that can be understood from this specification may be achieved in addition to or instead of the above effects. Other effects may also be achieved. [Brief explanation of the drawings]

[0017] [Figure 1] Conceptual diagram of information processing system A according to the first embodiment [Figure 2] Block diagram of the information processing system A [Figure 3] A flowchart illustrating an example of the operation of the information processing device 1. [Figure 4] 10 is a flowchart illustrating a first example of the credit acquisition process. [Figure 5] 10 is a flowchart illustrating a second example of the credit acquisition process. [Figure 6] 10 is a flowchart illustrating a third example of the credit acquisition process. [Figure 7] A flowchart illustrating an example of the operation of the terminal device 2 [Figure 8] Figure showing an example of an image of the same person [Figure 9] Figure showing an example of the output [Figure 10] Block diagram of the computer system DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, embodiments of an information processing device and the like will be described with reference to the drawings. Note that components with the same reference numerals in the embodiments perform similar operations, and therefore repeated description may be omitted.

[0019] (Embodiment 1) In this embodiment, an information processing device that uses a person's image and a learning model to acquire and output credit information about the person will be described.

[0020] In this embodiment, we will describe an information processing device that uses a person's image and a learning model to obtain two or more personality characteristic values ​​of the person, and then uses the two or more personality characteristic values ​​to obtain and output credit information for the person.

[0021] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is not important. Information X and information Y may be linked, may exist in the same buffer, information X may be included in information Y, or information Y may be included in information X, etc.

[0022] Furthermore, in this specification, selecting or determining information Z means obtaining information Z, obtaining a pointer to information Z, obtaining the ID of information Z, setting a flag on information Z, etc., and it is sufficient if information Z can be accessed.

[0023] 1 is a conceptual diagram of an information processing system A according to this embodiment. The information processing system A includes an information processing device 1, one or more terminal devices 2, and one or more model management devices 3.

[0024] The information processing device 1 is a device that acquires and outputs credit information of a person using a person's image and a learning model. The learning model is a mechanism that learns information and performs prediction, classification, generation, etc. using the results of that learning. The learning model is, for example, a mechanism that learns patterns (regularities) from data and performs prediction, classification, generation, etc. using the patterns. The learning model may be, for example, a generative AI model or a machine learning learning model. The learning model is, for example, OpenAI's CLIP (Contrastive Language?Image Pre-training), but the type is not important. CLIP is a model that maps images and text to the same vector space and learns their correspondence with each other. The information processing device 1 is typically a server but may also be a terminal. When the information processing device 1 is a server, the information processing device 1 may be, for example, a cloud server or an ASP server, but the type is not important. When the information processing device 1 is a terminal, the information processing device 1 may be, for example, a smartphone, a tablet terminal, a so-called personal computer, etc., but the type is not important. If the information processing device 1 is a terminal device, the information system A does not need the terminal device 2.

[0025] The terminal device 2 is a terminal used by a user. The user is, for example, a person whose credit information is to be acquired, but may also be a person who acquires a person's credit information (for example, a person who lends money to a person, purchases a person's goods, or provides money using goods as collateral). The terminal device 2 is typically a smartphone, but may also be a tablet terminal, a so-called personal computer, or the like, and the type does not matter.

[0026] The model management device 3 is a device that has the function of a learning model. The model management device 3 is a device that stores one or more learning models. The model management device 3 is, for example, a cloud server or an ASP server, but the type does not matter. Furthermore, the information processing device 1 may have the function of a learning model. In such a case, the model management device 3 is not necessary for the information system A.

[0027] 2 is a block diagram of an information processing system A according to the present embodiment. The information processing device 1 includes a storage unit 11, a reception unit 12, a processing unit 13, and an output unit 14. The storage unit 11 includes a model storage unit 111 and a user management unit 112. The reception unit 12 includes a person information reception unit 121. The processing unit 13 includes an attribute value acquisition unit 131 and a credit acquisition unit 132. The credit acquisition unit 132 includes a characteristic value acquisition means 1321 and a credit acquisition means 1322. The output unit 14 includes a credit output unit 141.

[0028] The terminal device 2 includes a terminal storage unit 21, a terminal reception unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal reception unit 25, and a terminal output unit 26.

[0029] Various types of information are stored in the storage unit 11 that constitutes the information processing device 1. The various types of information include, for example, one or more learning models and user information, which will be described later.

[0030] One or more learning models are stored in the model storage unit 111. The learning models are, for example, learning models stored in the model management device 3.

[0031] The learning model is, for example, a model that takes as input a person's image of a person and outputs the person's credit information. The learning model is, for example, a model that takes as input a person's image of a person and one or more person attribute values ​​of the person and outputs the person's credit information. The learning model is, for example, a model that takes as input a person's image of a person and outputs two or more personality trait values ​​of the person. The learning model is, for example, a model that takes as input a person's image of a person and one or more person attribute values ​​of the person and outputs two or more personality trait values ​​of the person. The learning model is, for example, a model that takes as input two or more personality trait values ​​of a person and outputs the person's credit information. The learning model is, for example, a model that takes as input two or more personality trait values ​​of a person and one or more person attribute values ​​of the person and outputs the person's credit information.

[0032] A person image is an image of a single person. The person image is preferably a face image. The person image may also be an image of the whole body of a person. The person image may be in JPEG or PNG format, for example, but the data type is not important.

[0033] Credit information is information relating to a user's trustworthiness. Credit information is, for example, information that identifies a user's trustworthiness. Credit information is, for example, information for determining whether it is safe to lend money to a user. Credit information is, for example, information indicating whether or not a user will repay. Credit information is, for example, the probability of default. Credit information is, for example, a natural number between 1 and 10. Credit information is, for example, a natural number between 1 and 5. Credit information is, for example, a numerical value between 0 and 1. Credit information is, for example, one of A, B, or C.

[0034] The personality trait value is a score of a trait related to a person's personality. The one or more personality traits may be, for example, trustworthiness, aggressiveness, risk-taking, cunningness, achievement motivation, calmness, modesty, extroversion, organization, planning, carelessness, emotionality, curiosity, sociability, hardworkingness, easygoingness, open-mindedness, shyness, laziness, impulsiveness, intelligence, open-mindedness, conscientiousness, extroversion, agreeableness, or neuroticism.

[0035] A person attribute value is an attribute value of a person, such as gender, age, date of birth, occupation, address, and place of residence (prefecture, city, town, village, etc.).

[0036] The user management unit 112 stores one or more pieces of user information. The user information is information related to a user. The user information is associated with, for example, a user identifier. The user information has, for example, one or more personal attribute values. The user identifier is information that identifies a user. The user identifier is, for example, a user ID, an email address, a telephone number, or a terminal device identifier. The terminal device identifier is information that identifies the user's terminal device 2. The terminal device identifier is, for example, the MAC address or IP address of the terminal device 2.

[0037] The reception unit 12 receives various instructions and information. The various instructions and information are, for example, output instructions and appraisal instructions. An output instruction is an instruction to output credit information. An output instruction includes, for example, a person's image. An output instruction is associated with, for example, a user identifier. An appraisal instruction is an instruction to appraise a product that a user wants to sell or use as collateral. An appraisal instruction includes, for example, a product image that is an image of a product that a user wants to sell, etc. An appraisal instruction includes, for example, a person's image. An appraisal instruction is associated with, for example, a user identifier.

[0038] Here, acceptance typically refers to the reception of information transmitted via a wired or wireless communication line, but may also be a concept that includes the reception of information input from an input device such as a keyboard, mouse, or touch panel, or the reception of information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

[0039] The personal information receiving unit 121 receives personal information. The personal information receiving unit 121 may receive an output instruction or an assessment instruction including personal information.

[0040] The personal information is information about a person whose credit information is to be acquired. The personal information includes a person's image. The personal information includes, for example, one or more person attribute values.

[0041] The credit acquisition unit 132 provides image-related information of a person to the learning model to acquire credit information of the person. It is preferable that the credit acquisition unit 132 also acquires credit information using one or more person attribute values.

[0042] The image-related information is information based on a person image. The image-related information includes a person image or two or more personality trait values. The image-related information may also include one or more person attribute values.

[0043] The characteristic value acquisition means 1321 uses the person image accepted by the person information acceptance unit 121 to acquire two or more personality characteristic values.

[0044] The characteristic value acquisition means 1321, for example, provides a person image accepted by the person information accepting unit 121 to a first learning model, and acquires two or more personality characteristic values ​​from the first learning model. The characteristic value acquisition means 1321, for example, provides a person image accepted by the person information accepting unit 121 and one or more person attribute values ​​of the person to a first learning model, and acquires two or more personality characteristic values ​​from the first learning model. Such a first learning model is, for example, a model that receives a person image of a person as input and outputs two or more personality characteristic values ​​of the person. The first learning model is, for example, a model that receives a person image of a person and one or more person attribute values ​​of the person as input and outputs two or more personality characteristic values ​​of the person. The first learning model is, for example, CLIP.

[0045] The credit acquisition means 1322 acquires credit information using two or more personality characteristic values ​​acquired by the characteristic value acquisition means 1321 .

[0046] The credit acquisition means 1322, for example, provides a group of person explanatory variables including two or more personality characteristic values ​​acquired by the characteristic value acquisition means 1321 to the second learning model and acquires credit information from the second learning model. The group of person explanatory variables is, for example, two or more personality characteristic values ​​or two or more personality characteristic values ​​and one or more person attribute values. The second learning model is, for example, a model that takes two or more personality characteristic values ​​of a person as input and outputs credit information for the person. The second learning model is, for example, a model that takes two or more personality characteristic values ​​of a person and one or two or more person attribute values ​​of the person as input and outputs credit information for the person. The second learning model is, for example, a machine learning learning model or a generative AI model.

[0047] A machine learning learning model is information configured by machine learning learning processing and is information used in machine learning prediction processing. A machine learning learning model may also be called a learner, a classifier, a classification model, etc. Deep learning is preferred as a machine learning algorithm, but random forest, decision tree, SVR, SVM, etc. are also acceptable. Furthermore, for machine learning, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, fastText, TinySVM, etc., and various existing libraries can be used.

[0048] The output unit 14 outputs various types of information. The various types of information include, for example, personality characteristic values ​​and credit information. The output unit 14 outputs, for example, two or more personality characteristic values ​​acquired by the characteristic value acquisition means 1321.

[0049] Here, output means, for example, transmission to terminal device 2, but it may also be a concept that includes display on a display, projection using a projector, printing on a printer, sound output, transmission to a device other than terminal device 2, storage on a recording medium, and handing over processing results to other processing devices or other programs.

[0050] The credit output unit 141 outputs the credit information acquired by the credit acquisition unit 132. The credit output unit 141 transmits the credit information to the terminal device 2, for example.

[0051] Various types of information are stored in the terminal storage unit 21 included in the terminal device 2. The various types of information include, for example, a user identifier.

[0052] The terminal reception unit 22 receives various types of information, instructions, etc. Examples of the various types of information, instructions, etc. include output instructions and appraisal instructions.

[0053] The means for inputting various information and instructions may be any means, such as a touch panel, keyboard, mouse, menu screen, camera, or microphone.

[0054] The terminal processing unit 23 performs various types of processing. For example, the various types of processing are processing to convert received information, instructions, etc. into information, instructions, etc. with a structure to be transmitted. For example, the various types of processing are processing to convert received information into information with a structure to be output.

[0055] In response to receiving the output instruction or appraisal instruction, the terminal processing unit 23 composes the output instruction or appraisal instruction to be transmitted.

[0056] The terminal transmission unit 24 transmits various types of information, instructions, etc. to the information processing device 1. The various types of information, instructions, etc. are, for example, output instructions and appraisal instructions. The terminal transmission unit 24 transmits appraisal instructions having a person image and a product image to the information processing device 1. The terminal transmission unit 24 transmits output instructions having a person image to the information processing device 1. The terminal transmission unit 24 transmits various types of information, instructions, etc. to the information processing device 1 in association with a user identifier.

[0057] The terminal receiving unit 25 receives various types of information from the terminal, such as credit information and various types of screen information.

[0058] The terminal output unit 26 outputs various information, such as credit information and various screens.

[0059] Output is a concept that includes displaying on a display, projecting using a projector, printing on a printer, outputting sound, transmitting to an external device, storing on a recording medium, and handing over processing results to other processing devices or other programs.

[0060] The storage unit 11, model storage unit 111, user management unit 112, and terminal storage unit 21 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0061] There is no restriction on the process by which information is stored in the storage unit 11 etc. For example, information may be stored in the storage unit 11 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 11 etc., or information input via an input device may be stored in the storage unit 11 etc.

[0062] The reception unit 12 and the personal information reception unit 121 are preferably realized by wireless or wired communication means, but may also be realized by device drivers for input means such as touch panels and keyboards, or control software for menu screens.

[0063] The processing unit 13, attribute value acquisition unit 131, credit acquisition unit 132, characteristic value acquisition means 1321, and credit acquisition means 1322 can usually be realized by a processor, memory, etc. The processing procedures of the processing unit 13, etc. are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type does not matter.

[0064] The output unit 14 and the credit output unit 141 are preferably realized by wireless or wired communication means, but may also be realized by driver software for an output device such as a display or speaker, or by driver software for an output device and an output device, etc.

[0065] The terminal reception unit 22 can be realized by a device driver for an input means such as a touch panel or a keyboard, or control software for a menu screen.

[0066] The terminal transmitting unit 24 is usually realized by a wireless or wired communication means, but may also be realized by a broadcasting means.

[0067] The terminal receiving unit 25 is usually realized by a wireless or wired communication means, but may also be realized by a means for receiving broadcasts.

[0068] The terminal output unit 26 may or may not include an output device such as a display, a speaker, etc. The terminal output unit 26 may be realized by driver software for an output device, or by a combination of driver software for an output device and the output device, etc.

[0069] Next, an example of the operation of the information processing device 1 will be described with reference to the flowchart of FIG.

[0070] (Step S301) The person information receiving unit 121 determines whether or not an output instruction including a person image has been received. If an output instruction has been received, the process proceeds to step S302; if not, the process proceeds to step S305. Here, the receiving unit 12 determines whether or not an output instruction has been received from, for example, the terminal device 2.

[0071] (Step S302) The attribute value acquisition unit 131 acquires one or more person attribute values ​​paired with the user identifier associated with the output instruction accepted in step S301 from the user management unit 112. The credit acquisition unit 132 acquires image-related information having one or more person attribute values ​​and a person image included in the output instruction.

[0072] (Step S303) The credit acquisition unit 132 acquires credit information using the image-related information acquired in step S302. An example of such credit acquisition processing will be described with reference to the flowcharts of FIGS.

[0073] (Step S304) The credit output unit 141 outputs the credit information acquired in step S303. Return to step S301. The credit output unit 141 transmits the credit information to the terminal device 2, for example.

[0074] (Step S305) The person information receiving unit 121 determines whether or not an appraisal instruction including a person image has been received. If an appraisal instruction has been received, the process proceeds to step S306; if not, the process returns to step S301. Here, the receiving unit 12 determines whether or not an appraisal instruction has been received from, for example, the terminal device 2.

[0075] (Step S306) The attribute value acquisition unit 131 acquires one or more person attribute values ​​paired with the user identifier corresponding to the assessment instruction accepted in step S305 from the user management unit 112. The credit acquisition unit 132 acquires image-related information having one or more person attribute values ​​and a person image included in the assessment instruction.

[0076] (Step S307) The credit acquisition unit 132 acquires credit information using the image-related information acquired in step S306. An example of such credit acquisition processing will be described with reference to the flowcharts of FIGS.

[0077] (Step S308) The processing unit 13 or an assessment unit (not shown) determines whether the credit information satisfies the assessment conditions. If the assessment conditions are met, the process proceeds to step S309, and if not, the process proceeds to step S312.

[0078] The appraisal conditions are conditions for appraising a product. For example, the appraisal conditions are that the reliability indicated by the credit information is equal to or greater than a threshold. For example, the appraisal conditions are that the credit information is "OK (reliable)". For example, the appraisal conditions are that the default probability (an example of credit information) is less than or equal to a threshold.

[0079] (Step S309) The processing unit 13 or an assessment unit (not shown) acquires a product image. Here, the processing unit 13 acquires, for example, a product image included in the assessment instruction.

[0080] (Step S310) The processing unit 13 or an appraisal unit (not shown) uses the product image acquired in step S309 to acquire the appraised price of the product. The processing unit 13, for example, assigns the product image and the learning model to a machine learning prediction module, executes the prediction module, and acquires the appraised price. The learning model here is a model for outputting the appraised price. The learning model here is a model acquired by providing two or more sets of training data containing the product image and the appraised price to a machine learning learning module, executing the learning module. Note that the machine learning algorithm may be, for example, deep learning, random forest, or SVR, but is not critical. The processing unit 13 may also, for example, send the product image to the appraiser's terminal and receive the appraised price from the appraiser's terminal. The method for acquiring the appraised price is not critical.

[0081] (Step S311) The output unit 14 outputs the assessed amount, etc. acquired in step S310. Return to step S301. The output unit 14, for example, transmits the assessed amount, etc. to the terminal device 2. The assessed amount, etc., is, for example, the assessed amount and credit information, but may be just the assessed amount.

[0082] (Step S312) The processing unit 13 or an assessment unit (not shown) receives an error message indicating that assessment is not possible.

[0083] (Step S313) The output unit 14 outputs an error message. Return to step S301. The output unit 14 transmits the error message to the terminal device 2, for example.

[0084] In the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt occurs to end the process.

[0085] Next, a first example of the credit acquisition process in steps S303 and S307 will be described with reference to the flowchart in FIG.

[0086] (Step S401) The characteristic value acquisition means 1321 acquires a person image included in the image-related information. The characteristic value acquisition means 1321 acquires, for example, one or more person attribute values. The characteristic value acquisition means 1321 provides the person image, or the person image and one or more person attribute values, to a first learning model.

[0087] (Step S402) The characteristic value acquisition means 1321 determines whether or not an answer has been acquired from the first learning model. If an answer has been acquired, the process proceeds to step S403, and if not, the process returns to step S402.

[0088] The first learning model is a model that receives a person image, or a person image and one or more person attribute values ​​as input, and outputs two or more personality trait values. The first learning model is, for example, a CLIP model.

[0089] (Step S403) The characteristic value acquiring means 1321 acquires two or more personality characteristic values ​​from the acquired answers.

[0090] (Step S404) The credit acquisition means 1322 acquires one or more personal attribute values.

[0091] (Step S405) The credit acquisition means 1322 acquires a group of explanatory variables having two or more personality trait values. The group of explanatory variables has two or more personality trait values, or two or more personality trait values ​​and one or more person attribute values.

[0092] (Step S406) The credit acquisition means 1322 provides the group of explanatory variables to the second learning model.

[0093] (Step S407) The credit acquisition means 1322 determines whether or not a response has been obtained from the second learning model. If a response has been obtained, the process proceeds to step S408; if not, the process returns to step S407.

[0094] The second learning model is a model that takes two or more personality trait values, or two or more personality trait values ​​and one or more person attribute values, as input, and outputs credit information.

[0095] (Step S408) The credit acquisition means 1322 acquires the credit information contained in the response acquired in step S407, and returns to the upper processing.

[0096] Next, a second example of the credit acquisition process in steps S303 and S307 will be described with reference to the flowchart in FIG.

[0097] (Step S501) The credit acquisition unit 132 acquires a person image included in the image-related information. The credit acquisition unit 132 acquires, for example, one or more person attribute values. The credit acquisition unit 132 provides the person image, or the person image and one or more person attribute values, to a learning model.

[0098] The learning model is a model that takes a person image, or a person image and one or more person attribute values, as input and outputs credit information.

[0099] (Step S502) The credit acquisition unit 132 determines whether or not an answer has been acquired from the learning model. If an answer has been acquired, the process proceeds to step S503, and if not, the process returns to step S502.

[0100] (Step S503) The credit acquisition unit 132 acquires the credit information included in the response acquired in step S502, and returns to the upper-level processing.

[0101] Next, a third example of the credit acquisition process in steps S303 and S307 will be described using the flowchart in Fig. 6. In the flowchart in Fig. 6, the description of the same steps as in the flowchart in Fig. 4 will be omitted.

[0102] (Step S601) The credit acquisition means 1322 acquires credit information using a group of explanatory variables. The group of explanatory variables is, for example, a vector whose elements are two or more personality characteristic values ​​acquired by the characteristic value acquisition means 1321. The group of explanatory variables is, for example, a vector whose elements are two or more personality characteristic values ​​acquired by the characteristic value acquisition means 1321 and one or two or more person attribute values.

[0103] Here, the credit acquisition means 1322 acquires the credit information using an arithmetic expression or a correspondence table, each of which will be described below. (1) Method using an arithmetic formula

[0104] The credit acquisition means 1322 calculates credit information by substituting each of the two or more personality characteristic values ​​acquired by the characteristic value acquisition means 1321 into an arithmetic expression. The arithmetic expression may be different for each condition using one or two or more person attribute values.

[0105] The calculation formula is an increasing function with one or more of the following parameters: trustworthiness, risk-taking, achievement motivation, calmness, modesty, organization, planning, diligence, easygoing, open-mindedness, shyness, impulsiveness, intelligence, open-mindedness, honesty, and cooperativeness. The calculation formula is a decreasing function with one or more of the following parameters: aggressiveness, cunning, extroversion, carelessness, emotionality, inquisitiveness, sociability, laziness, and neuroticism. (2) Using a correspondence table

[0106] The credit acquisition means 1322 acquires, for example, from the correspondence table, the credit information paired with the vector having the greatest similarity to the group of explanatory variables, which are vectors.

[0107] The credit acquisition means 1322, for example, acquires from a correspondence table credit information paired with two or more vectors whose similarity to a group of explanatory variables, which are vectors, satisfies a condition (for example, the similarity is equal to or greater than a threshold), and acquires a representative value (for example, the average value, the weighted average value based on the similarity, or the median value) of the two or more credit information.

[0108] Next, an example of the operation of the terminal device 2 will be described with reference to the flowchart of FIG.

[0109] (Step S701) The terminal reception unit 22 determines whether or not an output instruction has been received. If an output instruction has been received, the process proceeds to step S702, and if not, the process proceeds to step S704.

[0110] (Step S702) The device processing unit 23 acquires a person image, etc. Here, the device processing unit 23 may acquire the person image by photographing it, or may acquire the person image from the device storage unit 21. The person image, etc. is, for example, a person image, or a person image and one or more person attribute values.

[0111] (Step S703) The terminal transmitting unit 24 transmits an output instruction having an image of a person or the like to the information processing device 1. The process returns to step S701.

[0112] (Step S704) The terminal reception unit 22 determines whether or not an assessment instruction has been received. If an assessment instruction has been received, the process proceeds to step S705, and if not, the process proceeds to step S710.

[0113] (Step S705) The device processing unit 23 acquires a product image. Here, the device processing unit 23 may photograph and acquire the product image, or may acquire the product image from the device storage unit 21.

[0114] (Step S706) The device processing unit 23 acquires a person image, etc. Here, the device processing unit 23 may acquire the person image by photographing it, or may acquire the person image from the device storage unit 21. The person image, etc. is, for example, a person image, or a person image and one or more person attribute values.

[0115] (Step S707) The terminal transmitting unit 24 transmits an appraisal instruction including a product image, a person image, and the like to the information processing device 1.

[0116] (Step S708) The terminal receiving unit 25 determines whether or not information has been received from the information processing device 1. If information has been received, the process proceeds to step S709, and if information has not been received, the process returns to step S708. The information may be, for example, an appraisal result. The appraisal result may be, for example, an appraised amount.

[0117] (Step S709) The terminal output unit 26 outputs the information received in step S708. The process returns to step S701.

[0118] (Step S710) The terminal receiving unit 25 determines whether or not credit information, etc. has been received from the information processing device 1. If credit information, etc. has been received, the process proceeds to step S711, and if credit information, etc. has not been received, the process returns to step S701.

[0119] (Step S711) The terminal output unit 26 outputs the credit information etc. received in step S710. The process returns to step S701.

[0120] A specific example of the operation of the information processing device 1 according to this embodiment will be described below. The information processing device 1 performs the process described using the flowchart in FIG.

[0121] Assume now that the personal information receiving unit 121 of the information processing device 1 receives an output instruction including the personal image shown in FIG. 8 from the terminal device 2. The output instruction corresponds to the user identifier of the person in the personal image. The attribute value acquiring unit 131 then acquires one or more personal attribute values ​​paired with the user identifier from the user management unit 112. Next, the credit acquiring unit 132 performs, for example, the process described using the flowchart of FIG. 4. That is, the characteristic value acquiring means 1321 provides the personal image, one or more personal attribute values, and information on two or more personality traits, such as "trustworthiness, aggressiveness, risk-taking, cunningness, motivation to achieve, calmness, modesty, extroversion, organization, and planning," to the first learning model. Then, the characteristic value acquiring means 1321 acquires the personality trait values ​​of the person, such as "trustworthiness, aggressiveness, etc.", from the first learning model. The first learning model is, for example, a CLIP model.

[0122] Next, the credit acquisition means 1322 acquires one or more acquired person attribute values. The credit acquisition means 1322 also acquires a group of explanatory variables having one or more person attribute values ​​and personality characteristic values ​​such as "trustworthiness, aggressiveness, ...". Next, the credit acquisition means 1322 provides the group of explanatory variables to the second learning model. Next, it is assumed that the credit acquisition means 1322 acquires credit information "0.78" from the second learning model.

[0123] Next, the output unit 14 transmits the two or more personality characteristic values ​​and the credit information of the person to the terminal device 2.

[0124] Next, the terminal device 2 receives and outputs the two or more personality characteristic values ​​and the credit information. An example of such output is shown in FIG.

[0125] As described above, according to this embodiment, credit information of a person can be easily obtained using a person's image.

[0126] Furthermore, according to this embodiment, appropriate credit information of a person can be easily obtained using a person image and one or more person attribute values.

[0127] The processing in this embodiment may be implemented by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that implements the information processing system A in this embodiment is the following program. That is, this program causes a computer to function as a personal information receiving unit that receives personal information including a personal image, a credit acquisition unit that provides image-related information based on the personal image received by the personal information receiving unit to a learning model and acquires credit information, which is information regarding the credit of the person, and a credit output unit that outputs the credit information acquired by the credit acquisition unit.

[0128] FIG. 10 is a block diagram of a computer system 300 that executes the programs described in this specification to realize the information processing device 1 and the like according to the various embodiments described above.

[0129] In FIG. 10, computer system 300 includes computer 301 including a CD-ROM drive, keyboard 302, mouse 303, and monitor 304.

[0130] 10, computer 301 includes, in addition to CD-ROM drive 3012, MPU 3013, bus 3014 connected to CD-ROM drive 3012 etc., ROM 3015 for storing programs such as a boot-up program, RAM 3016 connected to MPU 3013 for temporarily storing instructions of application programs and providing temporary storage space, and hard disk 3017 for storing application programs, system programs, and data. Although not shown here, computer 301 may further include a network card for providing connection to a LAN.

[0131] A program that causes computer system 300 to execute the functions of information processing device 1 and the like of the above-described embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and then transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 when executed. The program may also be loaded directly from CD-ROM 3101 or the network.

[0132] The program does not necessarily include an operating system (OS) or a third-party program that causes the computer 301 to execute the functions of the information processing device 1 of the above-described embodiment. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve desired results. How the computer system 300 operates is well known, and a detailed description thereof will be omitted.

[0133] In addition, in the above program, the steps of transmitting information and receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmission step (processing that can only be performed by hardware).

[0134] The computer that executes the program may be a single computer or a plurality of computers, that is, it may perform centralized processing or distributed processing.

[0135] Furthermore, in each of the above embodiments, it goes without saying that two or more communication means present in one device may be physically realized by one medium.

[0136] Furthermore, in each of the above embodiments, each process may be realized by centralized processing in a single device, or may be realized by distributed processing in a plurality of devices.

[0137] The present invention is not limited to the above-described embodiment, and various modifications are possible, and it goes without saying that these modifications are also included within the scope of the present invention. [Industrial Applicability]

[0138] As described above, the information processing device according to the present invention has an effect of being able to easily obtain credit information of a person using an image of the person, and is useful as an information processing device or the like. [Explanation of symbols]

[0139] A. Information Processing System 1. Information processing equipment 2. Terminal Device 3 Model Management Device 11 Storage area 12 Reception 13 Processing section 14 Output section 21 Terminal storage section 22 Terminal Reception 23 Terminal processing section 24 Terminal transmitter 25 Terminal receiving section 26 Terminal Output Unit 111 Model storage area 112 User Management Department 121 Personal Information Reception Department 131 Attribute value acquisition unit 132 Credit acquisition department 141 Credit output unit 1321 Characteristic value acquisition means 1322 Credit acquisition means

Claims

1. a person information receiving unit that receives person information including a person image; a credit acquisition unit that provides image-related information based on the person image accepted by the person information acceptance unit to a learning model and acquires credit information that is information regarding the credit of the person based on the person image; a credit output unit that outputs the credit information acquired by the credit acquisition unit, The credit acquisition unit a characteristic value acquiring means for acquiring, using the person image accepted by the person information accepting unit, two or more personality characteristic values ​​which are scores of characteristics related to the person's personality based on the person image; a credit acquisition means for acquiring the credit information using the two or more personality characteristic values ​​acquired by the characteristic value acquisition means, The credit acquisition means A group of explanatory variables including the two or more personality trait values ​​acquired by the characteristic value acquisition means is provided to a second learning model, and the credit information is acquired from the second learning model; or Substituting a group of explanatory variables including the two or more personality trait values ​​acquired by the characteristic value acquisition means into an arithmetic formula for calculating credit information by substituting a group of explanatory variables including each of the two or more personality trait values, and calculating credit information; or From a correspondence table having two or more records each having a group of explanatory variables that are vectors and credit information, obtain credit information that pairs with a vector having the greatest similarity to the group of explanatory variables including the two or more personality trait values ​​obtained by the characteristic value obtaining means, and obtain the credit information; or An information processing device that acquires credit information that pairs with two or more vectors whose similarity to a group of explanatory variables including the two or more personality trait values ​​acquired by the characteristic value acquisition means is equal to or greater than a threshold from a correspondence table having two or more records each having a group of explanatory variables that are vectors and credit information, and acquires the credit information that is a representative value of the two or more credit information.

2. The characteristic value acquisition means The information processing device described in claim 1, wherein the person image received by the person information receiving unit is provided to a first learning model, and two or more personality characteristic values, which are scores of characteristics related to the person's personality based on the person image, are obtained from the first learning model.

3. Further comprising an attribute value acquisition unit that acquires one or more person attribute values ​​of a person based on the person image, The credit acquisition means The information processing apparatus according to claim 1 , wherein the credit information is acquired using the group of explanatory variables including the two or more personality characteristic values ​​and the one or more person attribute values.

4. An information processing method for causing a computer to execute all of the processes executed by the information processing device according to any one of claims 1 to 3.

5. Computer, A program for causing the information processing device according to any one of claims 1 to 3 to function as the information processing device.

Images