System

Abstract

An object of a system according to an embodiment is to specify an optimal occupation or career path on the basis of a skill of a user and a career goal and provide an individually corresponding career plan.SOLUTION: A system includes a reception unit, an analysis unit, a specification unit, and a provision unit. The reception unit receives input of a skill and a career goal of a user. The analysis unit analyzes the information received by the reception unit. The specification unit specifies an appropriate job type or carrier path based on the information analyzed by the analysis unit. The providing unit provides a specific career development plan based on the job or the career path specified by the specifying unit.SELECTED DRAWING: Figure 1

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 making it difficult to identify the optimal job type and career path based on a user's skills and career goals, and have not provided sufficient support for individualized career planning.

[0005] The system according to the embodiment aims to identify the most suitable occupation and career path based on the user's skills and career goals, and provide an individualized career plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives input of a user's skills and career goals. The analysis unit analyzes the information received by the reception unit. The identification unit identifies an appropriate job type and career path based on the information analyzed by the analysis unit. The provision unit provides a specific career development plan based on the job type and career path identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the most suitable job type and career path based on the user's skills and career goals, and provide an individualized career plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A career support system according to an embodiment of the present invention identifies the optimal occupation and career path based on a user's skills and career goals, and provides a specific career development plan. The career support system accepts input of the user's skills and career goals, analyzes them using a generation AI, identifies the optimal occupation and career path, and provides a specific career development plan. For example, the career support system allows a user to input their own skills and career goals. For example, they input programming skills, project management experience, and a career goal such as a desire to demonstrate leadership in the future. This information is input to the generation AI. The career support system then uses the generation AI to analyze the input information and identify the optimal occupation and career path for the user. The generation AI then suggests the occupation and career path that best suits the user's skills and career goals based on past data and trends. For example, a user with strong programming skills and a desire to demonstrate leadership may be suggested to take the role of software development team leader. Furthermore, the career support system uses the generation AI to provide the user with a specific career development plan. For example, the system suggests a training program to improve specific skills or a step-by-step plan to reach a career goal. This allows the user to concretely advance their own career development. For example, new graduates and job seekers can find the most suitable job based on their skills and career goals. It can also help people considering a career change find a new career path. This allows the career support system to provide personalized career planning support and help people with their career development. For example, new graduates and job seekers can find the most suitable job based on their skills and career goals. It can also help people considering a career change find a new career path.

[0029] A career support system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives input of a user's skills and career goals. The user's skills include, but are not limited to, technical skills and soft skills. The career goals include, but are not limited to, short-term goals and long-term goals. The reception unit, for example, stores the skills and career goals input by the user in a database. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit, for example, analyzes the user's skills and career goals to identify the user's strengths and weaknesses. The analysis unit can also use the generation AI to analyze past data and trends based on the user's skills and career goals. For example, the analysis unit analyzes the user's skill set and identifies what type of job is suitable for the user. The identification unit, using the generation AI, identifies the optimal job type and career path based on the information analyzed by the analysis unit. The identification unit, for example, suggests the optimal job type and career path based on the user's skills and career goals. The identification unit can also use the generation AI to identify an occupation or career path that is most suitable for the user's skills and career goals. For example, the identification unit suggests an optimal occupation based on the user's skill set and career goals. The provision unit uses the generation AI to provide a specific career development plan based on the occupation or career path identified by the identification unit. The provision unit can, for example, propose a training program for improving specific skills or a step-by-step plan for reaching the career goal. The provision unit can also use the generation AI to provide a specific career development plan to the user. For example, the provision unit suggests what kind of training program is suitable based on the user's skill set. As a result, the career support system according to the embodiment can identify an optimal occupation or career path based on the user's skills and career goals and provide a specific career development plan.

[0030] The career support system includes a reception unit that analyzes a user's past input history and selects an appropriate input method. The reception unit analyzes the user's past input history and selects an appropriate input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception unit can automatically display skills and career goals that the user has previously entered as candidates. This improves the user's input efficiency by selecting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data into a generation AI and have the generation AI select the optimal input method.

[0031] The career support system includes a reception unit that filters skills and career goals based on the user's current occupation and areas of interest when the skills and career goals are input. The reception unit filters the skills and career goals based on the user's current occupation and areas of interest when the skills and career goals are input. For example, skills and career goals related to the user's current occupation are preferentially displayed. The reception unit can also suggest related skills and career goals based on the user's areas of interest. For example, the user's occupational history can be used to filter appropriate skills and career goals. This allows the user to input more relevant skills and career goals by filtering based on the user's current occupation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's occupational history data to a generation AI and have the generation AI perform filtering.

[0032] The career support system includes a reception unit that selects an appropriate input means according to the user's input method when inputting skills and career goals. The reception unit selects an appropriate input means according to the user's input method when inputting skills and career goals. For example, if the user desires voice input, the reception unit provides a voice input interface. Furthermore, if the user desires text input, the reception unit can provide a text input interface. Furthermore, if the user desires image input, the reception unit can also support input using image recognition technology. For example, if the user desires voice input, the reception unit converts the voice into text using voice recognition technology. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0033] The career support system includes a reception unit that, when inputting skills and career goals, prioritizes inputting highly relevant information taking into account the user's geographical location information. When inputting skills and career goals, the reception unit prioritizes inputting highly relevant information taking into account the user's geographical location information. For example, the reception unit suggests skills and career goals specific to a region based on the user's current location. The reception unit can also prioritize displaying nearby job information based on the user's geographical location information. For example, the reception unit can suggest skills and career goals that match local trends based on the user's geographical location information. This allows the system to reflect region-specific skills and career goals by inputting highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0034] The career support system includes a reception unit that analyzes a user's social media activity and inputs related information when skills and career goals are input. The reception unit analyzes the user's social media activity and inputs related information when skills and career goals are input. For example, the reception unit analyzes the user's social media posts and suggests related skills and career goals. The reception unit can also suggest related skills and career goals by referring to the activities of the user's friends on social media. For example, related skills and career goals can be suggested based on the user's social media check-in information. In this way, by analyzing the user's social media activity, more relevant skills and career goals can be suggested. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media data to a generation AI and have the generation AI select related information.

[0035] The career support system includes a reception unit that customizes an input method by reflecting a user's past feedback when inputting skills and career goals. The reception unit customizes the input method by reflecting a user's past feedback when inputting skills and career goals. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. For example, the input procedure can be optimized by referring to the user's past feedback. This optimizes the input method by reflecting the user's past feedback, improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0036] The career support system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of skills and career goals during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of skills and career goals during analysis. For example, the analysis unit performs a detailed analysis of important skills and career goals. The analysis unit can also perform a simplified analysis of general skills and career goals. For example, the analysis unit can perform a particularly detailed analysis of skills that are directly related to the user's career goals. By adjusting the level of detail of the analysis based on the importance of skills and career goals, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input importance data of skills and career goals into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] The career support system includes an analysis unit that applies different analysis algorithms depending on the category of skills and career goals during analysis. The analysis unit applies different analysis algorithms depending on the category of skills and career goals during analysis. For example, the analysis unit applies a technical analysis algorithm to technical skills. The analysis unit can apply an analysis algorithm related to leadership to management skills. The analysis unit can apply an analysis algorithm related to creativity to creative skills. In this way, by applying different analysis algorithms depending on the category of skills and career goals, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input category data of skills and career goals into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] The career support system includes an analysis unit that improves the accuracy of analysis by referring to the user's past analysis results during analysis. The analysis unit improves the accuracy of analysis by referring to the user's past analysis results during analysis. For example, the accuracy of the current analysis is improved based on the user's past analysis results. The analysis unit can also analyze trends from the user's past analysis results and improve accuracy. For example, the analysis algorithm can be optimized by referring to the user's past analysis results. This makes it possible to improve the accuracy of analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] The career support system includes an analysis unit that determines the analysis priority based on the submission time of skills and career goals during analysis. The analysis unit determines the analysis priority based on the submission time of skills and career goals during analysis. For example, the analysis unit prioritizes the most recently submitted skills and career goals. Furthermore, skills and career goals that were submitted earlier can be analyzed later. For example, the analysis schedule can be adjusted based on the submission time. This enables more efficient analysis by determining the analysis priority based on the submission time of skills and career goals. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0040] The career support system includes an analysis unit that adjusts the order of analysis based on the relevance of skills and career goals during analysis. The analysis unit adjusts the order of analysis based on the relevance of skills and career goals during analysis. For example, highly relevant skills and career goals are analyzed preferentially. Less relevant skills and career goals can be analyzed later. For example, the order of analysis can be optimized based on the relevance. By adjusting the order of analysis based on the relevance of skills and career goals, more relevant analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs relevance data of skills and career goals into the generation AI and causes the generation AI to adjust the order of analysis.

[0041] The career support system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user's level of expertise is high, the analysis unit performs an analysis that makes heavy use of technical terms. On the other hand, if the user's level of expertise is low, the analysis unit can perform an analysis that avoids technical terms. For example, the method of expression of the analysis can be adjusted according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0042] The career support system includes an identification unit that, during identification, improves the accuracy of identification by taking into account the interrelationships between skills and career goals. The identification unit improves the accuracy of identification by taking into account the interrelationships between skills and career goals. For example, the identification unit analyzes the interrelationships between skills and career goals to improve the accuracy of identification. The identification unit can also improve the accuracy of identification by taking into account the associations between skills and career goals. For example, the identification algorithm can be optimized based on the interrelationships between skills and career goals. This improves the accuracy of identification by taking into account the interrelationships between skills and career goals. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input interrelationship data between skills and career goals into the generation AI and cause the generation AI to improve the accuracy of identification.

[0043] The career support system includes an identification unit that, at the time of identification, takes into consideration attribute information of the submitter of skills and career goals. The identification unit, at the time of identification, takes into consideration attribute information of the submitter of skills and career goals. For example, the identification unit takes into consideration the submitter's age and gender. The identification unit can also take into consideration the submitter's occupation and experience. The identification unit can also take into consideration the submitter's region and cultural background. This enables more individualized identification by taking into consideration the submitter's attribute information. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the submitter's attribute information data into the generation AI and cause the generation AI to execute identification.

[0044] The career support system includes an identification unit that, at the time of identification, assigns specific weights based on the frequency of submission of skills and career goals. The identification unit, at the time of identification, assigns specific weights based on the frequency of submission of skills and career goals. For example, the identification unit assigns weights to skills and career goals that are submitted frequently. The identification unit can also assign weights to skills and career goals that are submitted infrequently. For example, the identification algorithm can be optimized based on the frequency of submission. As a result, weighting based on the frequency of submission improves the accuracy of identification. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input submission frequency data to the generation AI and cause the generation AI to perform the specific weighting.

[0045] The career support system includes an identification unit that performs identification taking into account the geographic distribution of skills and career goals during identification. The identification unit performs identification taking into account the geographic distribution of skills and career goals during identification. For example, the identification unit identifies relevant skills and career goals based on the geographic distribution of users. The identification unit can also identify region-specific skills and career goals by taking the geographic distribution into account. For example, the identification algorithm can be optimized based on the geographic distribution. This enables identification that reflects region-specific skills and career goals by taking the geographic distribution into account. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit can input geographic distribution data into the generation AI and cause the generation AI to execute identification.

[0046] The career support system includes an identification unit that, during identification, improves the accuracy of identification by referring to literature related to skills and career goals. The identification unit, during identification, improves the accuracy of identification by referring to literature related to skills and career goals. For example, the identification unit improves the accuracy of identification of skills and career goals by referring to related literature. The identification unit can also optimize the identification algorithm based on the related literature. The identification unit can also adjust the identification criteria by referring to the related literature. As a result, the accuracy of identification is improved by referring to the related literature. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of identification.

[0047] The career support system includes an identification unit that performs identification taking into account the market value of skills and career goals during identification. The identification unit performs identification taking into account the market value of skills and career goals during identification. For example, it prioritizes identifying skills and career goals with high market value. In addition, it can postpone identification of skills and career goals with low market value. For example, it is possible to optimize the identification algorithm based on market value. In this way, by taking market value into account, it is possible to identify more valuable skills and career goals. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit may input market value data into the generation AI and cause the generation AI to execute the identification.

[0048] The career support system includes a providing unit that, when providing information, improves the accuracy of the information provided by taking into account the interrelationships between skills and career goals. The providing unit, when providing information, improves the accuracy of the information provided by taking into account the interrelationships between skills and career goals. For example, the providing unit analyzes the interrelationships between skills and career goals to improve the accuracy of the information provided. The providing unit can also improve the accuracy of the information provided by taking into account the associations between skills and career goals. For example, the providing algorithm can be optimized based on the interrelationships between skills and career goals. This improves the accuracy of the information provided by taking into account the interrelationships between skills and career goals. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input interrelationship data between skills and career goals into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0049] The career support system includes a providing unit that provides skills and career goals while taking into consideration attribute information of the submitter. The providing unit provides skills and career goals while taking into consideration attribute information of the submitter. For example, the providing unit provides the skills and career goals while taking into consideration the submitter's age and gender. The providing unit can also provide the skills and career goals while taking into consideration the submitter's occupation and experience. The providing unit can also provide the skills and career goals while taking into consideration the submitter's region and cultural background. This enables more personalized provision by taking into consideration the submitter's attribute information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the submitter's attribute information data into the generation AI and cause the generation AI to execute the provision.

[0050] The career support system includes a providing unit that weights the provision of skills and career goals based on the frequency of their submission at the time of provision. The providing unit weights the provision of skills and career goals based on the frequency of their submission at the time of provision. For example, the providing unit weights skills and career goals that are submitted frequently. The providing unit can also weight skills and career goals that are submitted infrequently. For example, the providing algorithm can be optimized based on the frequency of submission. As a result, weighting based on the frequency of submission improves the accuracy of the provision. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input submission frequency data to the generation AI and cause the generation AI to perform the weighting of the provision.

[0051] The career support system includes a provision unit that provides skills and career goals while taking into account the geographic distribution of the skills and career goals. The provision unit provides the skills and career goals while taking into account the geographic distribution of the skills and career goals. For example, the provision unit provides related skills and career goals based on the geographic distribution of users. The provision unit can also provide region-specific skills and career goals while taking into account the geographic distribution. For example, the provision algorithm can be optimized based on the geographic distribution. This makes it possible to provide skills and career goals that reflect region-specific skills and career goals by taking the geographic distribution into account. Some or all of the above-described processing in the provision unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the provision unit can input geographic distribution data into the generation AI and cause the generation AI to execute the provision.

[0052] The career support system includes a providing unit that, at the time of providing, improves the accuracy of the provision by referring to literature related to skills and career goals. The providing unit, at the time of providing, improves the accuracy of the provision by referring to literature related to skills and career goals. For example, the accuracy of the provision of skills and career goals is improved by referring to related literature. The providing unit can also optimize the providing algorithm based on the related literature. The providing unit can also adjust the provision criteria by referring to related literature. As a result, the accuracy of the provision is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0053] The career support system includes a provision unit that provides skills and career goals while taking into consideration the market value of the skills and career goals. The provision unit provides skills and career goals while taking into consideration the market value of the skills and career goals. For example, skills and career goals with high market value are provided preferentially. Skills and career goals with low market value can be provided later. For example, the provision algorithm can be optimized based on market value. This makes it possible to provide more valuable skills and career goals by taking market value into consideration. Some or all of the above-described processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input market value data into the generation AI and have the generation AI execute the provision.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The career support system can also have a function that utilizes the user's network based on the user's skills and career goals. For example, it can analyze the user's personal connections and past colleagues to identify connections that will be useful for the user's career goals. It can also broaden the user's career path options through recommendations and introductions within the user's network. It can also utilize the user's network to provide information on industry trends and the latest job openings. This allows users to make the most of their own networks and build their careers more effectively.

[0056] The career support system can also analyze a user's past input history and provide a career development plan tailored to the user's learning style. For example, if the user has a visual learning style, a plan that makes extensive use of visual aids and infographics can be provided. If the user has an auditory learning style, a plan in audio guide or podcast format can be provided. Furthermore, if the user has a practical learning style, a plan that includes hands-on workshops and on-the-job training can be provided. This makes it possible to provide a career development plan that is optimal for the user's learning style and support effective skill acquisition.

[0057] When entering skills and career goals, the career support system can also provide advice from relevant industry experts based on the user's current occupation and areas of interest. For example, if a user is interested in the IT industry, advice from and interview articles about IT industry experts can be provided. If a user is interested in the medical industry, advice from medical industry experts and the latest research results can be provided. Furthermore, if a user is interested in the creative industry, advice and success stories from creative industry experts can be provided. This allows users to obtain specialized knowledge and advice related to their areas of interest and set more specific career goals.

[0058] The career support system can also provide interactive guidance depending on the user's input method when entering skills and career goals. For example, if the user prefers voice input, a voice guide can provide real-time feedback. If the user prefers text input, an interactive chatbot can support the input by answering questions. Furthermore, if the user prefers image input, image recognition technology can be used to automatically extract related information and assist with input. This makes it possible to provide interactive guidance depending on the user's input method, improving input convenience.

[0059] When entering skills and career goals, the career support system can also suggest local career events and networking opportunities by taking the user's geographic location information into consideration. For example, if a user lives in a specific area, the system can provide information on career fairs and seminars held in that area. It can also suggest events held by nearby professional groups and industry associations based on the user's geographic location information. Furthermore, if the user is considering relocating, the system can provide information on career opportunities and lifestyles in the area to which the user is planning to relocate. This allows the user to take advantage of local career events and networking opportunities to advance their career development.

[0060] When a user enters their skills and career goals, the career support system can analyze the user's social media activity and suggest relevant online communities and forums based on the user's interests. For example, if a user is interested in a specific technology, the system can provide links to online communities and forums related to that technology. Also, if a user is interested in a specific industry, the system can suggest discussion groups and networking events related to that industry. Furthermore, if a user has a specific career goal, the system can provide success stories and inspiration related to that career goal. This allows users to take advantage of online communities and forums based on their interests and advance their career development.

[0061] The career support system can also customize the content and format of the career advice provided to the user by reflecting the user's past feedback when the user inputs skills and career goals. For example, the content of the advice can be adjusted based on the user's past feedback to provide more specific and practical advice. The system can also provide the advice in different formats, such as text, audio, or video, based on the user's feedback. Furthermore, the frequency and timing of the advice can be adjusted based on the user's feedback. This makes it possible to provide customized career advice that reflects the user's past feedback, improving user convenience.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit accepts input of the user's skills and career goals. The user's skills include technical skills and soft skills, and the career goals include short-term and long-term goals. The reception unit saves the skills and career goals entered by the user in a database. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis unit analyzes the user's skills and career goals to identify their strengths and weaknesses. It also analyzes past data and trends to identify suitable occupations based on the user's skill set. Step 3: The identification unit uses the generation AI to identify the optimal occupation and career path based on the information analyzed by the analysis unit. The identification unit proposes the optimal occupation and career path based on the user's skills and career goals. Step 4: The delivery department uses generative AI to provide a specific career development plan based on the job types and career paths identified by the identification department. The delivery department suggests training programs to improve specific skills and a step-by-step plan to reach career goals.

[0064] (Example 2) A career support system according to an embodiment of the present invention identifies the optimal occupation and career path based on a user's skills and career goals, and provides a specific career development plan. The career support system accepts input of the user's skills and career goals, analyzes them using a generation AI, identifies the optimal occupation and career path, and provides a specific career development plan. For example, the career support system allows a user to input their own skills and career goals. For example, they input programming skills, project management experience, and a career goal such as a desire to demonstrate leadership in the future. This information is input to the generation AI. The career support system then uses the generation AI to analyze the input information and identify the optimal occupation and career path for the user. The generation AI then suggests the occupation and career path that best suits the user's skills and career goals based on past data and trends. For example, a user with strong programming skills and a desire to demonstrate leadership may be suggested to take the role of software development team leader. Furthermore, the career support system uses the generation AI to provide the user with a specific career development plan. For example, the system suggests a training program to improve specific skills or a step-by-step plan to reach a career goal. This allows the user to concretely advance their own career development. For example, new graduates and job seekers can find the most suitable job based on their skills and career goals. It can also help people considering a career change find a new career path. This allows the career support system to provide personalized career planning support and help people with their career development. For example, new graduates and job seekers can find the most suitable job based on their skills and career goals. It can also help people considering a career change find a new career path.

[0065] A career support system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives input of a user's skills and career goals. The user's skills include, but are not limited to, technical skills and soft skills. The career goals include, but are not limited to, short-term goals and long-term goals. The reception unit, for example, stores the skills and career goals input by the user in a database. The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis unit, for example, analyzes the user's skills and career goals to identify the user's strengths and weaknesses. The analysis unit can also use the generation AI to analyze past data and trends based on the user's skills and career goals. For example, the analysis unit analyzes the user's skill set and identifies what type of job is suitable for the user. The identification unit, using the generation AI, identifies the optimal job type and career path based on the information analyzed by the analysis unit. The identification unit, for example, suggests the optimal job type and career path based on the user's skills and career goals. The identification unit can also use the generation AI to identify an occupation or career path that is most suitable for the user's skills and career goals. For example, the identification unit suggests an optimal occupation based on the user's skill set and career goals. The provision unit uses the generation AI to provide a specific career development plan based on the occupation or career path identified by the identification unit. The provision unit can, for example, propose a training program for improving specific skills or a step-by-step plan for reaching the career goal. The provision unit can also use the generation AI to provide a specific career development plan to the user. For example, the provision unit suggests what kind of training program is suitable based on the user's skill set. As a result, the career support system according to the embodiment can identify an optimal occupation or career path based on the user's skills and career goals and provide a specific career development plan.

[0066] The career support system includes a reception unit that estimates a user's emotions and adjusts the timing of inputting skills and career goals based on the estimated user emotions. The reception unit estimates the user's emotions and adjusts the timing of inputting skills and career goals based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the reception unit delays the input timing to provide a relaxing environment. If the user is relaxed, the reception unit accelerates the input timing to allow the input to proceed smoothly. If the user is in a hurry, the reception unit optimizes the input timing to allow the input to be completed quickly. This allows the input timing to be adjusted according to the user's emotions, allowing the user to input skills and career goals in a more appropriate environment.

[0067] The career support system includes a reception unit that analyzes a user's past input history and selects an appropriate input method. The reception unit analyzes the user's past input history and selects an appropriate input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception unit can automatically display skills and career goals that the user has previously entered as candidates. This improves the user's input efficiency by selecting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history data into a generation AI and have the generation AI select the optimal input method.

[0068] The career support system includes a reception unit that filters skills and career goals based on the user's current occupation and areas of interest when the skills and career goals are input. The reception unit filters the skills and career goals based on the user's current occupation and areas of interest when the skills and career goals are input. For example, skills and career goals related to the user's current occupation are preferentially displayed. The reception unit can also suggest related skills and career goals based on the user's areas of interest. For example, the user's occupational history can be used to filter appropriate skills and career goals. This allows the user to input more relevant skills and career goals by filtering based on the user's current occupation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's occupational history data to a generation AI and have the generation AI perform filtering.

[0069] The career support system includes a reception unit that selects an appropriate input means according to the user's input method when inputting skills and career goals. The reception unit selects an appropriate input means according to the user's input method when inputting skills and career goals. For example, if the user desires voice input, the reception unit provides a voice input interface. Furthermore, if the user desires text input, the reception unit can provide a text input interface. Furthermore, if the user desires image input, the reception unit can also support input using image recognition technology. For example, if the user desires voice input, the reception unit converts the voice into text using voice recognition technology. This improves input convenience by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input method data to a generation AI and cause the generation AI to select the optimal input means.

[0070] The career support system includes a reception unit that estimates a user's emotions and determines the priority of skills and career goals to be input based on the estimated user emotions. The reception unit estimates the user's emotions and determines the priority of skills and career goals to be input based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the reception unit may prioritize input of simple skills and career goals. On the other hand, if the user is relaxed, the reception unit may prioritize input of detailed skills and career goals. On the other hand, if the user is in a hurry, the reception unit may prioritize input of important skills and career goals. In this way, by determining the priority of skills and career goals to be input based on the user's emotions, input can be performed in a more appropriate order.

[0071] The career support system includes a reception unit that, when inputting skills and career goals, prioritizes inputting highly relevant information taking into account the user's geographical location information. When inputting skills and career goals, the reception unit prioritizes inputting highly relevant information taking into account the user's geographical location information. For example, the reception unit suggests skills and career goals specific to a region based on the user's current location. The reception unit can also prioritize displaying nearby job information based on the user's geographical location information. For example, the reception unit can suggest skills and career goals that match local trends based on the user's geographical location information. This allows the system to reflect region-specific skills and career goals by inputting highly relevant information taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant information.

[0072] The career support system includes a reception unit that analyzes a user's social media activity and inputs related information when skills and career goals are input. The reception unit analyzes the user's social media activity and inputs related information when skills and career goals are input. For example, the reception unit analyzes the user's social media posts and suggests related skills and career goals. The reception unit can also suggest related skills and career goals by referring to the activities of the user's friends on social media. For example, related skills and career goals can be suggested based on the user's social media check-in information. In this way, by analyzing the user's social media activity, more relevant skills and career goals can be suggested. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media data to a generation AI and have the generation AI select related information.

[0073] The career support system includes a reception unit that customizes an input method by reflecting a user's past feedback when inputting skills and career goals. The reception unit customizes the input method by reflecting a user's past feedback when inputting skills and career goals. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. For example, the input procedure can be optimized by referring to the user's past feedback. This optimizes the input method by reflecting the user's past feedback, improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the input method.

[0074] The career support system includes an analysis unit that estimates a user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions.

[0075] The career support system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of skills and career goals during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of skills and career goals during analysis. For example, the analysis unit performs a detailed analysis of important skills and career goals. The analysis unit can also perform a simplified analysis of general skills and career goals. For example, the analysis unit can perform a particularly detailed analysis of skills that are directly related to the user's career goals. By adjusting the level of detail of the analysis based on the importance of skills and career goals, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input importance data of skills and career goals into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] The career support system includes an analysis unit that applies different analysis algorithms depending on the category of skills and career goals during analysis. The analysis unit applies different analysis algorithms depending on the category of skills and career goals during analysis. For example, the analysis unit applies a technical analysis algorithm to technical skills. The analysis unit can apply an analysis algorithm related to leadership to management skills. The analysis unit can apply an analysis algorithm related to creativity to creative skills. In this way, by applying different analysis algorithms depending on the category of skills and career goals, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input category data of skills and career goals into the generation AI and cause the generation AI to apply the analysis algorithm.

[0077] The career support system includes an analysis unit that improves the accuracy of analysis by referring to the user's past analysis results during analysis. The analysis unit improves the accuracy of analysis by referring to the user's past analysis results during analysis. For example, the accuracy of the current analysis is improved based on the user's past analysis results. The analysis unit can also analyze trends from the user's past analysis results and improve accuracy. For example, the analysis algorithm can be optimized by referring to the user's past analysis results. This makes it possible to improve the accuracy of analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0078] The career support system includes an analysis unit that estimates a user's emotions and adjusts the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is in a hurry, the analysis unit performs a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually stimulating analysis. This allows the analysis length to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0079] The career support system includes an analysis unit that determines the analysis priority based on the submission time of skills and career goals during analysis. The analysis unit determines the analysis priority based on the submission time of skills and career goals during analysis. For example, the analysis unit prioritizes the most recently submitted skills and career goals. Furthermore, skills and career goals that were submitted earlier can be analyzed later. For example, the analysis schedule can be adjusted based on the submission time. This enables more efficient analysis by determining the analysis priority based on the submission time of skills and career goals. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the analysis priority.

[0080] The career support system includes an analysis unit that adjusts the order of analysis based on the relevance of skills and career goals during analysis. The analysis unit adjusts the order of analysis based on the relevance of skills and career goals during analysis. For example, highly relevant skills and career goals are analyzed preferentially. Less relevant skills and career goals can be analyzed later. For example, the order of analysis can be optimized based on the relevance. By adjusting the order of analysis based on the relevance of skills and career goals, more relevant analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs relevance data of skills and career goals into the generation AI and causes the generation AI to adjust the order of analysis.

[0081] The career support system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user's level of expertise is high, the analysis unit performs an analysis that makes heavy use of technical terms. On the other hand, if the user's level of expertise is low, the analysis unit can perform an analysis that avoids technical terms. For example, the method of expression of the analysis can be adjusted according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0082] The career support system includes an identification unit that estimates a user's emotions and adjusts identification criteria based on the estimated user emotions. The identification unit estimates the user's emotions and adjusts identification criteria based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the identification unit may perform identification using simple criteria. On the other hand, if the user is relaxed, the identification unit may perform identification using detailed criteria. On the other hand, if the user is in a hurry, the identification unit may set criteria for quick identification. As a result, by adjusting the identification criteria according to the user's emotions, a more appropriate job type or career path can be identified.

[0083] The career support system includes an identification unit that, during identification, improves the accuracy of identification by taking into account the interrelationships between skills and career goals. The identification unit improves the accuracy of identification by taking into account the interrelationships between skills and career goals. For example, the identification unit analyzes the interrelationships between skills and career goals to improve the accuracy of identification. The identification unit can also improve the accuracy of identification by taking into account the associations between skills and career goals. For example, the identification algorithm can be optimized based on the interrelationships between skills and career goals. This improves the accuracy of identification by taking into account the interrelationships between skills and career goals. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input interrelationship data between skills and career goals into the generation AI and cause the generation AI to improve the accuracy of identification.

[0084] The career support system includes an identification unit that, at the time of identification, takes into consideration attribute information of the submitter of skills and career goals. The identification unit, at the time of identification, takes into consideration attribute information of the submitter of skills and career goals. For example, the identification unit takes into consideration the submitter's age and gender. The identification unit can also take into consideration the submitter's occupation and experience. The identification unit can also take into consideration the submitter's region and cultural background. This enables more individualized identification by taking into consideration the submitter's attribute information. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the submitter's attribute information data into the generation AI and cause the generation AI to execute identification.

[0085] The career support system includes an identification unit that, at the time of identification, assigns specific weights based on the frequency of submission of skills and career goals. The identification unit, at the time of identification, assigns specific weights based on the frequency of submission of skills and career goals. For example, the identification unit assigns weights to skills and career goals that are submitted frequently. The identification unit can also assign weights to skills and career goals that are submitted infrequently. For example, the identification algorithm can be optimized based on the frequency of submission. As a result, weighting based on the frequency of submission improves the accuracy of identification. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input submission frequency data to the generation AI and cause the generation AI to perform the specific weighting.

[0086] The career support system includes an identification unit that estimates a user's emotions and adjusts the order in which specific results are displayed based on the estimated user emotions. The identification unit estimates the user's emotions and adjusts the order in which specific results are displayed based on the estimated user emotions. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the identification unit may prioritize displaying simple results. Also, if the user is relaxed, the identification unit may prioritize displaying detailed results. Also, if the user is in a hurry, the identification unit may prioritize displaying important results. This allows for more appropriate information to be provided by adjusting the display order of results according to the user's emotions.

[0087] The career support system includes an identification unit that performs identification taking into account the geographic distribution of skills and career goals during identification. The identification unit performs identification taking into account the geographic distribution of skills and career goals during identification. For example, the identification unit identifies relevant skills and career goals based on the geographic distribution of users. The identification unit can also identify region-specific skills and career goals by taking the geographic distribution into account. For example, the identification algorithm can be optimized based on the geographic distribution. This enables identification that reflects region-specific skills and career goals by taking the geographic distribution into account. Some or all of the above-described processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the identification unit can input geographic distribution data into the generation AI and cause the generation AI to execute identification.

[0088] The career support system includes an identification unit that, during identification, improves the accuracy of identification by referring to literature related to skills and career goals. The identification unit, during identification, improves the accuracy of identification by referring to literature related to skills and career goals. For example, the identification unit improves the accuracy of identification of skills and career goals by referring to related literature. The identification unit can also optimize the identification algorithm based on the related literature. The identification unit can also adjust the identification criteria by referring to the related literature. As a result, the accuracy of identification is improved by referring to the related literature. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of identification.

[0089] The career support system includes an identification unit that performs identification taking into account the market value of skills and career goals during identification. The identification unit performs identification taking into account the market value of skills and career goals during identification. For example, it prioritizes identifying skills and career goals with high market value. In addition, it can postpone identification of skills and career goals with low market value. For example, it is possible to optimize the identification algorithm based on market value. In this way, by taking market value into account, it is possible to identify more valuable skills and career goals. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit may input market value data into the generation AI and cause the generation AI to execute the identification.

[0090] The career support system includes a providing unit that estimates a user's emotions and prioritizes plans to provide based on the estimated user emotions. The providing unit estimates the user's emotions and prioritizes plans to provide based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the providing unit may prioritize providing simple plans. Also, if the user is relaxed, the providing unit may prioritize providing detailed plans. Also, if the user is in a hurry, the providing unit may prioritize providing important plans. This allows for the provision of more appropriate career development plans by prioritizing plans according to the user's emotions.

[0091] The career support system includes a providing unit that, when providing information, improves the accuracy of the information provided by taking into account the interrelationships between skills and career goals. The providing unit, when providing information, improves the accuracy of the information provided by taking into account the interrelationships between skills and career goals. For example, the providing unit analyzes the interrelationships between skills and career goals to improve the accuracy of the information provided. The providing unit can also improve the accuracy of the information provided by taking into account the associations between skills and career goals. For example, the providing algorithm can be optimized based on the interrelationships between skills and career goals. This improves the accuracy of the information provided by taking into account the interrelationships between skills and career goals. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input interrelationship data between skills and career goals into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0092] The career support system includes a providing unit that provides skills and career goals while taking into consideration attribute information of the submitter. The providing unit provides skills and career goals while taking into consideration attribute information of the submitter. For example, the providing unit provides the skills and career goals while taking into consideration the submitter's age and gender. The providing unit can also provide the skills and career goals while taking into consideration the submitter's occupation and experience. The providing unit can also provide the skills and career goals while taking into consideration the submitter's region and cultural background. This enables more personalized provision by taking into consideration the submitter's attribute information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the submitter's attribute information data into the generation AI and cause the generation AI to execute the provision.

[0093] The career support system includes a providing unit that weights the provision of skills and career goals based on the frequency of their submission at the time of provision. The providing unit weights the provision of skills and career goals based on the frequency of their submission at the time of provision. For example, the providing unit weights skills and career goals that are submitted frequently. The providing unit can also weight skills and career goals that are submitted infrequently. For example, the providing algorithm can be optimized based on the frequency of submission. As a result, weighting based on the frequency of submission improves the accuracy of the provision. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input submission frequency data to the generation AI and cause the generation AI to perform the weighting of the provision.

[0094] The career support system includes a providing unit that estimates a user's emotions and adjusts the display method of the plan to be provided based on the estimated user emotions. The providing unit estimates the user's emotions and adjusts the display method of the plan to be provided based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can provide a display method that includes detailed information. If the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows for more appropriate information to be provided by adjusting the display method of the plan according to the user's emotions.

[0095] The career support system includes a provision unit that provides skills and career goals while taking into account the geographic distribution of the skills and career goals. The provision unit provides the skills and career goals while taking into account the geographic distribution of the skills and career goals. For example, the provision unit provides related skills and career goals based on the geographic distribution of users. The provision unit can also provide region-specific skills and career goals while taking into account the geographic distribution. For example, the provision algorithm can be optimized based on the geographic distribution. This makes it possible to provide skills and career goals that reflect region-specific skills and career goals by taking the geographic distribution into account. Some or all of the above-described processing in the provision unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the provision unit can input geographic distribution data into the generation AI and cause the generation AI to execute the provision.

[0096] The career support system includes a providing unit that, at the time of providing, improves the accuracy of the provision by referring to literature related to skills and career goals. The providing unit, at the time of providing, improves the accuracy of the provision by referring to literature related to skills and career goals. For example, the accuracy of the provision of skills and career goals is improved by referring to related literature. The providing unit can also optimize the providing algorithm based on the related literature. The providing unit can also adjust the provision criteria by referring to related literature. As a result, the accuracy of the provision is improved by referring to related literature. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0097] The career support system includes a provision unit that provides skills and career goals while taking into consideration the market value of the skills and career goals. The provision unit provides skills and career goals while taking into consideration the market value of the skills and career goals. For example, skills and career goals with high market value are provided preferentially. Skills and career goals with low market value can be provided later. For example, the provision algorithm can be optimized based on market value. This makes it possible to provide more valuable skills and career goals by taking market value into consideration. Some or all of the above-described processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input market value data into the generation AI and have the generation AI execute the provision. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input of the user's skills and career goals using the reception device 38 of the smart device 14. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's skills and career goals using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the optimal job type and career path based on the analyzed information. The provision unit can provide the user with a specific career development plan using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input of the user's skills and career goals using the microphone 238 of the smart glasses 214. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's skills and career goals using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the optimal job type and career path based on the analyzed information. The provision unit can provide the user with a specific career development plan using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive input of the user's skills and career goals using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's skills and career goals using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the optimal job type and career path based on the analyzed information. The provision unit can provide the user with a specific career development plan using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of the user's skills and career goals using the microphone 238 of the robot 414. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the user's skills and career goals using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies the optimal job type and career path based on the analyzed information. The provision unit can provide the user with a specific career development plan using the speaker 240 of the robot 414.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The career support system can also have a function that utilizes the user's network based on the user's skills and career goals. For example, it can analyze the user's personal connections and past colleagues to identify connections that will be useful for the user's career goals. It can also broaden the user's career path options through recommendations and introductions within the user's network. It can also utilize the user's network to provide information on industry trends and the latest job openings. This allows users to make the most of their own networks and build their careers more effectively.

[0100] The career support system can also estimate the user's emotions and adjust the content and tone of the feedback it provides to the user based on the estimated emotions. For example, if the user is feeling stressed, the feedback can emphasize encouragement and support. If the user is relaxed, the feedback can provide specific advice and detailed explanations of next steps. If the user is in a hurry, the feedback can be concise and to the point. This allows the system to provide appropriate feedback based on the user's emotions and maintain the user's motivation.

[0101] The career support system can also analyze a user's past input history and provide a career development plan tailored to the user's learning style. For example, if the user has a visual learning style, a plan that makes extensive use of visual aids and infographics can be provided. If the user has an auditory learning style, a plan in audio guide or podcast format can be provided. Furthermore, if the user has a practical learning style, a plan that includes hands-on workshops and on-the-job training can be provided. This makes it possible to provide a career development plan that is optimal for the user's learning style and support effective skill acquisition.

[0102] When entering skills and career goals, the career support system can also provide advice from relevant industry experts based on the user's current occupation and areas of interest. For example, if a user is interested in the IT industry, advice from and interview articles about IT industry experts can be provided. If a user is interested in the medical industry, advice from medical industry experts and the latest research results can be provided. Furthermore, if a user is interested in the creative industry, advice and success stories from creative industry experts can be provided. This allows users to obtain specialized knowledge and advice related to their areas of interest and set more specific career goals.

[0103] The career support system can also provide interactive guidance depending on the user's input method when entering skills and career goals. For example, if the user prefers voice input, a voice guide can provide real-time feedback. If the user prefers text input, an interactive chatbot can support the input by answering questions. Furthermore, if the user prefers image input, image recognition technology can be used to automatically extract related information and assist with input. This makes it possible to provide interactive guidance depending on the user's input method, improving input convenience.

[0104] The career support system can also estimate the user's emotions and adjust the way it evaluates the user's progress toward achieving their career goals based on the estimated emotions. For example, if the user is feeling stressed, the evaluation can emphasize the positive aspects and include an encouraging message. If the user is relaxed, the evaluation can be detailed with specific improvements and next steps. If the user is in a hurry, the evaluation can be concise and to the point. This makes it possible to provide an appropriate evaluation based on the user's emotions and maintain the user's motivation.

[0105] When entering skills and career goals, the career support system can also suggest local career events and networking opportunities by taking the user's geographic location information into consideration. For example, if a user lives in a specific area, the system can provide information on career fairs and seminars held in that area. It can also suggest events held by nearby professional groups and industry associations based on the user's geographic location information. Furthermore, if the user is considering relocating, the system can provide information on career opportunities and lifestyles in the area to which the user is planning to relocate. This allows the user to take advantage of local career events and networking opportunities to advance their career development.

[0106] When a user enters their skills and career goals, the career support system can analyze the user's social media activity and suggest relevant online communities and forums based on the user's interests. For example, if a user is interested in a specific technology, the system can provide links to online communities and forums related to that technology. Also, if a user is interested in a specific industry, the system can suggest discussion groups and networking events related to that industry. Furthermore, if a user has a specific career goal, the system can provide success stories and inspiration related to that career goal. This allows users to take advantage of online communities and forums based on their interests and advance their career development.

[0107] The career support system can also customize the content and format of the career advice provided to the user by reflecting the user's past feedback when the user inputs skills and career goals. For example, the content of the advice can be adjusted based on the user's past feedback to provide more specific and practical advice. The system can also provide the advice in different formats, such as text, audio, or video, based on the user's feedback. Furthermore, the frequency and timing of the advice can be adjusted based on the user's feedback. This makes it possible to provide customized career advice that reflects the user's past feedback, improving user convenience.

[0108] The career support system can also estimate the user's emotions and adjust the way it reports the user on the progress of their career development plan based on the estimated emotions. For example, if the user is feeling stressed, the progress report can emphasize the positive aspects and include encouraging messages. If the user is relaxed, the progress report can be detailed with specific improvements and next steps. If the user is in a hurry, the progress report can be concise and to the point. This makes it possible to provide appropriate progress reports according to the user's emotions and maintain the user's motivation.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit accepts input of the user's skills and career goals. The user's skills include technical skills and soft skills, and the career goals include short-term and long-term goals. The reception unit saves the skills and career goals entered by the user in a database. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis unit analyzes the user's skills and career goals to identify their strengths and weaknesses. It also analyzes past data and trends to identify suitable occupations based on the user's skill set. Step 3: The identification unit uses the generation AI to identify the optimal occupation and career path based on the information analyzed by the analysis unit. The identification unit proposes the optimal occupation and career path based on the user's skills and career goals. Step 4: The delivery department uses generative AI to provide a specific career development plan based on the job types and career paths identified by the identification department. The delivery department suggests training programs to improve specific skills and a step-by-step plan to reach career goals.

[0111] 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.

[0112] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a user's skills and career goals; an analysis unit that analyzes the information received by the reception unit; an identification unit that identifies an appropriate occupation or career path based on the information analyzed by the analysis unit; a provision unit that provides a specific career development plan based on the job type and career path identified by the identification unit. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of inputting skills and career goals based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past input history and select the appropriate input method 2. The system of claim 1.

4. The reception unit Filter skills and career goals based on the user's current occupation and interests 2. The system of claim 1.

5. The reception unit When entering skills and career goals, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the skills and career goals to be entered based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering skills and career goals, the app takes into account the user's geographic location to prioritize the most relevant information.

2. The system of claim 1.

8. The reception unit Analyzes users' social media activity and populates relevant information when entering skills and career goals 2. The system of claim 1.

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