system

Abstract

The system according to the embodiment aims to provide an ideal carrier case by having a user input information about his / her carrier. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives carrier information from a user. The analysis unit analyzes the information received by the reception unit and generates an ideal carrier case. The provision unit provides the carrier case generated by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult for business people to obtain specific guidelines to resolve their career concerns.

[0005] The system according to the embodiment aims to provide an ideal carrier case by having a user input information about his / her carrier. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives carrier information from a user. The analysis unit analyzes the information received by the reception unit and generates an ideal carrier case. The provision unit provides the carrier case generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an ideal carrier case by allowing a user to input information about his / her carrier. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A career support system according to an embodiment of the present invention is a platform for businesspeople to resolve their career concerns and find their ideal career path. In this career support system, a user inputs information about their career, and a generation AI analyzes the information to generate and provide an ideal career case for the user. For example, the user inputs information such as their current occupation, desired occupation, skills, years of experience, and desired annual salary. This information is then input to the generation AI, which analyzes it. The generation AI generates an ideal career case based on the user's information. For example, the generation AI determines whether the user has the skills and experience necessary for their desired occupation and provides advice on how to acquire the necessary skills and experience. The generated career case is then provided to the user. For example, the system presents specific steps for the user to obtain their desired occupation and methods for acquiring the necessary skills and experience. This allows the user to resolve their career concerns and find their ideal career path. This system allows businesspeople to resolve their career concerns and find their ideal career path. For example, a user who is dissatisfied with their current occupation can learn specific steps to obtain their desired occupation. Furthermore, learning methods for acquiring the necessary skills and experience can lead to greater career diversity. This allows the career support system to efficiently accept and analyze the user's career information and provide an ideal career case.

[0029] A career support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives career information from a user. The career information includes, for example, a current occupation, a desired occupation, skills, years of experience, and a desired annual salary, but is not limited to these examples. The reception unit, for example, stores the information input by the user in a database and transmits it to the analysis unit. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate an ideal career case. The generation AI, for example, determines whether the user has the skills and experience necessary for the desired occupation based on the user's information and provides advice on how to acquire the necessary skills and experience. The generation AI analyzes the user's information and generates an ideal career case using a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the user with the career case generated by the analysis unit. The provision unit, for example, displays the generated career case on the user's device and presents specific steps and methods for acquiring the necessary skills and experience. This enables the career support system according to an embodiment to efficiently receive and analyze the user's career information and provide an ideal career case.

[0030] The reception unit can receive information on the user's current occupation, desired occupation, skills, years of experience, and desired annual salary. The reception unit, for example, stores the information on the current occupation, desired occupation, skills, years of experience, and desired annual salary input by the user in a database. For example, the user can input "engineer" as the current occupation, "project manager" as the desired occupation, "programming" as the skill, "5 years" as the years of experience, and "8 million yen" as the desired annual salary. The reception unit can also send the information input by the user to the analysis unit. This makes it possible to receive detailed career information from the user.

[0031] The analysis unit can generate an ideal career case based on user information. The analysis unit, for example, uses a generation AI to analyze the user's information and generate an ideal career case. The generation AI, for example, determines whether the user has the skills and experience necessary for their desired career and provides advice to supplement the necessary skills and experience. For example, if the user lacks "project management skills" as a skill necessary to obtain their desired career, the generation AI can advise them to "take training to improve their project management skills." The generation AI can also suggest an ideal career path based on the user's information. For example, it could present specific steps for the user to change jobs from their current job to their desired job. This makes it possible to generate an ideal career case based on the user's information.

[0032] The provision unit can provide the user with specific steps to obtain a desired occupation and methods for acquiring the necessary skills and experience. The provision unit, for example, displays the generated career case on the user's device and presents the specific steps and methods for acquiring the necessary skills and experience. For example, the provision unit can present "taking training to improve project management skills" as a step required for the user to obtain a desired occupation. The provision unit can also present "taking online courses" and "gaining work experience" as methods for the user to obtain the necessary skills and experience for the user to obtain a desired occupation. This makes it possible to provide the user with specific steps to obtain a desired occupation and methods for acquiring the necessary skills and experience.

[0033] The analysis unit can determine whether the user has the skills and experience necessary for the desired occupation and provide advice to compensate for the required skills and experience. The analysis unit, for example, uses a generation AI to determine whether the user has the skills and experience necessary for the desired occupation. For example, if the user lacks "project management skills" as a skill necessary for the desired occupation, the generation AI can advise the user to "take training to improve their project management skills." Furthermore, if the user lacks "five or more years of work experience" as a necessary experience for the desired occupation, the generation AI can advise the user to "participate in an internship to gain work experience." This makes it possible to determine whether the user has the skills and experience necessary for the desired occupation and provide advice to compensate for the required skills and experience.

[0034] The providing unit can provide the generated career case to the user. For example, the providing unit displays the generated career case on the user's device and presents specific steps and methods for acquiring the necessary skills and experience. For example, the providing unit can present "taking training to improve project management skills" as a step necessary for the user to obtain the occupation of their choice. The providing unit can also present "taking online courses" and "gaining work experience" as methods for the user to obtain the skills and experience necessary for obtaining the occupation of their choice. In this way, the generated career case can be provided to the user.

[0035] The reception unit can analyze the user's past career information and suggest the optimal input method. For example, the reception unit automatically displays frequently used items as candidates based on the career information the user has previously input. For example, the reception unit can automatically display the work history and skills the user has previously input, eliminating the need to re-enter the information. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has previously used. For example, if the user has used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also suggest input items related to specific occupations and skills based on the user's past career information. For example, if the user has worked as an "engineer" in the past, the reception unit suggests skills and experience related to engineering as input items. This makes it possible to analyze the user's past career information and suggest the optimal input method.

[0036] The reception unit can filter the career information based on the user's current living situation and areas of interest when the career information is input. For example, the reception unit allows the user to input relevant career information preferentially based on the user's current living situation (e.g., raising a child, looking for a new job). For example, a user who is raising a child is allowed to input occupations and skills that can be combined with childcare preferentially. The reception unit can also suggest input items for related occupations and skills based on the user's areas of interest (e.g., IT, medicine). For example, a user who is interested in IT is allowed to input IT-related occupations and skills preferentially. The reception unit can also simplify the input work by omitting unnecessary input items based on the user's current living situation and areas of interest. For example, input items for areas that the user is not interested in are omitted, making the input work more efficient. This makes it possible to filter career information based on the user's current living situation and areas of interest.

[0037] When career information is input, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize input of information related to occupations and skills specific to a region based on the user's current location. For example, if the user inputs "Tokyo" as the current location, the reception unit can prioritize input of occupations and skills related to Tokyo. The reception unit can also prioritize input of information related to nearby job information and career events based on the user's geographical location information. For example, if the user inputs "Osaka Prefecture" as the current location, the reception unit can prioritize input of job information and career events related to Osaka Prefecture. The reception unit can also prioritize input of career information based on regional labor market trends taking into account the user's geographical location information. For example, if the user inputs "Fukuoka Prefecture" as the current location, the reception unit can prioritize input of career information based on labor market trends in Fukuoka Prefecture. In this way, career information can be input taking into account the user's geographical location information.

[0038] When career information is entered, the reception unit can analyze the user's social media activity and cause the user to enter related information. For example, the reception unit can prioritize the input of information related to occupations and skills of interest from the user's social media activity. For example, if the user has expressed an interest in "data science" on social media, the reception unit can prioritize the input of occupations and skills related to data science. The reception unit can also suggest related career information based on the user's social media network. For example, if the user has a network in the "IT industry," the reception unit can suggest career information related to the IT industry. The reception unit can also analyze the user's social media activity and cause the user to prioritize the input of career information based on trends. For example, if the user has expressed an interest in "AI technology," the reception unit can prioritize the input of career information related to AI technology. In this way, the user's social media activity can be analyzed and career information can be input.

[0039] During analysis, the analysis unit can generate a career case by referring to the user's past career path. The analysis unit, for example, suggests an optimal career path based on the user's past work history. For example, if the user previously worked as an "engineer," the analysis unit suggests a career path that makes use of that experience. The analysis unit can also refer to the user's past skill set to generate a career case that helps improve those skills. For example, if the user has "programming skills," the analysis unit generates a career case that makes use of those programming skills. The analysis unit can also analyze the user's past career path and provide advice based on career turning points. For example, if the user wants to change jobs to become a "project manager," the analysis unit suggests specific steps to take to change jobs to become a project manager. This makes it possible to generate an optimal career case by referring to the user's past career path.

[0040] During analysis, the analysis unit can apply different analysis algorithms based on the user's current occupation and skills. For example, the analysis unit applies an analysis algorithm specialized for a specific industry based on the user's current occupation. For example, if the user is engaged in the "IT industry," the analysis unit applies an analysis algorithm specialized for the IT industry. The analysis unit can also apply an analysis algorithm for generating a career case that will help improve the user's skills based on the user's skill set. For example, if the user has "data analysis skills," the analysis unit applies an analysis algorithm for generating a career case that utilizes the data analysis skills. The analysis unit can also apply an analysis algorithm for suggesting a career path that utilizes the user's experience based on the user's work experience. For example, if the user has "marketing experience," the analysis unit applies an analysis algorithm for suggesting a career path that utilizes the marketing experience. This makes it possible to apply different analysis algorithms based on the user's current occupation and skills.

[0041] During analysis, the analysis unit can determine the analysis priority based on the user's submission time. For example, if the user is in a hurry, the analysis unit sets the analysis priority high, taking into account the submission time. For example, if the user is in a hurry, the generation AI sets the analysis priority high, taking into account the submission time. In addition, if the user has time to spare, the analysis unit can also set the priority low to perform a more detailed analysis. For example, if the user has time to spare, the generation AI sets the priority low to perform a more detailed analysis. In addition, the analysis unit can also set an optimal analysis schedule based on the user's submission time. For example, if the user sets a submission deadline, the generation AI sets an optimal analysis schedule. This makes it possible to determine the analysis priority based on the user's submission time.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and data related to the user. For example, the analysis unit performs the analysis by referring to the latest research papers related to the user's occupation. For example, if the user works as a "data scientist," the analysis can be performed by referring to the latest research papers related to data science. The analysis unit can also improve the accuracy of the analysis by referring to a database related to the user's skills. For example, if the user has "programming skills," the analysis can be performed by referring to a database related to programming. The analysis unit can also perform the analysis by referring to statistical data related to the user's industry. For example, if the user works in the "marketing industry," the analysis can be performed by referring to statistical data related to marketing. This allows the analysis to be performed by referring to literature and data related to the user.

[0043] When providing the data, the providing unit can select the optimal provision method by referring to the user's past career information. The providing unit, for example, preferentially provides relevant career cases based on the user's past career information. For example, if the user previously worked as an "engineer," it preferentially provides career cases related to engineers. The providing unit can also refer to the user's past work history and provide career cases related to their work history. For example, if the user previously worked as a "marketing manager," it provides career cases related to marketing managers. The providing unit can also provide career cases that are useful for improving skills based on the user's past skill set. For example, if the user has "data analysis skills," it provides career cases that utilize data analysis skills. This makes it possible to select the optimal provision method by referring to the user's past career information.

[0044] The providing unit can customize the means for providing a career case based on the user's current living situation at the time of providing. For example, if the user is raising a child, the providing unit provides a career case that takes time constraints into consideration. For example, if the user is raising a child, the providing unit provides a career case that can be completed in a short period of time. Furthermore, if the user is searching for a new job, the providing unit can also provide a career case that can be completed quickly. For example, if the user is searching for a new job, the providing unit can provide a career case that can produce results in a short period of time. Furthermore, if the user desires remote work, the providing unit can also provide a career case that is suitable for remote work. For example, if the user desires remote work, the providing unit provides a career case that includes occupations and skills that are suitable for remote work. This makes it possible to customize the means for providing a career case according to the user's current living situation.

[0045] The providing unit can provide the optimal career case by taking into consideration the user's geographical location information. For example, the providing unit provides a region-specific career case based on the user's current location. For example, if the user inputs "Tokyo" as the current location, a career case related to Tokyo is provided. The providing unit can also provide career cases related to nearby job information and career events based on the user's geographical location information. For example, if the user inputs "Osaka Prefecture" as the current location, job information and career events related to Osaka Prefecture are provided. The providing unit can also provide a career case based on regional labor market trends by taking into consideration the user's geographical location information. For example, if the user inputs "Fukuoka Prefecture" as the current location, a career case based on labor market trends in Fukuoka Prefecture is provided. This makes it possible to provide the optimal career case by taking into consideration the user's geographical location information.

[0046] At the time of provision, the provision unit can analyze the user's social media activity to suggest a means for providing career cases. For example, the provision unit provides career cases related to occupations and skills of interest from the user's social media activity. For example, if the user has expressed an interest in "data science" on social media, career cases related to data science are provided. The provision unit can also suggest related career cases based on the user's social media network. For example, if the user has a network in the "IT industry," career cases related to the IT industry are suggested. The provision unit can also analyze the user's social media activity to provide career cases based on trends. For example, if the user has expressed an interest in "AI technology," career cases related to AI technology are provided. In this way, a means for providing career cases can be suggested by analyzing the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can also generate a career case by taking into consideration the user's hobbies and interests in addition to the user's career information. For example, if the user lists "photography" as a hobby, a career case including occupations and skills related to photography can be generated. If the user is interested in "outdoor activities," a career case including occupations and skills related to outdoor activities can be generated. Furthermore, if the user is interested in "cooking," a career case including occupations and skills related to cooking can be generated. This makes it possible to provide a career case that takes into consideration the user's hobbies and interests.

[0049] When providing the user's career case, the providing unit can suggest learning resources according to the user's learning style. For example, if the user has a visual learning style, the providing unit can suggest learning resources including video tutorials and infographics. If the user has an auditory learning style, the providing unit can suggest learning resources including podcasts and audiobooks. Furthermore, if the user has a hands-on learning style, the providing unit can suggest learning resources including workshops and hands-on sessions. This makes it possible to provide optimal learning resources according to the user's learning style.

[0050] The analysis unit can predict future career trends based on the user's career information and make suggestions to the user. For example, it can analyze current market trends and suggest to the user occupations and skills that are predicted to be in high demand in the future. It can also suggest occupations and skills that may emerge based on technological advances and social changes. It can also analyze industry trends related to the user's career path and suggest promising career paths for the future. This allows the user to understand future career trends and make appropriate career choices.

[0051] When providing the user's career case, the provision unit can suggest career support that utilizes the user's network. For example, the provision unit can suggest a mentorship program by utilizing the professional network of the user's industry. The provision unit can also suggest career advice and information exchange by utilizing online communities and forums in which the user participates. Furthermore, the provision unit can suggest participation in career-related events and seminars by utilizing the user's social media network. This allows the user to receive career support by utilizing their own network.

[0052] The analysis unit can simulate different career paths based on the user's career information and suggest them to the user. For example, if the user is interested in multiple career paths, the analysis unit can simulate and compare the future prospects, skills, and experience required for each career path. It can also simulate the risks and benefits of the user choosing a specific career path and provide this to the user. It can also simulate the impact of the user changing their career path and support the user in making the optimal career choice. This allows the user to compare different career paths and make the optimal career choice.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives career information from the user. The career information includes current occupation, desired occupation, skills, years of experience, desired annual salary, etc. The reception unit stores the information entered by the user in a database and sends it to the analysis unit. Step 2: The analysis unit uses generation AI to analyze the information received by the reception unit and generate an ideal career case. Based on the user's information, generation AI determines whether the user has the skills and experience necessary for the desired occupation and provides advice on how to acquire the necessary skills and experience. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to analyze the user's information and generate an ideal career case. Step 3: The providing unit provides the user with the career case generated by the analysis unit. The providing unit displays the generated career case on the user's device and presents specific steps and methods for acquiring the necessary skills and experience.

[0055] (Example 2) A career support system according to an embodiment of the present invention is a platform for businesspeople to resolve their career concerns and find their ideal career path. In this career support system, a user inputs information about their career, and a generation AI analyzes the information to generate and provide an ideal career case for the user. For example, the user inputs information such as their current occupation, desired occupation, skills, years of experience, and desired annual salary. This information is then input to the generation AI, which analyzes it. The generation AI generates an ideal career case based on the user's information. For example, the generation AI determines whether the user has the skills and experience necessary for their desired occupation and provides advice on how to acquire the necessary skills and experience. The generated career case is then provided to the user. For example, the system presents specific steps for the user to obtain their desired occupation and methods for acquiring the necessary skills and experience. This allows the user to resolve their career concerns and find their ideal career path. This system allows businesspeople to resolve their career concerns and find their ideal career path. For example, a user who is dissatisfied with their current occupation can learn specific steps to obtain their desired occupation. Furthermore, learning methods for acquiring the necessary skills and experience can lead to greater career diversity. This allows the career support system to efficiently accept and analyze the user's career information and provide an ideal career case.

[0056] A career support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives career information from a user. The career information includes, for example, a current occupation, a desired occupation, skills, years of experience, and a desired annual salary, but is not limited to these examples. The reception unit, for example, stores the information input by the user in a database and transmits it to the analysis unit. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate an ideal career case. The generation AI, for example, determines whether the user has the skills and experience necessary for the desired occupation based on the user's information and provides advice on how to acquire the necessary skills and experience. The generation AI analyzes the user's information and generates an ideal career case using a text generation AI (e.g., LLM) or a multimodal generation AI. The provision unit provides the user with the career case generated by the analysis unit. The provision unit, for example, displays the generated career case on the user's device and presents specific steps and methods for acquiring the necessary skills and experience. This enables the career support system according to an embodiment to efficiently receive and analyze the user's career information and provide an ideal career case.

[0057] The reception unit can receive information on the user's current occupation, desired occupation, skills, years of experience, and desired annual salary. The reception unit, for example, stores the information on the current occupation, desired occupation, skills, years of experience, and desired annual salary input by the user in a database. For example, the user can input "engineer" as the current occupation, "project manager" as the desired occupation, "programming" as the skill, "5 years" as the years of experience, and "8 million yen" as the desired annual salary. The reception unit can also send the information input by the user to the analysis unit. This makes it possible to receive detailed career information from the user.

[0058] The analysis unit can generate an ideal career case based on user information. The analysis unit, for example, uses a generation AI to analyze the user's information and generate an ideal career case. The generation AI, for example, determines whether the user has the skills and experience necessary for their desired career and provides advice to supplement the necessary skills and experience. For example, if the user lacks "project management skills" as a skill necessary to obtain their desired career, the generation AI can advise them to "take training to improve their project management skills." The generation AI can also suggest an ideal career path based on the user's information. For example, it could present specific steps for the user to change jobs from their current job to their desired job. This makes it possible to generate an ideal career case based on the user's information.

[0059] The provision unit can provide the user with specific steps to obtain a desired occupation and methods for acquiring the necessary skills and experience. The provision unit, for example, displays the generated career case on the user's device and presents the specific steps and methods for acquiring the necessary skills and experience. For example, the provision unit can present "taking training to improve project management skills" as a step required for the user to obtain a desired occupation. The provision unit can also present "taking online courses" and "gaining work experience" as methods for the user to obtain the necessary skills and experience for the user to obtain a desired occupation. This makes it possible to provide the user with specific steps to obtain a desired occupation and methods for acquiring the necessary skills and experience.

[0060] The analysis unit can determine whether the user has the skills and experience necessary for the desired occupation and provide advice to compensate for the required skills and experience. The analysis unit, for example, uses a generation AI to determine whether the user has the skills and experience necessary for the desired occupation. For example, if the user lacks "project management skills" as a skill necessary for the desired occupation, the generation AI can advise the user to "take training to improve their project management skills." Furthermore, if the user lacks "five or more years of work experience" as a necessary experience for the desired occupation, the generation AI can advise the user to "participate in an internship to gain work experience." This makes it possible to determine whether the user has the skills and experience necessary for the desired occupation and provide advice to compensate for the required skills and experience.

[0061] The providing unit can provide the generated career case to the user. For example, the providing unit displays the generated career case on the user's device and presents specific steps and methods for acquiring the necessary skills and experience. For example, the providing unit can present "taking training to improve project management skills" as a step necessary for the user to obtain the occupation of their choice. The providing unit can also present "taking online courses" and "gaining work experience" as methods for the user to obtain the skills and experience necessary for obtaining the occupation of their choice. In this way, the generated career case can be provided to the user.

[0062] The reception unit can estimate the user's emotions and customize the career information input interface based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple and intuitive interface and minimizes input steps. For example, the reception unit reduces the number of items the user needs to input and allows the user to input only the minimum amount of information necessary. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, the reception unit can increase the number of items the user needs to input and allow the user to input more detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input career information. For example, the user can input career information by voice, and the system automatically converts it into text. In this way, the career information input interface can be customized according to the user's emotions.

[0063] The reception unit can analyze the user's past career information and suggest the optimal input method. For example, the reception unit automatically displays frequently used items as candidates based on the career information the user has previously input. For example, the reception unit can automatically display the work history and skills the user has previously input, eliminating the need to re-enter the information. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has previously used. For example, if the user has used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also suggest input items related to specific occupations and skills based on the user's past career information. For example, if the user has worked as an "engineer" in the past, the reception unit suggests skills and experience related to engineering as input items. This makes it possible to analyze the user's past career information and suggest the optimal input method.

[0064] The reception unit can filter the career information based on the user's current living situation and areas of interest when the career information is input. For example, the reception unit allows the user to input relevant career information preferentially based on the user's current living situation (e.g., raising a child, looking for a new job). For example, a user who is raising a child is allowed to input occupations and skills that can be combined with childcare preferentially. The reception unit can also suggest input items for related occupations and skills based on the user's areas of interest (e.g., IT, medicine). For example, a user who is interested in IT is allowed to input IT-related occupations and skills preferentially. The reception unit can also simplify the input work by omitting unnecessary input items based on the user's current living situation and areas of interest. For example, input items for areas that the user is not interested in are omitted, making the input work more efficient. This makes it possible to filter career information based on the user's current living situation and areas of interest.

[0065] The reception unit can estimate the user's emotions and determine the priority of career information to be input based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit allows the user to input important career information preferentially and postpone detailed information. For example, the number of items to be input by the user is reduced, and only the most important information is input. Furthermore, when the user is relaxed, the reception unit can also allow the user to input detailed career information preferentially and collect more specific information. For example, the number of items to be input by the user is increased, and more detailed information is input. Furthermore, when the user is in a hurry, the reception unit can also allow the user to input only the most important career information preferentially and complete the input work quickly. For example, the number of items to be input by the user is reduced, and the input work is completed quickly. In this way, the input priority of career information can be determined according to the user's emotions.

[0066] When career information is input, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize input of information related to occupations and skills specific to a region based on the user's current location. For example, if the user inputs "Tokyo" as the current location, the reception unit can prioritize input of occupations and skills related to Tokyo. The reception unit can also prioritize input of information related to nearby job information and career events based on the user's geographical location information. For example, if the user inputs "Osaka Prefecture" as the current location, the reception unit can prioritize input of job information and career events related to Osaka Prefecture. The reception unit can also prioritize input of career information based on regional labor market trends taking into account the user's geographical location information. For example, if the user inputs "Fukuoka Prefecture" as the current location, the reception unit can prioritize input of career information based on labor market trends in Fukuoka Prefecture. In this way, career information can be input taking into account the user's geographical location information.

[0067] When career information is entered, the reception unit can analyze the user's social media activity and cause the user to enter related information. For example, the reception unit can prioritize the input of information related to occupations and skills of interest from the user's social media activity. For example, if the user has expressed an interest in "data science" on social media, the reception unit can prioritize the input of occupations and skills related to data science. The reception unit can also suggest related career information based on the user's social media network. For example, if the user has a network in the "IT industry," the reception unit can suggest career information related to the IT industry. The reception unit can also analyze the user's social media activity and cause the user to prioritize the input of career information based on trends. For example, if the user has expressed an interest in "AI technology," the reception unit can prioritize the input of career information related to AI technology. In this way, the user's social media activity can be analyzed and career information can be input.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and generates more carrier cases. For example, if the user is relaxed, the generation AI performs a detailed analysis and generates multiple carrier cases. The analysis unit can also perform a quick analysis and prioritize the generation of the most important carrier cases if the user is in a hurry. For example, if the user is in a hurry, the generation AI can perform an analysis quickly and generate the most important carrier cases. The analysis unit can also adjust the algorithm to provide simple and intuitive analysis results if the user is feeling stressed. For example, if the user is feeling stressed, the generation AI can provide simple and intuitive analysis results. This makes it possible to adjust the analysis algorithm according to the user's emotions.

[0069] During analysis, the analysis unit can generate a career case by referring to the user's past career path. The analysis unit, for example, suggests an optimal career path based on the user's past work history. For example, if the user previously worked as an "engineer," the analysis unit suggests a career path that makes use of that experience. The analysis unit can also refer to the user's past skill set to generate a career case that helps improve those skills. For example, if the user has "programming skills," the analysis unit generates a career case that makes use of those programming skills. The analysis unit can also analyze the user's past career path and provide advice based on career turning points. For example, if the user wants to change jobs to become a "project manager," the analysis unit suggests specific steps to take to change jobs to become a project manager. This makes it possible to generate an optimal career case by referring to the user's past career path.

[0070] During analysis, the analysis unit can apply different analysis algorithms based on the user's current occupation and skills. For example, the analysis unit applies an analysis algorithm specialized for a specific industry based on the user's current occupation. For example, if the user is engaged in the "IT industry," the analysis unit applies an analysis algorithm specialized for the IT industry. The analysis unit can also apply an analysis algorithm for generating a career case that will help improve the user's skills based on the user's skill set. For example, if the user has "data analysis skills," the analysis unit applies an analysis algorithm for generating a career case that utilizes the data analysis skills. The analysis unit can also apply an analysis algorithm for suggesting a career path that utilizes the user's experience based on the user's work experience. For example, if the user has "marketing experience," the analysis unit applies an analysis algorithm for suggesting a career path that utilizes the marketing experience. This makes it possible to apply different analysis algorithms based on the user's current occupation and skills.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is nervous, the generation AI provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is relaxed, the generation AI provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, the generation AI provides a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0072] During analysis, the analysis unit can determine the analysis priority based on the user's submission time. For example, if the user is in a hurry, the analysis unit sets the analysis priority high, taking into account the submission time. For example, if the user is in a hurry, the generation AI sets the analysis priority high, taking into account the submission time. In addition, if the user has time to spare, the analysis unit can also set the priority low to perform a more detailed analysis. For example, if the user has time to spare, the generation AI sets the priority low to perform a more detailed analysis. In addition, the analysis unit can also set an optimal analysis schedule based on the user's submission time. For example, if the user sets a submission deadline, the generation AI sets an optimal analysis schedule. This makes it possible to determine the analysis priority based on the user's submission time.

[0073] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and data related to the user. For example, the analysis unit performs the analysis by referring to the latest research papers related to the user's occupation. For example, if the user works as a "data scientist," the analysis can be performed by referring to the latest research papers related to data science. The analysis unit can also improve the accuracy of the analysis by referring to a database related to the user's skills. For example, if the user has "programming skills," the analysis can be performed by referring to a database related to programming. The analysis unit can also perform the analysis by referring to statistical data related to the user's industry. For example, if the user works in the "marketing industry," the analysis can be performed by referring to statistical data related to marketing. This allows the analysis to be performed by referring to literature and data related to the user.

[0074] The providing unit can estimate the user's emotions and adjust the method of providing the carrier case based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides a simple and highly visible method of providing the carrier case. For example, if the user is nervous, the generation AI provides a simple and highly visible method of providing the carrier case. Furthermore, if the user is relaxed, the providing unit can also provide a method of providing the carrier case that includes detailed information. For example, if the user is relaxed, the generation AI provides a method of providing the carrier case that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a method of providing the carrier case that focuses on the main points. For example, if the user is in a hurry, the generation AI provides a method of providing the carrier case that focuses on the main points. This makes it possible to adjust the method of providing the carrier case according to the user's emotions.

[0075] When providing the data, the providing unit can select the optimal provision method by referring to the user's past career information. The providing unit, for example, preferentially provides relevant career cases based on the user's past career information. For example, if the user previously worked as an "engineer," it preferentially provides career cases related to engineers. The providing unit can also refer to the user's past work history and provide career cases related to their work history. For example, if the user previously worked as a "marketing manager," it provides career cases related to marketing managers. The providing unit can also provide career cases that are useful for improving skills based on the user's past skill set. For example, if the user has "data analysis skills," it provides career cases that utilize data analysis skills. This makes it possible to select the optimal provision method by referring to the user's past career information.

[0076] The providing unit can customize the means for providing a career case based on the user's current living situation at the time of providing. For example, if the user is raising a child, the providing unit provides a career case that takes time constraints into consideration. For example, if the user is raising a child, the providing unit provides a career case that can be completed in a short period of time. Furthermore, if the user is searching for a new job, the providing unit can also provide a career case that can be completed quickly. For example, if the user is searching for a new job, the providing unit can provide a career case that can produce results in a short period of time. Furthermore, if the user desires remote work, the providing unit can also provide a career case that is suitable for remote work. For example, if the user desires remote work, the providing unit provides a career case that includes occupations and skills that are suitable for remote work. This makes it possible to customize the means for providing a career case according to the user's current living situation.

[0077] The providing unit can estimate the user's emotions and determine the priority of carrier cases based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing the most important carrier case. For example, if the user is feeling stressed, the generation AI can prioritize providing the most important carrier case. The providing unit can also prioritize providing a detailed carrier case if the user is relaxed. For example, if the user is relaxed, the generation AI can prioritize providing a detailed carrier case. The providing unit can also prioritize providing a carrier case that can be quickly executed if the user is in a hurry. For example, if the user is in a hurry, the generation AI can prioritize providing a carrier case that can be quickly executed. This makes it possible to determine the priority of carrier cases according to the user's emotions.

[0078] The providing unit can provide the optimal career case by taking into consideration the user's geographical location information. For example, the providing unit provides a region-specific career case based on the user's current location. For example, if the user inputs "Tokyo" as the current location, a career case related to Tokyo is provided. The providing unit can also provide career cases related to nearby job information and career events based on the user's geographical location information. For example, if the user inputs "Osaka Prefecture" as the current location, job information and career events related to Osaka Prefecture are provided. The providing unit can also provide a career case based on regional labor market trends by taking into consideration the user's geographical location information. For example, if the user inputs "Fukuoka Prefecture" as the current location, a career case based on labor market trends in Fukuoka Prefecture is provided. This makes it possible to provide the optimal career case by taking into consideration the user's geographical location information.

[0079] At the time of provision, the provision unit can analyze the user's social media activity to suggest a means for providing career cases. For example, the provision unit provides career cases related to occupations and skills of interest from the user's social media activity. For example, if the user has expressed an interest in "data science" on social media, career cases related to data science are provided. The provision unit can also suggest related career cases based on the user's social media network. For example, if the user has a network in the "IT industry," career cases related to the IT industry are suggested. The provision unit can also analyze the user's social media activity to provide career cases based on trends. For example, if the user has expressed an interest in "AI technology," career cases related to AI technology are provided. In this way, a means for providing career cases can be suggested by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives carrier information entered by a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the carrier information using a generation AI to generate an ideal carrier case. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated carrier case to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives carrier information input by voice from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the carrier information using a generation AI to generate an ideal carrier case. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated carrier case to the user by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives carrier information input by voice from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the carrier information using a generation AI to generate an ideal carrier case. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and displays the generated carrier case to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives carrier information input by voice from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the carrier information using a generation AI to generate an ideal carrier case. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated carrier case to the user by voice.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can also generate a career case by taking into consideration the user's hobbies and interests in addition to the user's career information. For example, if the user lists "photography" as a hobby, a career case including occupations and skills related to photography can be generated. If the user is interested in "outdoor activities," a career case including occupations and skills related to outdoor activities can be generated. Furthermore, if the user is interested in "cooking," a career case including occupations and skills related to cooking can be generated. This makes it possible to provide a career case that takes into consideration the user's hobbies and interests.

[0082] When providing the user's career case, the providing unit can suggest learning resources according to the user's learning style. For example, if the user has a visual learning style, the providing unit can suggest learning resources including video tutorials and infographics. If the user has an auditory learning style, the providing unit can suggest learning resources including podcasts and audiobooks. Furthermore, if the user has a hands-on learning style, the providing unit can suggest learning resources including workshops and hands-on sessions. This makes it possible to provide optimal learning resources according to the user's learning style.

[0083] The analysis unit can predict future career trends based on the user's career information and make suggestions to the user. For example, it can analyze current market trends and suggest to the user occupations and skills that are predicted to be in high demand in the future. It can also suggest occupations and skills that may emerge based on technological advances and social changes. It can also analyze industry trends related to the user's career path and suggest promising career paths for the future. This allows the user to understand future career trends and make appropriate career choices.

[0084] When providing the user's career case, the provision unit can suggest career support that utilizes the user's network. For example, the provision unit can suggest a mentorship program by utilizing the professional network of the user's industry. The provision unit can also suggest career advice and information exchange by utilizing online communities and forums in which the user participates. Furthermore, the provision unit can suggest participation in career-related events and seminars by utilizing the user's social media network. This allows the user to receive career support by utilizing their own network.

[0085] The analysis unit can simulate different career paths based on the user's career information and suggest them to the user. For example, if the user is interested in multiple career paths, the analysis unit can simulate and compare the future prospects, skills, and experience required for each career path. It can also simulate the risks and benefits of the user choosing a specific career path and provide this to the user. It can also simulate the impact of the user changing their career path and support the user in making the optimal career choice. This allows the user to compare different career paths and make the optimal career choice.

[0086] The providing unit can estimate the user's emotions and adjust the timing of providing the carrier case based on the estimated user's emotions. For example, if the user is feeling stressed, the carrier case can be provided at a time when the user can relax. Also, if the user is relaxed, the carrier case can be provided at a time when the user can concentrate. Furthermore, if the user is in a hurry, the carrier case can be provided quickly so that the user can immediately obtain the necessary information. This makes it possible to provide the carrier case at the optimal timing according to the user's emotions.

[0087] The analysis unit can estimate the user's emotions and adjust the content of the career case based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-execute career case can be provided. Alternatively, if the user is relaxed, a detailed and challenging career case can be provided. Furthermore, if the user is in a hurry, a quickly executable career case can be provided so that the user can quickly obtain the necessary information. In this way, the optimal career case can be provided according to the user's emotions.

[0088] The providing unit can estimate the user's emotions and adjust the carrier case feedback method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide feedback including a positive and encouraging message. If the user is relaxed, the providing unit can provide detailed and constructive feedback. Furthermore, if the user is in a hurry, the providing unit can provide concise and to-the-point feedback to help the user quickly move on to the next step. In this way, optimal feedback can be provided according to the user's emotions.

[0089] The analysis unit can estimate the user's emotions and adjust the difficulty of the carrier case based on the estimated user's emotions. For example, if the user is feeling stressed, a carrier case with a low level of difficulty can be provided. Alternatively, if the user is relaxed, a carrier case with a high level of difficulty can be provided. Furthermore, if the user is in a hurry, a carrier case that can be quickly executed can be provided so that the user can quickly obtain the necessary information. In this way, a carrier case with the optimal level of difficulty can be provided according to the user's emotions.

[0090] The providing unit can estimate the user's emotions and adjust the presentation method of the carrier case based on the estimated user's emotions. For example, if the user is feeling stressed, a presentation using visually easy-to-understand graphics or infographics can be provided. Alternatively, if the user is relaxed, a presentation including detailed text and data can be provided. Furthermore, if the user is in a hurry, a concise presentation that focuses on the main points can be provided to quickly convey information. This makes it possible to provide the optimal presentation method according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives career information from the user. The career information includes current occupation, desired occupation, skills, years of experience, desired annual salary, etc. The reception unit stores the information entered by the user in a database and sends it to the analysis unit. Step 2: The analysis unit uses generation AI to analyze the information received by the reception unit and generate an ideal career case. Based on the user's information, generation AI determines whether the user has the skills and experience necessary for the desired occupation and provides advice on how to acquire the necessary skills and experience. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to analyze the user's information and generate an ideal career case. Step 3: The providing unit provides the user with the career case generated by the analysis unit. The providing unit displays the generated career case on the user's device and presents specific steps and methods for acquiring the necessary skills and experience.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives carrier information from a user; an analysis unit that analyzes the information received by the reception unit and generates an ideal carrier case; a providing unit that provides the carrier case generated by the analyzing unit. A system characterized by:

2. The reception unit Accepts information about the user's current occupation, desired occupation, skills, years of experience, and desired annual salary The system of claim 1 .

3. The analysis unit Generate the ideal carrier case based on user information The system of claim 1 .

4. The providing unit Providing users with concrete steps to get the job they want and ways to acquire the necessary skills and experience The system of claim 1 .

5. The analysis unit Determine whether users have the skills and experience necessary for their desired career and provide advice on how to acquire the necessary skills and experience. The system of claim 1 .

6. The providing unit Provide the generated carrier case to the user The system of claim 1 .

7. The reception unit The user's emotions are estimated, and the career information input interface is customized based on the estimated user's emotions. The system of claim 1 .

8. The reception unit Analyzes the user's past career information and suggests input methods The system of claim 1 .

Images