Occupation proposal system

Abstract

Upon reception of an occupation proposal request from a user device 2, an occupation proposal system (1) causes an existing trained model to generate a question sentence to ask the user about their "gender" and "specialty" in a writing style that is designated in advance, and outputs the question sentence generated by the existing trained model to the user device (2). Upon reception of an answer to the question sentence from the user device (2), the occupation proposal system (1) causes the existing trained model to generate, on the basis of the "gender" and "specialty" of the user included in the answer, information regarding an imaginary occupation in an occupational field that is designated in advance, and outputs the information regarding the imaginary occupation generated by the existing trained model to the user device (2).

Description

Occupation Proposal System

[0001] The present invention relates to an occupation proposal system that proposes fictional occupations to users.

[0002] Conventionally, various educational devices have been proposed. For example, a system that provides educational content for career formation has been proposed for working people or those who are about to become working people (see, for example, Japanese Unexamined Patent Application Publication No. 2023-168217).

[0003] According to this conventional device, as part of career formation, it is possible to provide educational content that can be recommended based on the user's desired occupation interest area, or to provide an "image of what one wants to be" derived from the educational content viewed by the user.

[0004] However, in the above-described conventional device, it is not assumed to propose fictional occupations (new occupations in the future). In particular, no system has been proposed that can propose a fictional occupation (new occupation in the future) that makes use of what the user is good at in a pre-specified occupation field (for example, the nursing field).

[0005] The present invention has been made under the above background. An object of the present invention is to provide an occupation proposal system that can propose a fictional occupation that makes use of what the user is good at in a pre-specified occupation field.

[0006] One aspect of the present invention is an occupation proposal system that proposes fictional occupations to users. When this occupation proposal system receives an occupation proposal request from a user, it causes an existing learning model to generate a question sentence for asking the user about their gender and what they are good at in a pre-specified style, a question answer processing unit that outputs the question sentence generated by the existing learning model to the user and receives an answer to the question sentence input by the user, a fictional occupation generation unit that causes the existing learning model to generate information on fictional occupations in a pre-specified occupation field based on the user's gender and what they are good at included in the answer from the user, and a user proposal processing unit that outputs the information on the fictional occupations generated by the existing learning model to the user.

[0007] Another aspect of the present invention is a method implemented in a job suggestion system that suggests fictitious occupations to a user, the method comprising: receiving a job suggestion request from a user, causing an existing learning model to generate a question in a predetermined style that asks the user about their gender and areas of expertise; outputting the question generated by the existing learning model to the user and receiving the user's answer to the question; causing the existing learning model to generate information about fictitious occupations in a predetermined occupational field based on the user's gender and areas of expertise included in the user's answer; and outputting the information about fictitious occupations generated by the existing learning model to the user.

[0008] Another aspect of the present invention is a program executed in a job suggestion system that proposes fictitious occupations to a user. The program causes the computer of the job suggestion system to, upon receiving a job suggestion request from a user, to cause an existing learning model to generate a question asking the user about their gender and areas of expertise in a predetermined style; to output the question generated by the existing learning model to the user and receive the user's answer to the question; to cause the existing learning model to generate information about fictitious occupations in a predetermined occupational field based on the user's gender and areas of expertise included in the user's answer; and to output the information about fictitious occupations generated by the existing learning model to the user.

[0009] As described below, other embodiments of the present invention exist. Therefore, this disclosure is intended to provide some embodiments of the present invention and is not intended to limit the scope of the invention described and claimed herein.

[0010] Figure 1 is a block diagram showing the configuration of the occupation suggestion system in an embodiment of the present invention. Figure 2 is a diagram showing an example of output from the occupation suggestion system (an example of generating information on a fictional occupation generated by an existing learning model). Figure 3 is a sequence diagram illustrating the operation of the occupation suggestion system in an embodiment of the present invention.

[0011] A detailed description of the present invention is given below. However, the following detailed description and accompanying drawings are not intended to limit the invention.

[0012] The present invention provides a job suggestion system that suggests a fictional job to a user, and includes: a question generation unit that, upon receiving a job suggestion request from a user, causes an existing learning model to generate a question in a predetermined style that asks the user about their gender and areas of expertise; a question answer processing unit that outputs the question generated by the existing learning model to the user and receives the user's answer to the question; a fictional job generation unit that, based on the user's gender and areas of expertise included in the user's answer, causes an existing learning model to generate information about a fictional job in a predetermined occupational field; and a user suggestion processing unit that outputs the information about the fictional job generated by the existing learning model to the user.

[0013] In this configuration, when a user requests a job suggestion, an existing learning model (such as GPT) is used to generate a question asking the user about their "gender" and "strengths," using a pre-specified writing style (for example, a cheerful and engaging style), and output it to the user. When the user provides an answer to the question, the existing learning model (such as GPT) is used to generate information about a fictional job (a new job of the future) in a pre-specified occupational field (for example, the nursing care field) based on the "gender" and "strengths" included in the answer, and output it to the user. In this way, by using an existing learning model, it is possible to easily suggest a fictional job (a new job of the future) that utilizes the user's strengths in a pre-specified occupational field (for example, the nursing care field).

[0014] Furthermore, in the occupation suggestion system of the present invention, the fictional occupation generation unit may cause an existing learning model to generate occupation names for fictional occupations in katakana, and may also prohibit the inclusion of katakana expressions that are simply katakana conversions of occupational fields in the katakana expressions of said occupation names.

[0015] This configuration generates fictional job titles in katakana, giving users a "futuristic and cool impression" of those jobs (fictional jobs). In this case, the katakana representation of the job title cannot include a katakana representation of the job field, thus preventing the generation of similar job titles and increasing the variety of generated job titles.

[0016] Furthermore, in the occupation suggestion system of the present invention, the occupation field is the nursing care field, and it may be prohibited to include the katakana expression "care" in the katakana expression of the occupation name.

[0017] This configuration allows for a "futuristic and cool impression" of fictional occupations in the caregiving field to be conveyed to users. In this case, the inclusion of the katakana word "care" in the occupational name is prohibited, which prevents the generation of similar occupational names and increases the variety of generated occupational names.

[0018] Furthermore, in the occupation suggestion system of the present invention, when the question generation unit receives an occupation suggestion request from a user, it may cause an existing learning model to generate a question in a predetermined style that asks the user about their gender, strengths, and future dreams. The fictional occupation generation unit may then cause an existing learning model to generate information about a fictional occupation in a predetermined occupational field based on the user's gender, strengths, and future dreams included in the user's response.

[0019] In this configuration, when a user requests a job suggestion, an existing learning model (such as GPT) is used to generate a question asking the user about their "gender," "strengths," and "future dreams," using a pre-specified writing style (for example, a cheerful and engaging style). When the user provides answers to the questions, the existing learning model (such as GPT) is used to generate information about a fictional job (a new job of the future) in a pre-specified occupational field (for example, the nursing care field) based on the "gender," "strengths," and "future dreams" included in the answer, and this information is output to the user.

[0020] The present invention relates to a method implemented in a job suggestion system that proposes fictional occupations to a user, the method comprising: receiving a job suggestion request from a user, causing an existing learning model to generate a question in a predetermined style that asks the user about their gender and areas of expertise; outputting the question generated by the existing learning model to the user and receiving the user's answer to the question; causing the existing learning model to generate information about fictional occupations in a predetermined occupational field based on the user's gender and areas of expertise included in the user's answer; and outputting the information about fictional occupations generated by the existing learning model to the user.

[0021] This method, as with the system described above, also receives a job suggestion request from a user. Using an existing learning model (e.g., GPT), it generates a question asking the user about their "gender" and "strengths," in a pre-specified style (e.g., a cheerful and engaging style), and outputs it to the user. Upon receiving the user's answer to the question, the existing learning model (e.g., GPT) is used to generate information about a hypothetical job (a new future job) in a pre-specified occupational field (e.g., the nursing care field) based on the "gender" and "strengths" included in the answer, and outputs it to the user. In this way, by using an existing learning model, it is possible to easily suggest a hypothetical job (a new future job) that utilizes the user's strengths in a pre-specified occupational field (e.g., the nursing care field).

[0022] The present invention is a program that is executed in a job suggestion system that proposes fictional occupations to a user. The program, upon receiving a job suggestion request from a user, causes an existing learning model to generate a question asking the user about their gender and strengths in a predetermined style; outputs the question generated by the existing learning model to the user and receives the user's answer to the question; based on the user's gender and strengths included in the user's answer, causes the existing learning model to generate information about fictional occupations in a predetermined occupational field; and outputs the information about fictional occupations generated by the existing learning model to the user.

[0023] Similar to the system described above, this program, upon receiving a job suggestion request from a user, uses an existing learning model (such as GPT) to generate a question asking the user about their "gender" and "strengths," using a pre-specified writing style (for example, a cheerful and engaging style) and outputting it to the user. Upon receiving the user's answer to the question, the program uses the existing learning model (such as GPT) to generate information about a hypothetical job (a new future job) in a pre-specified occupational field (such as the nursing care field) based on the "gender" and "strengths" included in the answer, and outputs it to the user. In this way, by using an existing learning model, it is possible to easily suggest a hypothetical job (a new future job) that utilizes the user's strengths in a pre-specified occupational field (such as the nursing care field).

[0024] According to the present invention, it is possible to easily propose a fictional occupation that utilizes the user's strengths within a predetermined occupational field.

[0025] (Embodiment) Hereinafter, an embodiment of the occupation suggestion system of the present invention will be described with reference to the drawings. In this embodiment, an example of an occupation suggestion system used in classes for elementary school students will be shown. The occupation suggestion system of this embodiment has a function to generate information on fictional occupations using an existing learning model. This function is realized by a program stored in a storage medium such as the memory area of ​​the occupation suggestion system.

[0026] The configuration of the job suggestion system according to an embodiment of the present invention will be described with reference to the drawings. Figure 1 is a block diagram showing the configuration of the job suggestion system according to this embodiment. As shown in Figure 1, the job suggestion system 1 is connected to the user device 2 via a network N such as the Internet. In this embodiment, the job suggestion system 1 is composed of, for example, a cloud server device. The user device 2 is composed of, for example, a computer device and includes an input unit 3 such as a keyboard and mouse, and a display unit 4 such as a display.

[0027] The input unit 3 of the user device 2 receives job suggestion requests from the user. For example, a job suggestion request is entered by clicking the "Create!" button displayed on the display unit 4 of the user device 2. The job suggestion requests entered into the user device 2 are sent to the job suggestion system 1.

[0028] The job suggestion system 1 comprises an input unit 5, an output unit 6, a storage unit 7, and a control unit 8. Various information input by the input unit 3 of the user device 2 is input to the input unit 5. Information generated by the job suggestion system 1 (information sent to the user device 2) is output from the output unit 6. The storage unit 7 stores various information (data) and programs for suggesting fictional jobs to the user. The control unit 8 has a function for suggesting fictional jobs to the user, and for this purpose, it comprises a question generation unit 80, a question answer processing unit 81, a fictional job generation unit 82, and a user suggestion processing unit 83 as functional blocks.

[0029] The question generation unit 80, upon receiving a job suggestion request from a user, has the function of having an existing learning model (for example, GPT (Generative Pre-trained Transformer)) generate a question asking the user about their "gender," "strengths," and "future dreams," in a pre-specified style. The pre-specified style includes, but is not limited to, a "style that keeps the conversation going in a cheerful tone."

[0030] More specifically, the question generation unit 80 has the function of generating prompts that cause an existing learning model (e.g., GPT) to generate questions that ask the user about their "gender," "strengths," and "future dreams," while engaging in a hospitable and fun conversation with a cheerful tone, much like a cast member at an amusement park. The question generation unit 80 also has the function of inputting the generated prompts into the existing learning model (e.g., GPT) and receiving the questions generated by the existing learning model (e.g., GPT) as a response to that input. In this way, the question generation unit 80 uses the existing learning model (e.g., GPT) to generate questions that ask the user about their "gender," "strengths," and "future dreams."

[0031] The question answering processing unit 81 has the function of outputting question sentences generated by an existing learning model (e.g., GPT) to the user. When a question sentence is sent from the job suggestion system 1 to the user device 2, the user device 2 inputs an answer to that question sentence. The question answering processing unit 81 has the function of receiving the answer to the question sentence entered by the user.

[0032] The fictional occupation generation unit 82 has a function to generate information on fictional occupations in a pre-specified occupational field using an existing learning model (e.g., GPT) based on the "gender," "strengths," and "future dreams" included in the user's response. In this case, the fictional occupation generation unit 82 causes the existing learning model (e.g., GPT) to generate the occupation name of the fictional occupation in katakana, and prohibits the inclusion of a katakana expression that is simply a katakana conversion of the occupational field in the katakana expression of the occupation name. In this embodiment, the occupational field is "nursing care," and the inclusion of the katakana expression "care" in the katakana expression of the occupation name is prohibited.

[0033] More specifically, the fictional occupation generation unit 82 has a function to generate prompts that cause an existing learning model (e.g., GPT) to generate a new occupation in the "nursing care field" by combining the "gender," "strengths," and "future dreams" included in the user's response. Furthermore, the fictional occupation generation unit 82 has a function to generate prompts that cause an existing learning model (e.g., GPT) to generate a cool and exciting "occupation name (in katakana)" that evokes a sense of the future for the generated new occupation in the "nursing care field." In this case, the inclusion of the katakana expression "care" in the occupation name is prohibited.

[0034] Furthermore, the fictional occupation generation unit 82 has a function to generate prompts that cause an existing learning model (e.g., GPT) to generate an "explanatory text" that explains, in an attractive way, why the generated new occupation in the "nursing care field" is a wonderful occupation with social significance. In addition, the fictional occupation generation unit 82 has a function to generate prompts that cause an existing learning model (e.g., GPT) to generate a "reasoning text" that explains, in an interesting and thorough manner, how the "skills" included in the user's response will be utilized in the generated new occupation in the "nursing care field".

[0035] Furthermore, the fictional occupation generation unit 82 has a function to generate prompts that cause an existing learning model (such as GPT) to generate images that are characteristic of each occupation and are also surprising, for the newly generated occupations in the "nursing care field". These images are, for example, images of people (people in their 20s) that are included in the "gender" of the user's response.

[0036] The fictional occupation generation unit 82 has the function of inputting the generated prompt into an existing learning model (e.g., GPT) and receiving the "occupation name," "description," "reason statement," and "image" generated by the existing learning model (e.g., GPT) as a response to that input. In this way, the fictional occupation generation unit 82 generates information about fictional occupations ("occupation name," "description," "reason statement," and "image") using the existing learning model (e.g., GPT).

[0037] The user suggestion processing unit 83 has a function to output information on fictional occupations generated by an existing learning model (e.g., GPT) to the user device 2. When the information on fictional occupations generated by an existing learning model (e.g., GPT) is sent from the occupation suggestion system 1 to the user device 2, it is displayed on the display unit 4 of the user device 2.

[0038] Figure 2 shows an example of generating information about a fictional occupation using an existing learning model (e.g., GPT). As shown in Figure 2, the information about a fictional occupation includes the "occupation name," "description," "reason statement," and "image."

[0039] When generating information for a fictional occupation in the "caregiving field," if the user's response includes "male" for "gender," "talent" for "drawing train route maps," and "future dream" for "train driver," the "occupation name" generated by an existing learning model (e.g., GPT) might be something like "train navigator." In this case, the "description" generated by an existing learning model (e.g., GPT) might be something like, "Train navigators provide train driving simulations and brain exercises using route maps in elderly care facilities. They teach seniors the joy of driving using train driving simulators and improve cognitive function with games using route maps. This brings joy to elderly people who love trains and increases their daily enjoyment." Furthermore, "reason statements" generated by existing learning models (such as GPT) might include phrases like, "The reason Train Navigator is needed is to provide seniors with new hobbies and stimulation. Train driving simulations allow seniors to experience the thrill and enjoyment of driving, and route map games help activate the brain. You can use your passion for trains and route map drawing skills to help seniors discover new forms of entertainment."

[0040] Furthermore, if the user's response includes "male" for "gender," "history" for "areas of expertise," and "future dream" for "to live a comfortable life," the "job title" generated by an existing learning model (e.g., GPT) might be something like "History Comfort Guide." In this case, the "explanatory text" generated by an existing learning model (e.g., GPT) might be something like, "For someone like you who is good at history and wants to live a comfortable life, there is a job called History Comfort Guide. This job involves supporting the comfortable lives of the elderly in nursing homes by telling them history stories. You can use your knowledge of history to provide a pleasant environment while making the elderly enjoy themselves." Also, the "reasoning text" generated by an existing learning model (e.g., GPT) might be something like, "Being a History Comfort Guide is an important job that enriches the hearts of the elderly and supports their comfortable lives. Your knowledge of history will allow the elderly to enjoy interesting stories and provide a comfortable environment, enabling them to live more relaxed lives. This unique combination is what energizes everyone."

[0041] The operation of the job suggestion system 1, configured as described above, will be explained with reference to the sequence diagram in Figure 3.

[0042] When proposing a fictional occupation (a new occupation of the future) using the occupation suggestion system 1 of this embodiment, the first operation performed is to click the "Create!" button on the user device 2 (S1). Then, an occupation suggestion request is sent from the user device 2 to the occupation suggestion system 1 (S2).

[0043] When the career proposal system 1 receives a career proposal request, it causes an existing learning model (e.g., GPT, etc.) to generate a question text for asking the user about "gender", "things they are good at", and "future dreams" in a pre-specified style. Specifically, the career proposal system 1 generates a prompt that causes an existing learning model (e.g., GPT, etc.) to generate a question text for asking the user about "gender", "things they are good at", and "future dreams" (S3), inputs the generated prompt into an existing learning model (e.g., GPT, etc.), and receives, as a response to the input, the question text generated by the existing learning model (e.g., GPT, etc.) (S4).

[0044] The career proposal system 1 transmits the question text generated by the existing learning model (e.g., GPT, etc.) to the user (S5). The question text received from the career proposal system 1 is displayed on the display unit 4 of the user device 2 (S6), and an answer to the question text is input from the input unit 3 of the user device 2 (S7). The input answer is transmitted from the user device 2 to the career proposal system 1 (S8).

[0045] When the career proposal system 1 receives an answer from the user device 2, based on the "gender", "things they are good at", and "future dreams" included in the answer, it causes an existing learning model (e.g., GPT, etc.) to generate information about fictional occupations in a pre-specified occupation field. Specifically, the career proposal system 1 generates a prompt that causes an existing learning model (e.g., GPT, etc.) to generate a new occupation in the "caregiving field" by integrating the "gender", "things they are good at", and "future dreams" included in the user's answer (S9), inputs the generated prompt into an existing learning model (e.g., GPT, etc.), and receives, as a response to the input, information about fictional occupations ("occupation name", "description text", "reason text", and "image") generated by the existing learning model (e.g., GPT, etc.) (S10).

[0046] Information on fictional occupations (occupation name, description, reason statement, and image) generated by an existing learning model (e.g., GPT, etc.) is sent from the occupation proposal system 1 to the user device 2 (S11) and displayed on the display unit 4 of the user device 2 (S12). In this way, fictional occupations (new occupations in the future) are proposed.

[0047] According to the occupation proposal system 1 of this embodiment, when receiving an occupation proposal request from a user, using an existing learning model (e.g., GPT, etc.), a question text for asking the user about "gender", "things they are good at", and "future dreams" is generated in a pre-specified style (e.g., a style that makes the conversation proceed pleasantly with a bright tone, etc.) and output to the user. The style of "making the conversation proceed pleasantly with a bright tone" is suitable as an expression for children such as elementary school students.

[0048] And in this embodiment, when receiving an answer to the question from the user, using an existing learning model (e.g., GPT, etc.), based on the "gender", "things they are good at", and "future dreams" included in the answer, information on fictional occupations (new occupations in the future) in a pre-specified occupation field (e.g., the nursing care field, etc.) is generated and output to the user.

[0049] In this way, according to the occupation proposal system 1 of this embodiment, by using an existing learning model (e.g., GPT, etc.), it is possible to easily propose fictional occupations (new occupations in the future) that make use of the user's strengths in a pre-specified occupation field (e.g., the nursing care field, etc.).

[0050] Also, in this embodiment, since the occupation names of fictional occupations in the "nursing care field" are generated in katakana expressions, it is possible to give the user an "impression of being future-oriented and cool" regarding that occupation (fictional occupation). In this case, it is prohibited to include the katakana expression "ケア" which is simply the katakana conversion of the occupation field in the katakana expression of the occupation name in the "nursing care field", so as to prevent similar occupation names from being easily generated and increase the variations of the generated occupation names.

[0051] Although embodiments of the present invention have been described above by example, the scope of the present invention is not limited to these, and modifications and alterations can be made within the scope described in the claims depending on the purpose.

[0052] For example, the above description explained the case where the predetermined occupational field is the "nursing care field," but the present invention is not limited to this and can be similarly implemented in other occupational fields (for example, the "agriculture field" or the "construction work field").

[0053] Furthermore, while the above explanation described an example where information about a fictional occupation is generated based on "gender," "strengths," and "future dreams," "future dreams" are not necessarily required. According to the present invention, it is also possible to generate information about a fictional occupation based on "gender" and "strengths."

[0054] Furthermore, although the above explanation described an example in which the question generation unit 80 and the fictional occupation generation unit 82 are implemented as separate functions, the question generation unit 80 and the fictional occupation generation unit 82 may also be implemented together as a single function (for example, "Question / Fictional Occupation Generation Unit").

[0055] As described above, the occupation suggestion system according to the present invention has the effect of easily suggesting a fictional occupation (a new occupation of the future) that utilizes the user's strengths in a pre-specified occupational field (for example, the nursing care field), and is useful for use in lessons for elementary school students and the like.

[0056] 1. Occupation suggestion system 2. User device 3. Input unit 4. Display unit 5. Input unit 6. Output unit 7. Storage unit 8. Control unit 80. Question text generation unit 81. Question answer processing unit 82. Fictional occupation generation unit 83. User suggestion processing unit

Claims

1. A job suggestion system that proposes fictional occupations to a user, comprising: a question generation unit that, upon receiving a job suggestion request from the user, causes an existing learning model to generate a question asking the user about their gender and areas of expertise in a pre-specified style; a question answer processing unit that outputs the question generated by the existing learning model to the user and receives the user's answer to the question; a fictional occupation generation unit that, based on the user's gender and areas of expertise included in the user's answer, causes the existing learning model to generate information about a fictional occupation in a pre-specified occupational field; and a user suggestion processing unit that outputs the information about the fictional occupation generated by the existing learning model to the user.

2. The occupation suggestion system according to claim 1, wherein the fictional occupation generation unit causes the existing learning model to generate occupation names for the fictional occupations in katakana, and prohibits the inclusion of katakana expressions that are simply katakana conversions of the occupational field in the katakana expressions of said occupation names.

3. The occupational suggestion system according to claim 2, wherein the occupational field is the nursing care field, and the inclusion of the katakana expression "care" in the katakana expression of the occupational name is prohibited.

4. The job suggestion system according to claim 1, wherein when the question generation unit receives a job suggestion request from the user, it causes the existing learning model to generate a question asking the user about their gender, strengths, and future dreams in the pre-specified style, and the fictional job generation unit causes the existing learning model to generate information about a fictional job in the pre-specified job field based on the user's gender, strengths, and future dreams included in the user's response.

5. A method for performing a job suggestion system that suggests fictitious jobs to a user, the method comprising: receiving a job suggestion request from the user, causing an existing learning model to generate a question sentence in a predetermined style that asks the user about their gender and areas of expertise; outputting the question sentence generated by the existing learning model to the user and receiving the user's answer to the question sentence; causing the existing learning model to generate information about fictitious jobs in a predetermined occupational field based on the user's gender and areas of expertise included in the user's answer; and outputting the information about fictitious jobs generated by the existing learning model to the user.

6. A program executed in a job suggestion system that suggests fictitious occupations to a user, wherein the program causes the computer of the job suggestion system to perform the following: when it receives a job suggestion request from the user, it causes an existing learning model to generate a question asking the user about their gender and areas of expertise in a predetermined style; it outputs the question generated by the existing learning model to the user and receives the user's answer to the question; it causes the existing learning model to generate information about fictitious occupations in a predetermined occupational field based on the user's gender and areas of expertise included in the user's answer; and it outputs the information about fictitious occupations generated by the existing learning model to the user.

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