Mentoring system

Abstract

To provide a mentoring system which performs mentoring for a user by generating information for use in generative AI input suitable for mentoring without the user intervention.SOLUTION: A mentoring system 1 according to the present invention has a prompt template DB 121 for storing a template of prompt for input to a generative AI 21, a mentor attribute DB 122 for storing mentor characteristic information as text data indicating attributes and personal characteristics of a mentor with respect to each mentor, and a control unit for reading the selected template from the prompt template DB 121 to read the mentor characteristic information of the selected mentor from the mentor attribute DB 122, applying it to the read-out template to generate the prompt, and transmitting the generated prompt to a generative AI server 2 having the generative AI 21, and receiving output data output from the generative AI through processing of the prompt to transmit it to a user terminal 4.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a mentoring system. [Background technology]

[0002] It is common for someone with experience in a certain field to act as a mentor and provide consultation and advice to someone with less experience in that field.

[0003] However, since mentors are human, the content of their advice may change from day to day, or they may give advice that is inappropriate for the personality of the user receiving the advice.

[0004] In this regard, techniques have been proposed that select a trained model that matches the user's personality, generate advice from this trained model, and provide the advice to the user in an expression that matches the user's personality (e.g., Patent Document 1, especially (Prevailing Example 3)), as well as a technique that quantifies the user's characteristics and changes the content of instruction based on this numerical value (e.g., Patent Document 2).

[0005] Recently, it has become possible to generate advice using so-called generative AI, which allows for more flexible advice tailored to the individual user's personality.

[0006] However, generative AI requires a certain level of skill to output the desired information based on the input information. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 6550603 [Patent Document 2] JP 2006-64880 A Summary of the Invention [Problem to be solved by the invention]

[0008] The problem to be solved by the present invention is to provide a mentoring system that generates information for input to a generation AI suitable for mentoring without user intervention and mentors the user. [Means for solving the problem]

[0009] The present invention provides a mentoring system comprising: a prompt template database storing prompt templates to be input to a generation AI; a mentor attribute database storing mentor personality information for each mentor, which is text data indicating the attributes and individual personality of the mentor; and a control unit that reads the template selected from the prompt template database, generates the prompt by reading and applying the mentor personality information of the mentor selected from the mentor attribute database to the read template, transmits the generated prompt to a generation AI server comprising the generation AI, receives output data output by the generation AI after processing the prompt, and transmits it to a user terminal. Effect of the Invention

[0010] According to the present invention, it is possible to provide a mentoring system that generates information suitable for mentoring and used for inputting to a generation AI without user intervention, and mentors the user. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing a configuration of a mentoring system according to a first embodiment. [Diagram 2] FIG. 2 is a diagram showing the data structure of a prompt template database. [Diagram 3] FIG. 13 is a diagram illustrating a data configuration of a mentor attribute database. [Figure 4]FIG. 4 is a diagram showing a data structure of a user data database. [Diagram 5] 13 is a flowchart showing a mentoring operation. [Figure 6] FIG. 13 is a block diagram showing a configuration of a mentoring system according to a second embodiment. [Figure 7] 13 is a flowchart showing a trained model generation operation. [Figure 8] 13 is a flowchart showing teacher prompt generation and the learning process operation of the generation AI. [Figure 9] 13 is a flowchart showing a mentoring operation of the mentoring system according to the fourth embodiment. [Figure 10] FIG. 13 is a diagram showing an example of a rubric. [Figure 11] FIG. 13 illustrates an example template of a prompt that includes a request for suitable job suggestions. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] A mentoring system according to an embodiment of the present invention will be described in detail below.

[0013] (Generation AI) This embodiment uses a so-called generation AI, so this generation AI will be explained first.

[0014] In general, in a conventional trained model, explanatory variables and objective variables are clearly separated and structured, and the structured data is used as training data to train the model. For example, when the model is a so-called neural network, a model that inputs explanatory variables and outputs a desired objective variable is selected, and this model is adopted as the trained model.

[0015] When a trained model generated in this way receives inputs that are somewhat similar to the explanatory variables of the training data, it outputs processing results that are somewhat similar to the objective variables of the training data. However, it cannot be expected to create, or generate, processing results that are different from the objective variables of the training data.

[0016] In contrast, if a model is trained using unstructured training data as described above, it becomes possible to obtain processing results that are somewhat creative.

[0017] For example, if a model is trained using hundreds of thousands to millions of question and answer combinations from a conventional consultation chat using a chat function via the Internet, where humans are the answerers, the trained model will respond very smoothly. When this trained model is then "requested" to create something, it will output or generate a processing result that is somewhat creative. The text data indicating this "request" is called a prompt.

[0018] In this way, trained models that generate somewhat creative processing results are now called generative AI. Such generative AI can generate program code, images, videos, audio, etc., and generative AI that generates text data, such as ChatGPT, is becoming increasingly popular.

[0019] ChatGPT is already in commercial operation, and the vendor provides an API (Application Programming Interface), making it easy to use.

[0020] (First embodiment) Fig. 1 is a block diagram showing the configuration of a mentoring system 1 according to a first embodiment. As shown in Fig. 1, the mentoring system 1 can be realized using a so-called server device or a computer. The mentoring system 1 includes a control unit 11, a storage unit 12, an input / output unit 13, and a communication unit 14.

[0021] The control unit 11 includes a calculation device such as a CPU (central processing unit), etc. The control unit 11 sequentially reads out and executes the programs stored in the storage unit 12.

[0022] The storage unit 12 includes a storage device such as a memory, an SSD (Solid State Drive), a hard disk drive, etc. The storage unit 12 stores a prompt template DB 121 (database is referred to as DB), a mentor attribute DB 122, an API specification DB 123 for each generated AI, a user data DB 124, and an interface DB 125.

[0023] The generation AI-specific API specification DB 123 stores the API specifications set for each generation AI 21 of the generation AI server 2 .

[0024] The interface DB 125 stores the specifications of the data to be displayed on the user terminal 4 .

[0025] The input / output unit 13 includes input / output devices such as a mouse, a keyboard, and a display.

[0026] The communication unit 14 includes a communication device that can be connected to a public communication network such as the Internet 3. The mentoring system 1 is connected to a generation AI server 2 including a generation AI and a user terminal 4 via the communication unit 14.

[0027] Fig. 2 is a diagram showing a data configuration of the prompt template DB 121. As shown in Fig. 2, the prompt template DB 121 stores a template ID, which is an identifier uniquely assigned to each template, a prompt purpose indicating the purpose for which the prompt is used, role setting information, which is information setting the role played by the generation AI, a mentor personality information FILLER indicating an area for storing information indicating the personality of the mentor, goal setting information indicating a goal described in the request content of the prompt, and flow setting information indicating the procedure of communication between the generation AI and the user terminal and the procedure of information processing to be performed by the generation AI.

[0028] Fig. 3 is a diagram showing the data configuration of the mentor attribute DB 122. As shown in Fig. 3, the mentor attribute DB 122 stores a mentor ID, which is identification information uniquely assigned to each mentor, and mentor personality information, which is text data indicating the attributes and personality of the mentor as a person.

[0029] Mentor personality information may include, for example, text data that indicates the mentor's attributes and personality as a person, or the mentor's character, such as date of birth, self-introduction, likes and dislikes, position in family, people who have influenced them, position at work, favorite subjects, favorite songs, reasons why they like their favorite songs, how they spent their time after school in high school, things they have continued to do, and things they have explored up to now.

[0030] Fig. 4 is a diagram showing a data configuration of the user data DB 124. As shown in Fig. 4, the user data DB 124 stores a user ID which is an identifier uniquely assigned to a user, user individuality information which is text data indicating the user's attributes and individuality as a person, and a question ID which is an identifier uniquely assigned to each question in a question DB which is provided separately and stores questions, and user answer information which is text data indicating the user's answer to the question.

[0031] User personality information includes, for example, gender, year of school, desired university, desired department, field of greatest interest, experience living abroad, language qualifications acquired, sports experience, music experience, awards won in contests, favorite books or movies, reasons for liking them, and what the user wants to contribute to society in the future, etc., which are text data that indicates the user's attributes and personality as a person or who the user is.

[0032] The questions stored in the question DB include text data, and are designed to reflect the user's attributes and individuality as a person, and to avoid being limited to one or a few answers. Examples of such questions include the questions described in paragraphs

[0029] to

[0038] of Patent No. 7320809, which is related to the applicant's patent application.

[0033] Fig. 5 is a flowchart showing the mentoring operation performed by the control unit 11 of the mentoring system 1. As shown in Fig. 5, in step S1, the user terminal 4 transmits to the mentoring system 1 a mentoring request indicating a request for various consultations to a mentor.

[0034] In step S2, the control unit 11 executes a mentor presentation process, which is a process of transmitting a list of mentors who will provide mentoring to the user terminal. Specifically, the control unit 11 reads out all or a part of the mentor IDs and mentor individual information from the mentor attribute DB 122, generates data that lists the mentor IDs and mentor individual information in a viewable state on the user terminal 4, and transmits the data to the user terminal 4.

[0035] In step S3, the user terminal 4 transmits to the mentoring system 1 the mentor ID of the mentor selected by the user.

[0036] In step S4, the control unit 11 reads, based on the mentor ID received from the user terminal 4, from the mentor attribute DB 122, the mentor individual information of the corresponding mentor ID.

[0037] In step S5, the control unit 11 generates a prompt. Specifically, the control unit 11 first reads out a template ID and a prompt use from the prompt template DB 121, and transmits them to the user terminal 4 in a viewable manner. Next, the control unit 11 reads out a template from the prompt template DB 121 based on the prompt ID corresponding to the prompt use selected by the user from the user terminal 4. Then, the control unit 11 sets the mentor personality information in the mentor personality information FILLER to generate a prompt, and stores the prompt in the memory of the storage unit 12.

[0038] In step S6, the control unit 11 reads out the API specifications of the generated AI to be used from the generated AI-specific API specification DB 123, and modifies the prompt in the memory as necessary based on the use of this API.

[0039] In step S 7 , the control unit 11 reads the prompt from the memory and transmits it to the generation AI server 2 .

[0040] In step S8, the generation AI server 2 receives the prompt from the mentoring system 1, processes the received prompt appropriately, and then inputs the prompt to the generation AI, thereby causing the generation AI to start processing.

[0041] In step S 9 , the generation AI server 2 generates a response sentence that is the result of processing based on the prompt, and transmits it to the mentoring system 1 .

[0042] In step S10, the control unit 11 transmits the received response message to the user terminal 4.

[0043] In step S11, if the received response text includes an instruction to input additional information, the user terminal 4 inputs the additional information and transmits it to the mentoring system 1.

[0044] The mentoring system 1 mediates data exchange between the generation AI server 2 and the user terminal 4 as many times as necessary, such as step S9 → step S10 → step S11 → step S10 → step S9.

[0045] In step S 12 , the generation AI 21 generates an answer sentence, and the generation AI server 2 transmits this answer sentence to the mentoring system 1 .

[0046] In step S13, the control unit 11 transmits the answer text to the user terminal 4.

[0047] In step S14, the user terminal 4 receives the reply text, displays it on a display unit such as a display that the user terminal 4 has, and ends the process.

[0048] As described above, the mentoring system 1 of this embodiment comprises a prompt template DB121 that stores prompt templates to be input to the generation AI21, a mentor attribute DB122 that stores mentor personality information for each mentor, which is text data that indicates the mentor's attributes and personality as a person, and a control unit that reads a template selected from the prompt template DB121, generates a prompt by reading and applying the mentor personality information of the mentor selected from the mentor attribute DB122 to the read template, transmits the generated prompt to the generation AI server 2 that comprises the generation AI21, receives output data output by the generation AI after processing the prompt, and transmits it to the user terminal 4.

[0049] Therefore, according to the first embodiment of the present invention, it is possible to provide a mentoring system 1 that generates a prompt, which is information for input to the generation AI suitable for mentoring, without user intervention, inputs this prompt into the generation AI 21, and transmits the output data generated by the generation AI 21 after processing the prompt to the user terminal 4, thereby providing mentoring to the user.

[0050] Second Embodiment In the mentoring system 1 of the first embodiment, mentor personality information is stored in advance in the mentor attribute DB 122. In contrast, the mentoring system 1 of the present embodiment creates training data including mentor personality information as an explanatory variable and information identifying a mentor such as a mentor ID as a target variable, causes the external AI 51, which is a model of the external AI server 5, to learn this training data to generate a trained model, inputs user personality information and user answer information into this trained model, and outputs information identifying a mentor suitable for the user, thereby eliminating the need for the user to select a mentor.

[0051] Fig. 6 is a block diagram showing the configuration of a mentoring system 1 according to the second embodiment. As shown in Fig. 6, the mentoring system 1 can be realized using a so-called server device or a computer. The mentoring system 1 includes a control unit 11, a storage unit 12, an input / output unit 13, and a communication unit 14.

[0052] The control unit 11 includes a calculation device such as a CPU, etc. The control unit 11 sequentially reads out and executes the programs stored in the storage unit 12.

[0053] The storage unit 12 includes a storage device such as a memory, an SSD, a hard disk drive, etc. The storage unit 12 stores a prompt template DB121, a mentor attribute DB122, a generated AI-specific API specification DB123, a user data DB124, and an interface DB125.

[0054] The generation AI-specific API specification DB 123 stores the API specifications set for each generation AI 21 of the generation AI server 2 .

[0055] The interface DB 125 stores the specifications of the data to be displayed on the user terminal 4 .

[0056] The input / output unit 13 includes input / output devices such as a mouse, a keyboard, and a display.

[0057] The communication unit 14 includes a communication device that can be connected to a public communication network such as the Internet 3. The mentoring system 1 is connected to a generation AI server 2 including a generation AI, an external AI server 5 including an external AI 51, and a user terminal 4 via the communication unit 14.

[0058] The data structures of the prompt template DB121, mentor attribute DB122, API specification for each generated AI DB123, user data DB124, and interface DB125 in this embodiment are similar to the data structures of the prompt template DB121, mentor attribute DB122, API specification for each generated AI DB123, user data DB124, and interface DB125 in the first embodiment.

[0059] Fig. 7 is a flowchart showing a trained model generating operation performed by the control unit 11 of the mentoring system 1. As shown in Fig. 7, in step S101, the control unit 11 reads several thousand to several tens of thousands of data sets in which information for identifying a mentor and mentor personality information are combined at once.

[0060] In step S102, the control unit 11 creates teacher data including mentor individuality information as an explanatory variable and information for identifying the mentor, such as a mentor ID, as a target variable.

[0061] In step S103, the control unit 11 transmits the created teacher data to the external AI server 5.

[0062] In step S104, the external AI server 5 receives the teacher data.

[0063] In step S105, the external AI server 5 trains a model using the received training data, generates a trained model that outputs information identifying a mentor when user personality information and user answer information are input, and ends the process.

[0064] Next, the mentoring operation in this embodiment will be described. In the mentoring operation performed by the control unit 11 of the mentoring system 1 in this embodiment, step S2 of the mentoring operation performed by the control unit 11 of the mentoring system 1 in the first embodiment is as follows. "In step S2, the control unit 11 executes a mentor presentation process which is a process of transmitting a list of mentors who will provide mentoring to the user terminal. Specifically, the control unit 11 reads out user individuality information and user answer information from the user data DB 124 based on the user ID entered at the time of login by the user who requested mentoring, transmits the information to the external AI server 5 for processing by the external AI 51, generates data which lists the list of mentors output by the external AI 51 as a processing result in a viewable state on the user terminal 4, and transmits the data to the user terminal 4." The processing of steps other than those described above is similar to the mentoring operation of the first embodiment.

[0065] As described above, the mentoring system 1 of this embodiment comprises a prompt template DB121 that stores prompt templates to be input to the generation AI 21, a mentor attribute DB122 that stores mentor personality information for each mentor, which is text data indicating the mentor's attributes and personality as a person, a user data DB124 that stores user personality information and user answer information, which are text data indicating the user's attributes and personality as a person, and a control unit that reads out a template selected from the prompt template DB121, inputs the user personality information and user answer information of the user to be mentored read out from the user data DB124 into a trained model that has been trained in advance to output information identifying the mentor when the user personality information and user answer information are input, transmits the information identifying the mentor output by the trained model to the user terminal 4, generates a prompt by reading out the mentor personality information of the selected mentor received from the user terminal 4 from the mentor attribute DB122 and applying it to the read template, transmits the generated prompt to the generation AI server 2 equipped with the generation AI 21, receives output data output by the generation AI after processing the prompt, and transmits it to the user terminal 4.

[0066] Therefore, according to the second embodiment of the present invention, by having the trained model as the external AI 51 output information identifying a mentor suitable for the user, it is possible to eliminate the need for the user to select a mentor, and it is possible to provide a mentoring system 1 that mentors the user by generating a prompt, which is information for inputting the generation AI suitable for mentoring, without user intervention, inputting this prompt into the generation AI 21, and transmitting the output data output by the generation AI 21 after processing the prompt to the user terminal 4.

[0067] (Third embodiment) In the mentoring system 1 of the first embodiment, mentor personality information is stored in advance in the mentor attribute DB 122. In contrast, the mentoring system 1 of this embodiment creates a teacher prompt including the mentor personality information and information identifying the mentor, has the generation AI 21 of the generation AI server 2 learn this teacher prompt, inputs user personality information and user answer information to this generation AI 21, and outputs information identifying a mentor suitable for the user, thereby eliminating the need for the user to select a mentor.

[0068] The configuration of the mentoring system 1 of this embodiment is similar to the configuration of the mentoring system 1 of the second embodiment.

[0069] Fig. 8 is a flowchart showing teacher prompt generation and the learning process of the generation AI performed by the control unit 11 of the mentoring system 1. As shown in Fig. 8, in step S201, the control unit 11 reads several thousand to several tens of thousands of data sets in which information specifying a mentor and mentor personality information are combined at once.

[0070] In step S202, the control unit 11 creates a teacher prompt including the loaded data set. This teacher prompt includes a request for the generation AI 21 to learn the data set.

[0071] In step S203, the control unit 11 transmits the created teacher prompt to the generation AI server 2.

[0072] In step S204, the generation AI server 2 receives the teacher prompt.

[0073] In step S205, the generation AI server 2 inputs the received teacher prompt to the generation AI 21 for processing, and trains the generation AI 21 so that when user individual information is input, it outputs information identifying a mentor, and then ends the process.

[0074] Next, the mentoring operation in this embodiment will be described. In the mentoring operation performed by the control unit 11 of the mentoring system 1 in this embodiment, step S2 of the mentoring operation performed by the control unit 11 of the mentoring system 1 in the first embodiment is as follows. "In step S2, the control unit 11 executes a mentor presentation process, which is a process of sending a list of mentors who will provide mentoring to the user terminal. Specifically, the control unit 11 reads out user personality information and user answer information from the user data DB 124 based on the user ID entered by the user who requested mentoring at the time of login, sends the information to the generation AI server 2 for processing by the generation AI 21, generates data that lists the list of mentors output by the generation AI 21 as a processing result in a viewable state on the user terminal 4, and sends the data to the user terminal 4."

[0075] The processing of steps other than those described above is similar to the mentoring operation of the first embodiment.

[0076] As described above, the mentoring system 1 of this embodiment comprises a prompt template DB121 that stores prompt templates to be input to the generation AI21, a mentor attribute DB122 that stores mentor personality information for each mentor, which is text data indicating the mentor's attributes and personality as a person, a user data DB124 that stores user personality information and user answer information, which are text data indicating the user's attributes and personality as a person, and a control unit that reads out a template selected from the prompt template DB121, inputs the user personality information and user answer information of the user to be mentored read out from the user data DB124 into the generation AI21 that has been trained to output information identifying the mentor when user personality information and user answer information are input, transmits the information identifying the mentor output by the generation AI21 to the user terminal 4, generates a prompt by reading out the mentor personality information of the selected mentor received from the user terminal 4 from the mentor attribute DB122 and applying it to the read template, transmits the generated prompt to the generation AI server 2 equipped with the generation AI21, receives output data output by the generation AI after processing the prompt, and transmits it to the user terminal 4.

[0077] Therefore, according to the third embodiment of the present invention, by having the generation AI 21 output information identifying a mentor suitable for the user, the user can be further relieved of the trouble of selecting a mentor, and a mentoring system 1 can be provided in which a prompt, which is information for inputting the generation AI suitable for mentoring, is generated without user intervention, the prompt is input to the generation AI 21, and the output data output by the generation AI 21 after processing the prompt is transmitted to the user terminal 4, thereby providing a mentoring system 1 for mentoring the user.

[0078] (Fourth embodiment) In the mentoring systems 1 of the first to third embodiments, the content of mentoring is limited to advice. In contrast, the mentoring system 1 of this embodiment generates a rubric, which is an evaluation standard for evaluating the user's answer, and a prompt including a request to suggest a suitable occupation that is an occupation suitable for the user, and transmits it to the generation AI server 2 for processing by the generation AI 21, and transmits information regarding the suitable occupation for the user, which is the processing result of the generation AI 21, to the user terminal 4. The rubric is stored in a rubric storage unit provided in the storage unit 12.

[0079] The configuration of the mentoring system 1 of this embodiment is similar to the configuration of the mentoring system 1 of the first embodiment.

[0080] 9 is a flowchart showing a mentoring operation performed by the control unit 11 of the mentoring system 1 of the fourth embodiment. As shown in Fig. 9, in step S301, the user terminal 4 transmits a career consultation request to the mentoring system 1, which indicates a request for consultation on a career to a mentor.

[0081] In step S302, the control unit 11 executes a mentor presentation process which is a process of transmitting a list of mentors who will provide mentoring to the user terminal. Specifically, the control unit 11 reads out all or a part of the mentor IDs and the mentor individual information from the mentor attribute DB 122, generates data that lists the mentor IDs and the mentor individual information in a viewable state on the user terminal 4, and transmits the data to the user terminal 4.

[0082] In step S303, the user terminal 4 transmits to the mentoring system 1 the mentor ID of the mentor selected by the user.

[0083] In step S304, the control unit 11 reads, based on the mentor ID received from the user terminal 4, from the mentor attribute DB 122, the mentor individual information of the corresponding mentor ID.

[0084] In step S305, the control unit 11 reads out a rubric from the rubric storage unit.

[0085] In step S306, the control unit 11 generates a prompt. Specifically, the control unit 11 first reads out a template ID and a prompt use from the prompt template DB 121, and transmits them to the user terminal 4 in a viewable manner. Next, the control unit 11 reads out a template from the prompt template DB 121 based on the prompt ID corresponding to the prompt use selected by the user from the user terminal 4. Then, the control unit 11 further reads out the mentor personality information of the selected mentor from the mentor attribute DB 122. Furthermore, the control unit 11 reads out a rubric from the rubric storage unit.

[0086] The control unit 11 sets the read mentor individual information in the mentor individual information FILLER of the template, and further adds the read rubric to the template.

[0087] The control unit 11 generates a prompt for suggesting suitable careers by adding a request to the template to display instructions on the user terminal 4 to encourage the user to input information, to evaluate additional input answers entered by the user from the user terminal 4 using a rubric, and to suggest suitable careers to the user based on the obtained evaluation and additional answers.

[0088] In step S307, the control unit 11 reads out the API specifications of the generated AI to be used from the generated AI-specific API specification DB 123. Based on this API specification, the control unit 11 modifies the suitable career suggestion prompt in the memory as necessary, and generates a suitable career suggestion prompt for sending.

[0089] In step S308, the control unit 11 reads out the suitable job suggestion prompt for transmission from the memory and transmits it to the generation AI server 2 including the generation AI 21.

[0090] In step S309, the generation AI server 2 receives the prompt from the mentoring system 1, and after appropriately processing the received prompt, inputs it to the generation AI, thereby causing the generation AI to start processing.

[0091] In step S310, the generation AI server 2 generates a response sentence that is the result of processing performed by the generation AI 21 based on the suitable job suggestion prompt to be sent, and sends this response sentence to the mentoring system 1.

[0092] In step S311, the control unit 11 transmits the received response message to the user terminal 4.

[0093] In step S312, if the received response text includes an instruction to input additional information, the user terminal 4 inputs the additional information and transmits it to the mentoring system 1.

[0094] The mentoring system 1 mediates data exchange between the generation AI server 2 and the user terminal 4 as many times as necessary, such as step S310 → step S311 → step S312 → step S311 → step S310.

[0095] In step S 313 , the generation AI 21 generates an answer sentence, and the generation AI server 2 transmits this answer sentence to the mentoring system 1 .

[0096] In step S314, the control unit 11 transmits the answer text to the user terminal 4.

[0097] In step S315, the user terminal 4 receives the reply text, displays it on a display unit such as a display that the user terminal 4 has, and ends the process.

[0098] FIG. 10 is a diagram showing an example of a rubric. As shown in FIG. 10, the rubric includes evaluation criteria for evaluating a text written by a user for each index, which is an evaluation viewpoint, points that convert the evaluation result into a numerical value corresponding to the evaluation result of the text written by the user evaluated by the evaluation criteria, and the meaning indicated by the numerical value of the points. Each index includes an evaluation viewpoint such as "insight" and an evaluation criterion that represents the qualitative evaluation standard of the index by text data. The number of indexes can be appropriately selected from about 5 to 20.

[0099] Fig. 11 is a diagram showing an example of a prompt template including a request for suitable job suggestions. As shown in Fig. 11, the prompt template including a request for suitable job suggestions describes in the goal setting information that the goal is to suggest suitable jobs, and describes in the flow setting information, by text data, a request for the user to input additional information and a request for suitable job suggestions in consideration of the input additional information.

[0100] As described above, the mentoring system 1 of this embodiment includes a prompt template DB 121 that stores a template ID and a prompt purpose for each prompt template to be input to the generation AI 21, a mentor attribute DB 122 that stores, for each mentor, mentor personality information which is text data indicating the attributes and personality of the mentor as a person, a rubric storage unit that stores, for each index which is an evaluation viewpoint, a rubric including evaluation criteria for evaluating a text written by a user and points for converting the evaluation result into a numerical value corresponding to the evaluation result obtained by evaluating the text written by the user based on the evaluation criteria, and a rubric storage unit that reads out the template ID and prompt purpose from the prompt template DB 121 and transmits them to the user terminal 4 in a viewable manner, and reads out a template from the prompt template DB 121 based on the prompt ID corresponding to the prompt purpose selected by the user from the user terminal 4, and transmits the template ID and prompt purpose selected from the mentor attribute DB 122 to the mentor attribute DB 122. a control unit that reads out the mentor personality information of the user terminal 4, reads out a rubric from the rubric memory unit, reads out the API specifications of the generation AI to be used from the generation AI-specific API specification DB123, adds the read out mentor personality information to the mentor personality information FILLER of the template, and further adds the read out rubric to the template, generates a sending prompt by further adding to the template requests to display on the user terminal 4 instructions to prompt the user for input, to evaluate an additional input answer entered by the user from the user terminal 4 using the rubric, and to suggest a suitable occupation for the user based on the obtained evaluation and the additional input answer, modifies the sending prompt in the memory based on the read out API specifications, sends the modified sending prompt to the generation AI server 2 having the generation AI 21, receives output data generated by processing the sending prompt received by the generation AI and outputting it from the generation AI server 2, and sends it to the user terminal 4.

[0101] Therefore, according to the fourth embodiment of the present invention, a mentoring system 1 can be provided that generates, without user intervention, a transmission prompt that includes information for input to a generation AI suitable for mentoring, a rubric that is an evaluation standard for evaluating the user's answer, and a request to suggest a suitable occupation for the user, and inputs this transmission prompt into the generation AI 21, and transmits the output data output by the generation AI 21 after processing the transmission prompt to the user terminal 4, thereby providing a mentoring system 1 that mentors a user. [Explanation of symbols]

[0102] 1. Mentoring System 2. Generative AI Server 3. Internet 4. User terminal 5 External AI Server 11 Control section 12 Storage section 13 Input / output section 14 Communications Department 51 External AI 21 Generation AI 51 External AI 121 Prompt Template DB 122 Mentor Attribute DB 123 API specification database for each generation AI 124 User Data DB 125 Interface DB

Claims

1. A prompt template database that stores templates of prompts to be input to the generation AI; a mentor attribute database for storing mentor personality information, which is text data indicating the attributes and personal characteristics of each mentor; a control unit that reads the template selected from the prompt template database, reads and applies the mentor personality information of the mentor selected from the mentor attribute database to the read template to generate the prompt, transmits the generated prompt to a generation AI server having the generation AI, receives output data output by the generation AI after processing the prompt, and transmits the output data to a user terminal; A mentoring system comprising:

2. A prompt template database that stores templates of prompts to be input to the generation AI; a mentor attribute database for storing mentor personality information, which is text data indicating the attributes and personal characteristics of each mentor; A user data database that stores user individuality information, which is text data indicating a user's attributes and individuality as a person; a control unit that reads the template selected from the prompt template database, inputs the user personality information of the user to be mentored read from the user data database into a trained model that has been trained in advance to output information identifying the mentor when the user personality information is input, transmits the information identifying the mentor output by the trained model to a user terminal, generates the prompt by reading the mentor personality information of the selected mentor received from the mentor attribute database and applying it to the read template, transmits the generated prompt to a generation AI server equipped with the generation AI, receives output data output by the generation AI after processing the prompt, and transmits it to the user terminal; A mentoring system comprising:

3. The user individual information is The mentoring system of claim 2 including user response information.

4. A prompt template database that stores templates of prompts to be input to the generation AI; a mentor attribute database for storing mentor personality information, which is text data indicating the attributes and personal characteristics of each mentor; A user data database that stores user individuality information, which is text data indicating a user's attributes and individuality as a person; a control unit that reads the selected template from the prompt template database, inputs the user personality information of the user to be mentored read from the user data database into the generation AI that has been previously trained to output information identifying the mentor when the user personality information is input, transmits the information identifying the mentor output by the generation AI to a user terminal, generates the prompt by reading the mentor personality information of the selected mentor received from the mentor attribute database and applying it to the read template, transmits the generated prompt to a generation AI server equipped with the generation AI, receives output data output by the generation AI after processing the prompt, and transmits it to the user terminal; A mentoring system comprising:

5. The user individual information is The mentoring system of claim 4 including user response information.

6. A prompt template database that stores a template ID and a prompt purpose for each prompt template to be input to the generation AI; a mentor attribute database for storing mentor personality information, which is text data indicating the attributes and personal characteristics of each mentor; a rubric storage unit that stores a rubric including evaluation criteria for evaluating a text written by a user for each index, which is an evaluation viewpoint, and points that convert the evaluation result into a numerical value corresponding to the evaluation result of the text written by the user based on the evaluation criteria; read out a template ID and a prompt purpose from the prompt template database and transmit them to a user terminal in a viewable manner, read out the template from the prompt template database based on a prompt ID corresponding to the prompt purpose selected by the user from the user terminal, read out the mentor personality information of the selected mentor from the mentor attribute database, and read out the rubric from the rubric storage unit; add the retrieved mentor personality information and the retrieved rubric to the template, and generate a transmission prompt by further adding to the template a request to display an instruction on the user terminal to prompt the user to input information, to evaluate an additional input answer input by the user from the user terminal using the rubric, and to suggest a suitable occupation for the user based on the obtained evaluation and the additional input answer; Transmitting the generated sending prompt to a generation AI server having the generation AI; A control unit that processes the prompt received by the generating AI and outputs output data, receives the output data from the generating AI server, and transmits the output data to the user terminal; A mentoring system comprising:

Images