system

Abstract

The system according to the embodiment aims to suggest the most suitable job type and company based on the user's skills and experience, and to predict the user's future career path. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, and a prediction unit. The reception unit inputs information about a user's skills or experience, desired job type, work location, and salary. The proposal unit analyzes the information input by the reception unit and proposes job types and companies based on the user's skills and experience. The prediction unit predicts the user's career path based on the job types and companies proposed by the proposal unit and provides advice on skills the user should acquire in the future.

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 technologies have had the problem of not being able to adequately suggest the most suitable jobs and companies based on a user's skills and experience, or predict their future career path.

[0005] The system according to the embodiment aims to suggest the most suitable job type and company based on the user's skills and experience, and to predict the user's future career path. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a prediction unit. The reception unit inputs information about the user's skills or experience, desired job type, work location, and salary. The proposal unit analyzes the information input by the reception unit and proposes job types and companies based on the user's skills and experience. The prediction unit predicts the user's career path based on the job types and companies proposed by the proposal unit and provides advice on skills the user should acquire in the future. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable job type and company based on the user's skills and experience, and predict the user's future career path. [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 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 matching system according to an embodiment of the present invention provides a career matching service using a generative AI. In this career matching system, users input information such as their skills, experience, desired job type, work location, and salary. The generative AI analyzes the input information and suggests optimal jobs and companies based on the user's skills and experience. This suggestion is made by identifying commonalities between the user's skills and experience and those required by companies. Furthermore, the generative AI predicts the user's career path and provides advice on the skills they should acquire in the future. This system allows users to find jobs and companies that best suit their skills and experience and supports their career growth. For example, users may input information such as skills, experience, desired job type, work location, and salary. The generative AI performs analysis based on this information. The analysis process can involve using a skill matrix to identify commonalities between the user's skills and the skills required by companies. It is also important to weight experience and evaluate the degree to which the user's experience matches the company's needs. Regarding career path prediction, advice on the skills the user should acquire in the future can be provided based on past data. For example, by referring to the career paths of past users with similar skill sets, the career matching system can support the user's career growth by suggesting the most suitable jobs and companies based on the user's skills and experience and predicting their career path.

[0029] A career matching system according to an embodiment includes a reception unit, a proposal unit, and a prediction unit. The reception unit inputs information about a user's skills or experience, desired job type, work location, and salary. The information input by the user includes, but is not limited to, technical skills, soft skills, work experience, project experience, desired job type, work location, and salary. The reception unit, for example, stores the information input by the user in a database and transmits it to the proposal unit for analysis. The proposal unit analyzes the information input by the reception unit and proposes job types and companies based on the user's skills and experience. The proposal unit, for example, uses a skill matrix to find commonalities between the user's skills and the skills required by companies. The skill matrix compares the user's skills with the skills required by companies based on skill evaluation criteria and a matrix structure. The proposal unit also weights experience and evaluates how well the user's experience matches the company's needs. The weighting of experience is based on, for example, years of experience and the importance of the project. The proposal unit proposes job types and companies that are most suitable for the user based on the analysis results. The prediction unit predicts the user's career path based on the job types and companies proposed by the suggestion unit, and advises the user on skills that the user should acquire in the future. The prediction unit advises the user on what skills the user should acquire in the future, for example, based on past data. The past data includes historical data and statistical data. The prediction unit can also refer to career paths taken by users with similar skill sets in the past. As a result, the career matching system according to the embodiment can support the user's career growth by proposing optimal job types and companies based on the user's skills and experience and predicting their career path.

[0030] The suggestion unit can use the skill matrix to find commonalities between the user's skills and the skills required by companies. The skill matrix compares the user's skills with the skills required by companies based on skill evaluation criteria and the matrix structure. For example, the skill matrix evaluates technical skills and soft skills and calculates the degree of match between the user's skills and the skills required by companies. The skill matrix can also find commonalities with the skills required by companies, taking into account the user's skill level and years of experience. As a result, the skill matrix can be used to efficiently find commonalities between the user's skills and the skills required by companies.

[0031] The suggestion unit can weight the experience and evaluate how well the user's experience matches the company's needs. The weighting of experience is performed, for example, based on years of experience or the importance of the project. The suggestion unit evaluates the user's experience and calculates how well it matches the company's needs. For example, the suggestion unit evaluates the user's work experience and project experience and compares it with the experience required by the company. The suggestion unit can also evaluate whether it matches the company's needs by taking into account the quality and results of the user's experience. In this way, by weighting the experience, it is possible to accurately evaluate how well the user's experience matches the company's needs.

[0032] The prediction unit can advise the user on the skills they should acquire in the future based on past data. Past data includes historical data and statistical data. The prediction unit advises the user on what skills they should acquire in the future based on past data. For example, the prediction unit refers to the career paths taken by users with similar skill sets in the past. The prediction unit can also analyze past data to identify skills that will be in high demand in the future and advise the user on them. This allows the system to accurately advise the user on the skills they should acquire in the future based on past data.

[0033] The prediction unit can refer to the career paths taken by users with similar skill sets in the past. The specific definition and evaluation criteria for a similar skill set must be clearly defined. For example, this includes the degree of matching of technical skills and similarity of experience. The prediction unit refers to the career paths taken by users with similar skill sets in the past. For example, the prediction unit predicts what skills a user should acquire in the future based on past data. The prediction unit can also analyze past career path data and suggest the most successful career path. This makes it possible to suggest more realistic career paths by referring to the career paths of similar users in the past.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the skills and experience that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the skills and experience that will be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested to the user by analyzing the past input history.

[0035] The reception unit can customize input items based on the user's current work situation and areas of interest at the time of input. For example, when the user inputs their current work situation, the reception unit automatically suggests related skills and experience. The reception unit can also preferentially display information about related occupations and companies based on the user's areas of interest. Furthermore, the reception unit can customize input items based on the user's work situation and areas of interest, allowing the user to efficiently input required information. This enables efficient information input by customizing input items based on the user's work situation and areas of interest.

[0036] The reception unit can prioritize displaying highly relevant input items based on the user's geographical location information when the user is entering information. For example, if the user is in a specific area, the reception unit can prioritize displaying information about occupations and companies related to that area. The reception unit can also prioritize displaying related skills and experience based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can also suggest optimal input items based on the user's current location. This allows highly relevant information to be displayed preferentially by taking the user's geographical location information into consideration.

[0037] The reception unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the reception unit can suggest job types and companies of interest based on the user's social media activity. The reception unit can also suggest related input items based on the user's social media skills and experience. Furthermore, the reception unit can analyze the user's social media activity and customize optimal input items. In this way, input items related to the user can be suggested by analyzing social media activity.

[0038] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the job type or company. For example, the suggestion unit provides detailed information for important job types or companies. The suggestion unit can also provide concise information for general job types or companies. Furthermore, the suggestion unit can also provide customized information for specific job types or companies. In this way, by adjusting the level of detail of the proposal based on the importance of the job type or company, it is possible to provide optimal information for the user.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the job type or company category. For example, the proposal unit can apply a proposal algorithm that emphasizes technical skills to technical jobs. The proposal unit can also apply a proposal algorithm that emphasizes management skills to managerial jobs. Furthermore, the proposal unit can apply a proposal algorithm that emphasizes creativity to creative jobs. In this way, by applying different proposal algorithms depending on the job type or company category, more appropriate proposals can be made.

[0040] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the job type and the company. For example, the suggestion unit gives priority to proposals for urgent job types and companies. The suggestion unit can also give priority to proposals for job types and companies with an upcoming submission deadline. Furthermore, the suggestion unit can postpone proposals for job types and companies with a distant submission deadline. In this way, by determining the priority of proposals based on the submission time of the job type and the company, it is possible to make optimal proposals for the user.

[0041] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of job types and companies. For example, the suggestion unit prioritizes suggesting job types and companies that are most relevant to the user's skills and experience. The suggestion unit can also prioritize suggesting information related to the user's desired job types and companies. Furthermore, the suggestion unit can also prioritize suggesting highly relevant job types and companies based on the user's past experience. This allows the suggestion unit to adjust the order of suggestions based on the relevance of job types and companies, making it possible to make optimal suggestions for the user.

[0042] At the time of prediction, the prediction unit can predict the current career path based on past career path data. For example, the prediction unit refers to the career paths taken by users with a similar skill set in the past. The prediction unit can also predict what skills the user should acquire in the future based on past data. Furthermore, the prediction unit can analyze past career path data and suggest the most successful career path. This allows the current career path to be accurately predicted by referring to past career path data.

[0043] When making predictions, the prediction unit can apply different prediction algorithms based on the user's skills and experience. For example, the prediction unit can apply a prediction algorithm specialized for technical jobs to a user with technical skills. The prediction unit can also apply a prediction algorithm specialized for managerial jobs to a user with managerial skills. Furthermore, the prediction unit can also apply a prediction algorithm specialized for creative jobs to a user with creative skills. In this way, by applying different prediction algorithms based on the user's skills and experience, a more appropriate career path can be predicted.

[0044] At the time of prediction, the prediction unit can analyze changes in the career path based on the user's submission time. For example, if the submission time is approaching, the prediction unit can preferentially suggest short-term career paths. Also, if the submission time is far away, the prediction unit can also suggest long-term career paths. Furthermore, the prediction unit can analyze changes in the career path based on the submission time and make optimal suggestions. In this way, by analyzing changes in the career path based on the user's submission time, it is possible to suggest optimal career paths.

[0045] At the time of prediction, the prediction unit can analyze the user's career path based on relevant market data. The prediction unit predicts the user's career path based on, for example, the relevant market data. The prediction unit can also propose an optimal career path taking market trends into consideration. Furthermore, the prediction unit can analyze the relevant market data and propose skills that will be in high demand in the future. In this way, the optimal career path for the user can be proposed by referring to the relevant market data.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can provide real-time feedback based on the user's input. For example, when a user inputs skills or experience, the reception unit instantly analyzes the content and displays a warning if the input is insufficient or inconsistent. The reception unit can also suggest that the user input additional related skills or experience based on the information entered by the user. Furthermore, the reception unit can evaluate the reliability of the input based on the information entered by the user and request additional confirmation if the reliability is low. This allows the user to enter more accurate and reliable information, improving the accuracy of career matching.

[0048] The suggestion unit can evaluate the possibility of changing jobs to different industries or occupations based on the user's skills and experience. For example, the suggestion unit can evaluate whether the user's technical skills are applicable to other industries and suggest a job change to a different industry. The suggestion unit can also evaluate whether the user's soft skills are applicable to other occupations and suggest a job change to a different occupation. Furthermore, the suggestion unit can analyze how well the user's experience is evaluated in other industries or occupations and suggest the possibility of a job change. This allows the user to find a new career path that makes the most of their skills and experience.

[0049] The prediction unit can take into account the user's lifestyle and personal goals when predicting the user's career path. For example, if the user has a family, the prediction unit can suggest a career path that emphasizes work-life balance. Also, if the user values ​​personal growth, the prediction unit can suggest a career path that includes skill development and qualification acquisition. Furthermore, if the user wishes to live in a specific area, the prediction unit can preferentially suggest a career path in that area. This makes it possible to increase user satisfaction by suggesting a career path that suits the user's lifestyle and personal goals.

[0050] The reception unit can suggest relevant educational programs and training courses based on the user's input. For example, if the user wants to improve a specific skill, the reception unit can suggest online courses and workshops related to that skill. Also, if the user wants to try a new field, the reception unit can suggest educational programs related to that field. Furthermore, if the user is considering a career change, the reception unit can suggest training courses to acquire the necessary qualifications and certifications. This allows the user to efficiently acquire the skills and knowledge necessary to achieve their career goals.

[0051] The prediction unit can take the user's health condition and lifestyle habits into consideration when predicting the user's career path. For example, if the user has health constraints, the prediction unit can suggest a career path that takes those constraints into consideration. Also, if the user maintains a healthy lifestyle, the prediction unit can suggest occupations and companies that can utilize those habits. Furthermore, if the user has specific health goals, the prediction unit can suggest a career path that will help the user achieve those goals. In this way, by suggesting a career path that suits the user's health condition and lifestyle habits, it is possible to increase user satisfaction.

[0052] The prediction unit can take the user's hobbies and interests into consideration when predicting the user's career path. For example, if the user has a specific hobby, the prediction unit can suggest occupations and companies related to that hobby. Also, if the user is interested in a specific field, the prediction unit can suggest career paths related to that field. Furthermore, if the user wants to take up a new hobby or interest, the prediction unit can suggest a career path for that. This can increase user satisfaction by suggesting career paths that match the user's hobbies and interests.

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

[0054] Step 1: The reception unit inputs the user's skills or experience, desired job type, work location, and salary information. The information input by the user includes, for example, technical skills, soft skills, work experience, project experience, desired job type, work location, salary, etc. The reception unit stores the information input by the user in a database and sends it to the proposal unit for analysis. Step 2: The proposal unit analyzes the information entered by the reception unit and suggests job types and companies based on the user's skills and experience. The proposal unit uses a skill matrix to find commonalities between the user's skills and the skills required by companies, weights the experience, and evaluates how well the user's experience matches the company's needs. Based on the analysis results, the proposal unit suggests the most suitable job types and companies for the user. Step 3: The prediction unit predicts the user's career path based on the job types and companies suggested by the suggestion unit, and advises on the skills they should acquire in the future. The prediction unit advises on what skills the user should acquire in the future based on past data, and refers to the career paths taken by users with similar skill sets in the past.

[0055] (Example 2) A career matching system according to an embodiment of the present invention provides a career matching service using a generative AI. In this career matching system, users input information such as their skills, experience, desired job type, work location, and salary. The generative AI analyzes the input information and suggests optimal jobs and companies based on the user's skills and experience. This suggestion is made by identifying commonalities between the user's skills and experience and those required by companies. Furthermore, the generative AI predicts the user's career path and provides advice on the skills they should acquire in the future. This system allows users to find jobs and companies that best suit their skills and experience and supports their career growth. For example, users may input information such as skills, experience, desired job type, work location, and salary. The generative AI performs analysis based on this information. The analysis process can involve using a skill matrix to identify commonalities between the user's skills and the skills required by companies. It is also important to weight experience and evaluate the degree to which the user's experience matches the company's needs. Regarding career path prediction, advice on the skills the user should acquire in the future can be provided based on past data. For example, by referring to the career paths of past users with similar skill sets, the career matching system can support the user's career growth by suggesting the most suitable jobs and companies based on the user's skills and experience and predicting their career path.

[0056] A career matching system according to an embodiment includes a reception unit, a proposal unit, and a prediction unit. The reception unit inputs information about a user's skills or experience, desired job type, work location, and salary. The information input by the user includes, but is not limited to, technical skills, soft skills, work experience, project experience, desired job type, work location, and salary. The reception unit, for example, stores the information input by the user in a database and transmits it to the proposal unit for analysis. The proposal unit analyzes the information input by the reception unit and proposes job types and companies based on the user's skills and experience. The proposal unit, for example, uses a skill matrix to find commonalities between the user's skills and the skills required by companies. The skill matrix compares the user's skills with the skills required by companies based on skill evaluation criteria and a matrix structure. The proposal unit also weights experience and evaluates how well the user's experience matches the company's needs. The weighting of experience is based on, for example, years of experience and the importance of the project. The proposal unit proposes job types and companies that are most suitable for the user based on the analysis results. The prediction unit predicts the user's career path based on the job types and companies proposed by the suggestion unit, and advises the user on skills that the user should acquire in the future. The prediction unit advises the user on what skills the user should acquire in the future, for example, based on past data. The past data includes historical data and statistical data. The prediction unit can also refer to career paths taken by users with similar skill sets in the past. As a result, the career matching system according to the embodiment can support the user's career growth by proposing optimal job types and companies based on the user's skills and experience and predicting their career path.

[0057] The suggestion unit can use the skill matrix to find commonalities between the user's skills and the skills required by companies. The skill matrix compares the user's skills with the skills required by companies based on skill evaluation criteria and the matrix structure. For example, the skill matrix evaluates technical skills and soft skills and calculates the degree of match between the user's skills and the skills required by companies. The skill matrix can also find commonalities with the skills required by companies, taking into account the user's skill level and years of experience. As a result, the skill matrix can be used to efficiently find commonalities between the user's skills and the skills required by companies.

[0058] The suggestion unit can weight the experience and evaluate how well the user's experience matches the company's needs. The weighting of experience is performed, for example, based on years of experience or the importance of the project. The suggestion unit evaluates the user's experience and calculates how well it matches the company's needs. For example, the suggestion unit evaluates the user's work experience and project experience and compares it with the experience required by the company. The suggestion unit can also evaluate whether it matches the company's needs by taking into account the quality and results of the user's experience. In this way, by weighting the experience, it is possible to accurately evaluate how well the user's experience matches the company's needs.

[0059] The prediction unit can advise the user on the skills they should acquire in the future based on past data. Past data includes historical data and statistical data. The prediction unit advises the user on what skills they should acquire in the future based on past data. For example, the prediction unit refers to the career paths taken by users with similar skill sets in the past. The prediction unit can also analyze past data to identify skills that will be in high demand in the future and advise the user on them. This allows the system to accurately advise the user on the skills they should acquire in the future based on past data.

[0060] The prediction unit can refer to the career paths taken by users with similar skill sets in the past. The specific definition and evaluation criteria for a similar skill set must be clearly defined. For example, this includes the degree of matching of technical skills and similarity of experience. The prediction unit refers to the career paths taken by users with similar skill sets in the past. For example, the prediction unit predicts what skills a user should acquire in the future based on past data. The prediction unit can also analyze past career path data and suggest the most successful career path. This makes it possible to suggest more realistic career paths by referring to the career paths of similar users in the past.

[0061] The reception unit can estimate the user's emotions and adjust the display method of the input form based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This improves the user's input experience by adjusting the display method of the input form according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the skills and experience that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the skills and experience that will be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested to the user by analyzing the past input history.

[0063] The reception unit can customize input items based on the user's current work situation and areas of interest at the time of input. For example, when the user inputs their current work situation, the reception unit automatically suggests related skills and experience. The reception unit can also preferentially display information about related occupations and companies based on the user's areas of interest. Furthermore, the reception unit can customize input items based on the user's work situation and areas of interest, allowing the user to efficiently input required information. This enables efficient information input by customizing input items based on the user's work situation and areas of interest.

[0064] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying important input items and postpone other items. Furthermore, when the user is relaxed, the reception unit can prioritize displaying detailed input items and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying the most important input items to enable quick information entry. This improves the user's input experience by prioritizing input items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The reception unit can prioritize displaying highly relevant input items based on the user's geographical location information when the user is entering information. For example, if the user is in a specific area, the reception unit can prioritize displaying information about occupations and companies related to that area. The reception unit can also prioritize displaying related skills and experience based on the user's geographical location information. Furthermore, if the user is traveling, the reception unit can also suggest optimal input items based on the user's current location. This allows highly relevant information to be displayed preferentially by taking the user's geographical location information into consideration.

[0066] The reception unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the reception unit can suggest job types and companies of interest based on the user's social media activity. The reception unit can also suggest related input items based on the user's social media skills and experience. Furthermore, the reception unit can analyze the user's social media activity and customize optimal input items. In this way, input items related to the user can be suggested by analyzing social media activity.

[0067] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also make suggestions that focus on the main points. This allows the suggestion unit to adjust the way suggestions are expressed according to the user's emotions, making it possible to provide optimal suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0068] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the job type or company. For example, the suggestion unit provides detailed information for important job types or companies. The suggestion unit can also provide concise information for general job types or companies. Furthermore, the suggestion unit can also provide customized information for specific job types or companies. In this way, by adjusting the level of detail of the proposal based on the importance of the job type or company, it is possible to provide optimal information for the user.

[0069] When making a proposal, the proposal unit can apply different proposal algorithms depending on the job type or company category. For example, the proposal unit can apply a proposal algorithm that emphasizes technical skills to technical jobs. The proposal unit can also apply a proposal algorithm that emphasizes management skills to managerial jobs. Furthermore, the proposal unit can apply a proposal algorithm that emphasizes creativity to creative jobs. In this way, by applying different proposal algorithms depending on the job type or company category, more appropriate proposals can be made.

[0070] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can also make suggestions with visually stimulating effects. This allows the suggestion to be optimized for the user by adjusting the length of the suggestion according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the job type and the company. For example, the suggestion unit gives priority to proposals for urgent job types and companies. The suggestion unit can also give priority to proposals for job types and companies with an upcoming submission deadline. Furthermore, the suggestion unit can postpone proposals for job types and companies with a distant submission deadline. In this way, by determining the priority of proposals based on the submission time of the job type and the company, it is possible to make optimal proposals for the user.

[0072] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of job types and companies. For example, the suggestion unit prioritizes suggesting job types and companies that are most relevant to the user's skills and experience. The suggestion unit can also prioritize suggesting information related to the user's desired job types and companies. Furthermore, the suggestion unit can also prioritize suggesting highly relevant job types and companies based on the user's past experience. This allows the suggestion unit to adjust the order of suggestions based on the relevance of job types and companies, making it possible to make optimal suggestions for the user.

[0073] The prediction unit can estimate the user's emotions and adjust the display method of the career path based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the career path according to the user's emotions, the optimal career path for the user is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] At the time of prediction, the prediction unit can predict the current career path based on past career path data. For example, the prediction unit refers to the career paths taken by users with a similar skill set in the past. The prediction unit can also predict what skills the user should acquire in the future based on past data. Furthermore, the prediction unit can analyze past career path data and suggest the most successful career path. This allows the current career path to be accurately predicted by referring to past career path data.

[0075] When making predictions, the prediction unit can apply different prediction algorithms based on the user's skills and experience. For example, the prediction unit can apply a prediction algorithm specialized for technical jobs to a user with technical skills. The prediction unit can also apply a prediction algorithm specialized for managerial jobs to a user with managerial skills. Furthermore, the prediction unit can also apply a prediction algorithm specialized for creative jobs to a user with creative skills. In this way, by applying different prediction algorithms based on the user's skills and experience, a more appropriate career path can be predicted.

[0076] The prediction unit can estimate the user's emotions and adjust the importance of career paths based on the estimated user emotions. For example, if the user is nervous, the prediction unit can prioritize and display important career paths. The prediction unit can also display detailed career paths when the user is relaxed. Furthermore, if the user is in a hurry, the prediction unit can display a career path that focuses on the main points. In this way, by adjusting the importance of career paths according to the user's emotions, the optimal career path for the user is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] At the time of prediction, the prediction unit can analyze changes in the career path based on the user's submission time. For example, if the submission time is approaching, the prediction unit can preferentially suggest short-term career paths. Also, if the submission time is far away, the prediction unit can also suggest long-term career paths. Furthermore, the prediction unit can analyze changes in the career path based on the submission time and make optimal suggestions. In this way, by analyzing changes in the career path based on the user's submission time, it is possible to suggest optimal career paths.

[0078] At the time of prediction, the prediction unit can analyze the user's career path based on relevant market data. The prediction unit predicts the user's career path based on, for example, the relevant market data. The prediction unit can also propose an optimal career path taking market trends into consideration. Furthermore, the prediction unit can analyze the relevant market data and propose skills that will be in high demand in the future. In this way, the optimal career path for the user can be proposed by referring to the relevant market data. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, proposal unit, and prediction 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 control unit 46A of the smart device 14, and stores information entered by the user in the database 24 and transmits it to the proposal unit. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a skill matrix to find commonalities between the user's skills and the skills required by companies and weights experience. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the user's career path based on past data and provides advice on skills to be acquired in the future. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, proposal unit, and prediction 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 control unit 46A of the smart glasses 214, stores information entered by the user in the database 24, and transmits it to the proposal unit. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a skill matrix to find commonalities between the user's skills and the skills required by companies and weights experience. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the user's career path based on past data and provides advice on skills to be acquired in the future. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, and prediction 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 control unit 46A of the headset-type terminal 314, and stores information entered by the user in the database 24 and transmits it to the proposal unit. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a skill matrix to find common points between the user's skills and the skills required by companies and weights experience. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the user's career path based on past data and provides advice on skills to be acquired in the future. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, and prediction 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 control unit 46A of the robot 414, and stores information entered by the user in the database 24 and transmits it to the proposal unit. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a skill matrix to find common points between the user's skills and the skills required by companies and weights experience. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the user's career path based on past data and provides advice on skills to be acquired in the future.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The reception unit can provide real-time feedback based on the user's input. For example, when a user inputs skills or experience, the reception unit instantly analyzes the content and displays a warning if the input is insufficient or inconsistent. The reception unit can also suggest that the user input additional related skills or experience based on the information entered by the user. Furthermore, the reception unit can evaluate the reliability of the input based on the information entered by the user and request additional confirmation if the reliability is low. This allows the user to enter more accurate and reliable information, improving the accuracy of career matching.

[0081] The suggestion unit can evaluate the possibility of changing jobs to different industries or occupations based on the user's skills and experience. For example, the suggestion unit can evaluate whether the user's technical skills are applicable to other industries and suggest a job change to a different industry. The suggestion unit can also evaluate whether the user's soft skills are applicable to other occupations and suggest a job change to a different occupation. Furthermore, the suggestion unit can analyze how well the user's experience is evaluated in other industries or occupations and suggest the possibility of a job change. This allows the user to find a new career path that makes the most of their skills and experience.

[0082] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit temporarily refrains from making suggestions and makes suggestions when the user is relaxed. Also, if the user is excited, the suggestion unit can make suggestions immediately to attract the user's interest. Furthermore, if the user is concentrating, the suggestion unit can make suggestions including detailed information, providing the user with time to think deeply. In this way, by adjusting the timing of suggestions according to the user's emotions, it is possible to provide optimal suggestions for the user.

[0083] The prediction unit can take into account the user's lifestyle and personal goals when predicting the user's career path. For example, if the user has a family, the prediction unit can suggest a career path that emphasizes work-life balance. Also, if the user values ​​personal growth, the prediction unit can suggest a career path that includes skill development and qualification acquisition. Furthermore, if the user wishes to live in a specific area, the prediction unit can preferentially suggest a career path in that area. This makes it possible to increase user satisfaction by suggesting a career path that suits the user's lifestyle and personal goals.

[0084] The prediction unit can estimate the user's emotions and perform a risk assessment of career paths based on the estimated user emotions. For example, if the user feels anxious, the prediction unit will preferentially suggest low-risk career paths. Also, if the user feels adventurous, the prediction unit can suggest a career path that is risky but has the potential for high returns. Furthermore, if the user seeks stability, the prediction unit can suggest a stable career path. In this way, by assessing the risks of career paths according to the user's emotions, it is possible to provide the optimal career path for the user.

[0085] The reception unit can suggest relevant educational programs and training courses based on the user's input. For example, if the user wants to improve a specific skill, the reception unit can suggest online courses and workshops related to that skill. Also, if the user wants to try a new field, the reception unit can suggest educational programs related to that field. Furthermore, if the user is considering a career change, the reception unit can suggest training courses to acquire the necessary qualifications and certifications. This allows the user to efficiently acquire the skills and knowledge necessary to achieve their career goals.

[0086] The suggestion unit can estimate the user's emotions and personalize the content of suggestions based on the estimated user emotions. For example, if the user is feeling motivated, the suggestion unit can suggest challenging jobs or projects. If the user is tired, the suggestion unit can suggest a relaxing work environment or flexible working style. Furthermore, if the user is excited, the suggestion unit can suggest creative jobs or projects. In this way, by personalizing the content of suggestions according to the user's emotions, it is possible to make suggestions that are optimal for the user.

[0087] The prediction unit can take the user's health condition and lifestyle habits into consideration when predicting the user's career path. For example, if the user has health constraints, the prediction unit can suggest a career path that takes those constraints into consideration. Also, if the user maintains a healthy lifestyle, the prediction unit can suggest occupations and companies that can utilize those habits. Furthermore, if the user has specific health goals, the prediction unit can suggest a career path that will help the user achieve those goals. In this way, by suggesting a career path that suits the user's health condition and lifestyle habits, it is possible to increase user satisfaction.

[0088] The suggestion unit can estimate the user's emotions and adjust the order of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the most important suggestions can be made first, with other suggestions postponed. Also, if the user is relaxed, suggestions including detailed information can be made sequentially. Furthermore, if the user is in a hurry, suggestions that focus on the main points can be made preferentially. In this way, by adjusting the order of suggestions according to the user's emotions, it is possible to provide optimal suggestions for the user.

[0089] The prediction unit can take the user's hobbies and interests into consideration when predicting the user's career path. For example, if the user has a specific hobby, the prediction unit can suggest occupations and companies related to that hobby. Also, if the user is interested in a specific field, the prediction unit can suggest career paths related to that field. Furthermore, if the user wants to take up a new hobby or interest, the prediction unit can suggest a career path for that. This can increase user satisfaction by suggesting career paths that match the user's hobbies and interests.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The reception unit inputs the user's skills or experience, desired job type, work location, and salary information. The information input by the user includes, for example, technical skills, soft skills, work experience, project experience, desired job type, work location, salary, etc. The reception unit stores the information input by the user in a database and sends it to the proposal unit for analysis. Step 2: The proposal unit analyzes the information entered by the reception unit and suggests job types and companies based on the user's skills and experience. The proposal unit uses a skill matrix to find commonalities between the user's skills and the skills required by companies, weights the experience, and evaluates how well the user's experience matches the company's needs. Based on the analysis results, the proposal unit suggests the most suitable job types and companies for the user. Step 3: The prediction unit predicts the user's career path based on the job types and companies suggested by the suggestion unit, and advises on the skills they should acquire in the future. The prediction unit advises on what skills the user should acquire in the future based on past data, and refers to the career paths taken by users with similar skill sets in the past.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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, in order to avoid confusion and to 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 section for inputting information on the user's skills or experience, desired job type, work location, and salary; a suggestion unit that analyzes the information input by the reception unit and suggests occupations and companies based on the user's skills and experience; a prediction unit that predicts the user's career path based on the job types and companies proposed by the proposal unit and provides advice on skills that the user should acquire in the future. A system characterized by:

2. The proposal unit Use a skills matrix to find commonalities between user skills and the skills companies are looking for 2. The system of claim 1.

3. The proposal unit Experience weighting to assess how well the user experience meets the needs of the business 2. The system of claim 1.

4. The prediction unit Advise users on the skills they should acquire in the future based on past data 2. The system of claim 1.

5. The prediction unit Learn from the career paths of past users with similar skill sets 2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting the display of input forms based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

8. The reception unit As you type, customize the input fields based on your current job status or interests 2. The system of claim 1.

Images