System

Abstract

An object of a system according to an embodiment is to specify and provide a user's specialty field and future potential.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The specifying unit specifies a specialty field or a future potential based on the information analyzed by the analyzing unit. The providing unit provides the information specified by the specifying unit to the user.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 not been able to efficiently identify and provide users' areas of expertise and future potential, and there is room for improvement.

[0005] The system according to the embodiment aims to identify and provide users' areas of expertise and future potential. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, an identifying unit, and a providing unit. The receiving unit receives information from a user. The analyzing unit analyzes the information received by the receiving unit. The identifying unit identifies a specialty or future potential based on the information analyzed by the analyzing unit. The providing unit provides the information identified by the identifying unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can identify and provide information about a user's areas of expertise and future potential. [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 system according to an embodiment of the present invention uses a generative AI to identify an individual's areas of expertise and future potential. In this system, a user inputs information such as their interests, experience, skills, past achievements, and educational background. The generative AI analyzes this information and identifies the user's areas of expertise and future potential. For example, if a user is interested in programming and has previously worked on several projects, the generative AI uses this information to suggest that the user has high potential in programming. The generative AI also predicts the fields in which the user is likely to succeed in the future and provides specific advice. For example, if a user is interested in data science, the generative AI suggests a career path in the field of data science and the necessary skill sets. This system allows users to understand their strengths and use this information to help them make future career choices. This allows the system to efficiently analyze user information, identify and provide information about their areas of expertise and future potential. For example, users can understand their strengths and use this information to help them make future career choices. Furthermore, users can navigate at their own pace and intuitively without complex operations. This simple structure makes the system easy to use for everyone, from children to the elderly, and is popular with everyone.

[0029] The system according to the embodiment includes a receiving unit, an analyzing unit, an identifying unit, and a providing unit. The receiving unit receives information from a user. The information from the user includes, but is not limited to, interests, experience, skills, past achievements, and educational background. The receiving unit receives, for example, text information entered by the user. The receiving unit can also receive image data uploaded by the user. The receiving unit can also receive information entered by voice from the user. For example, the receiving unit converts the text information entered by the user into a format that is easy to analyze. The analyzing unit uses a generating AI to analyze the information received by the receiving unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, these examples. For example, the analyzing unit uses the generating AI to analyze information such as the user's interests, experience, and skills. The analyzing unit can also use the generating AI to analyze information such as the user's past achievements and educational background. The analyzing unit can also use the generating AI to predict future potential based on the user's information. For example, the generating AI receives user information and outputs areas of expertise and future potential. The identification unit uses the generation AI to identify the user's areas of expertise and future potential based on the information analyzed by the analysis unit. The identification may, for example, identify an area of ​​expertise such as a technical field or a business field, but is not limited to such examples. For example, the identification unit may use the generation AI to identify the user's areas of expertise. The identification unit may also use the generation AI to identify the user's future potential. The identification unit may also use the generation AI to provide specific advice based on the user's information. For example, the generation AI inputs the user's information and outputs a career path and a required skill set. The provision unit provides the user with the information identified by the identification unit. The provision may, for example, be in text format or image format, but is not limited to such examples. For example, the provision unit provides the user with specific advice. The provision unit may also suggest a career path and a required skill set to the user. The provision unit may also suggest future potential to the user. For example, the provision unit may suggest a career path and a required skill set in the field of data science to the user.As a result, the system according to the embodiment can efficiently analyze user information, identify areas of expertise and future potential, and provide them to users. For example, users can understand their own strengths and use them to help them choose their future careers.

[0030] The reception unit can accept information including the user's interests, experience, skills, past achievements, and educational background. The reception unit, for example, accepts text information entered by the user. For example, information about the user's fields of interest and hobbies is entered. The reception unit can also accept information about projects and job roles the user has previously experienced. For example, details and results of projects the user has previously participated in are entered. The reception unit can also accept information about the user's skills. For example, information about programming languages ​​and technologies the user has mastered is entered. The reception unit can also accept information about the user's past achievements. For example, information about awards the user has received and goals the user has achieved is entered. The reception unit can also accept information about the user's educational background. For example, information about the degree the user has obtained and field of major is entered. This allows for more accurate analysis by accepting a variety of user 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 can input the text information entered by the user to a generation AI and convert it into a format that is easy for the generation AI to analyze.

[0031] The analysis unit can analyze the information received by the reception unit using the generation AI. The analysis unit, for example, uses the generation AI to analyze information such as the user's interests, experience, and skills. For example, the generation AI analyzes information about the user's interests and hobbies entered by the user to identify the user's areas of expertise. The analysis unit can also use the generation AI to analyze information such as the user's past achievements and educational background. For example, the generation AI analyzes details and results of projects the user has participated in in the past to predict the user's future potential. The analysis unit can also use the generation AI to predict future potential based on the user's information. For example, the generation AI takes the user's information as input and outputs the user's areas of expertise and future potential. This improves the accuracy of information analysis by using the generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information entered by the user to the generation AI, which then performs analysis.

[0032] The identification unit can use the generation AI to identify the user's areas of expertise and future potential based on the information analyzed by the analysis unit. The identification unit, for example, uses the generation AI to identify the user's areas of expertise. For example, the generation AI analyzes information about interests and hobbies entered by the user to identify the user's areas of expertise. The identification unit can also use the generation AI to identify the user's future potential. For example, the generation AI analyzes details and results of projects the user has previously participated in to predict the user's future potential. The identification unit can also use the generation AI to provide specific advice based on the user's information. For example, the generation AI inputs the user's information and outputs a career path and required skill set. This improves the accuracy of identifying the user's areas of expertise and future potential by using the generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input information entered by the user to the generation AI, and the generation AI can identify the user's areas of expertise and future potential.

[0033] The providing unit can provide specific advice to the user based on the information identified by the identifying unit. The providing unit, for example, provides specific advice to the user. For example, the providing unit suggests a career path and a required skill set to the user. The providing unit can also suggest future potential to the user. For example, the providing unit suggests a career path and a required skill set in the field of data science to the user. This makes it possible to provide specific advice to the user and support career selection. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the information identified by the identifying unit to a generating AI, which can generate specific advice.

[0034] The providing unit can propose a career path and a required skill set. The providing unit, for example, proposes a career path and a required skill set to the user. For example, the providing unit proposes a career path and a required skill set in the field of data science to the user. The providing unit can also propose a career path in a technical field, a business field, or the like to the user. For example, the providing unit proposes a required skill set, such as programming skills or project management skills, to the user. This allows the user to be supported in making future career choices by proposing specific career paths and skill sets to the user. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the information identified by the identifying unit into a generating AI, which can then propose a career path and a required skill set.

[0035] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that the user will use in a specific time period based on the user's past input history. For example, the reception unit can analyze information that the user has input in the past and provide an optimal input interface. In this way, by analyzing the past input history, an optimal input interface can be provided to the user. 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 input history data to a generation AI, which can then suggest an optimal input interface.

[0036] When receiving information, the reception unit can filter the input content based on the user's current situation and areas of interest. For example, the reception unit preferentially receives information related to areas in which the user is currently interested. The reception unit can also filter related information based on the user's current situation (e.g., occupational or academic status). The reception unit can also filter information related to areas of interest by referring to the user's past search history. For example, the reception unit preferentially receives information related to areas in which the user is currently interested. In this way, by filtering the input content based on the user's current situation and areas of interest, more relevant information can be received. 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 data on the user's current situation and areas of interest to a generation AI, and the generation AI can filter the input content.

[0037] When receiving information, the reception unit can select an appropriate input means depending on the user's input method. 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 also provide a text input interface. Furthermore, if the user desires image input, the reception unit can also provide an input interface using image recognition technology. For example, if the user desires voice input, the reception unit provides a voice input interface. This facilitates information input by selecting the optimal input means depending on 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 using AI. For example, the reception unit can input data on the user's input method into a generation AI, which can then select the optimal input means.

[0038] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, the reception unit prioritizes receiving information related to the user's current location. The reception unit can also suggest related events and activities based on the user's geographical location information. The reception unit can also prioritize receiving region-specific information based on the user's geographical location information. For example, the reception unit prioritizes receiving information related to the user's current location. This makes it possible to prioritize receiving highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information into a generation AI and prioritize receiving information that is highly relevant according to the generation AI.

[0039] The reception unit may analyze the user's social media activity and receive related information when receiving information. For example, the reception unit may receive related information based on information shared by the user on social media. The reception unit may also analyze the user's social media activity and suggest related information. The reception unit may also receive related information by referring to the activity of the user's friends on social media. For example, the reception unit may receive related information based on information shared by the user on social media. This allows the user's social media activity to be analyzed and related information to be received efficiently. 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 data on the user's social media activity into a generation AI and receive related information from the generation AI.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when receiving information. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input interface by reflecting the user's past feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. In this way, the input method can be optimized by reflecting the user's past feedback. 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, which can customize the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of the information and performs a detailed analysis on important information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specialized analysis algorithm to technical information. The analysis unit can also apply an experience-based analysis algorithm to information based on experience. The analysis unit can also apply an education-based analysis algorithm to information based on educational background. For example, the analysis unit applies different analysis algorithms depending on the category of information. This improves the accuracy of analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to a generation AI, and have the generation AI apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit determines the priority of analysis based on the time of submission of information. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to a generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of highly relevant information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to a generation AI, and the generation AI can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simpler terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and, if the user has technical expertise, provide analysis results that use a lot of technical terms. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI, which can then adjust the use of technical terms in the analysis.

[0047] The identification unit can improve the accuracy of identification by taking into account the interrelationships of information during identification. For example, the identification unit analyzes the interrelationships of information and improves the accuracy of identification. The identification unit can also improve the accuracy of identification by taking into account the relevance of information. The identification unit can also improve the accuracy of identification based on the interrelationships of information. For example, the identification unit analyzes the interrelationships of information and improves the accuracy of identification. In this way, the accuracy of identification is improved by taking the interrelationships of information into account. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input interrelationship data of information to a generation AI, and the generation AI can improve the accuracy of identification.

[0048] The identification unit can perform identification by taking into consideration the attribute information of the information submitter. The identification unit can perform identification by taking into consideration, for example, the occupation and educational background of the information submitter. The identification unit can also perform identification by taking into consideration the experience and skills of the information submitter. The identification unit can also perform identification based on the attribute information of the information submitter. For example, the identification unit can perform identification by taking into consideration the occupation and educational background of the information submitter. By taking into consideration the attribute information of the information submitter, the accuracy of identification is improved. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input attribute information data of the information submitter into a generation AI, and the generation AI can perform identification.

[0049] During identification, the identification unit can assign a specific weight based on the frequency of submission of information. For example, the identification unit can assign a higher weight to information that is submitted frequently. The identification unit can also assign a lower weight to information that is submitted infrequently. The identification unit can also adjust the specific weight based on the submission frequency. For example, the identification unit can assign a higher weight to information that is submitted frequently. By assigning a specific weight based on the frequency of submission of information, the accuracy of identification is improved. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input information submission frequency data to a generation AI, and the generation AI can assign a specific weight.

[0050] The identification unit can perform identification by taking into account the geographical distribution of the information. For example, the identification unit analyzes the geographical distribution of the information to improve the accuracy of the identification. The identification unit can also prioritize the identification of information with high geographic relevance. The identification unit can also adjust the identification results based on the geographical distribution. For example, the identification unit analyzes the geographical distribution of the information to improve the accuracy of the identification. As a result, the accuracy of the identification is improved by taking the geographical distribution of the information into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical distribution data of the information into a generation AI, and the generation AI can perform identification.

[0051] During identification, the identification unit can improve the accuracy of identification by referring to literature related to the information. The identification unit, for example, refers to related literature to improve the accuracy of identification. The identification unit can also complement the identification results based on information in the related literature. The identification unit can also analyze related literature to improve the accuracy of identification. For example, the identification unit refers to related literature to improve the accuracy of identification. As a result, the accuracy of identification is improved by referring to literature related to the information. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input literature data related to the information into a generation AI, and the generation AI can improve the accuracy of identification.

[0052] The identification unit can perform identification taking into consideration the market value of the information. For example, the identification unit prioritizes identification of information with high market value. The identification unit can also adjust the weighting of identification based on the market value. The identification unit can also analyze market value information to improve the accuracy of identification. For example, the identification unit prioritizes identification of information with high market value. In this way, the accuracy of identification is improved by taking the market value of the information into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input market value data of the information into a generation AI, and the generation AI can perform identification.

[0053] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. The providing unit can also provide a simplified explanation for information with low importance. The providing unit can also determine the priority of the information provided based on the importance of the information. For example, the providing unit evaluates the importance of the information and provides a detailed explanation for important information. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information importance data to a generating AI, and the generating AI can adjust the level of detail of the information provided.

[0054] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit applies a specialized provision algorithm to technical information. The providing unit can also apply an experience-based provision algorithm to information based on experience. The providing unit can also apply an education-based provision algorithm to information based on educational background. For example, the providing unit applies different provision algorithms depending on the category of information. This improves the accuracy of information provision by applying different provision algorithms depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information category data to a generation AI, and have the generation AI apply different provision algorithms.

[0055] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of provision by referring to the user's past provision results. The providing unit can also analyze the user's past provision results and propose an optimal provision method. For example, the providing unit adjusts the provision algorithm based on the user's past provision results. As a result, the accuracy of provision is improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into a generation AI, and the generation AI can improve the accuracy of provision.

[0056] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit prioritizes the provision of the latest information. The providing unit can also postpone the provision of information that was submitted earlier. The providing unit can also adjust the provision schedule based on the time of submission. For example, the providing unit determines the priority of provision based on the time of submission of information. This enables efficient information provision by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information submission time data into a generation AI, and the generation AI can determine the priority of provision.

[0057] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit provides highly relevant information with priority. The providing unit can also postpone the provision of less relevant information. The providing unit can also adjust the order of provision based on the relevance of the information. For example, the providing unit evaluates the relevance of the information and provides highly relevant information with priority. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the information to a generating AI, and the generating AI can adjust the order of provision.

[0058] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information in simple language. Furthermore, the providing unit can adjust the way information is presented according to the user's level of expertise. For example, the providing unit can evaluate the user's level of expertise and, if the user has technical expertise, provide information that uses a lot of technical terminology. This allows for the provision of information that is easier to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without AI. For example, the providing unit can input the user's level of expertise data into a generating AI, which can adjust the use of technical terminology in the provided information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also provide the latest trend information related to the user's interests and skills based on the user's input information. For example, if the user is interested in programming, the reception unit can provide information on the latest programming languages ​​and technologies. If the user is interested in data science, the reception unit can provide information on the latest data science research and tools. Furthermore, if the user is interested in a particular industry, the reception unit can provide the latest news and trends in that industry. This allows the user to obtain the latest information related to their interests and skills, enabling more effective career choices.

[0061] The analysis unit can compare the user's skills and experience with those of other users based on the user's input information. For example, if the user has programming skills, the analysis unit can compare them with the skill levels of other programmers to evaluate the user's skill level. Also, if the user has experience participating in a specific project, the analysis unit can compare them with the experience of other project participants to evaluate the value of the user's experience. Furthermore, the analysis unit can compare the user's educational background and work history with those of other users to evaluate the user's potential. This allows the user to understand the level of their skills and experience compared to those of other users and identify directions for self-improvement.

[0062] The identification unit can suggest learning resources and training programs suitable for the user based on the user's input information. For example, if the user wants to improve their programming skills, the identification unit can suggest online courses and learning materials. If the user wants to acquire data science skills, the identification unit can suggest data science training programs and workshops. Furthermore, if the user wants to obtain a specific qualification, the identification unit can suggest learning resources and exam preparation related to that qualification. This allows the user to obtain resources to effectively improve their skills and knowledge.

[0063] The provider can suggest networking events and communities suitable for the user based on the user's input information. For example, if the user is interested in programming, the provider can suggest programming-related meetups and conferences. If the user is interested in data science, the provider can suggest data science communities and forums. Furthermore, if the user is interested in a specific industry, the provider can suggest professional networking events in that industry. This allows the user to access networking opportunities related to their interests and skills, which can help with career development.

[0064] The offering unit can suggest internship and volunteer opportunities suitable for the user based on the user's input information. For example, if the user wants to improve their programming skills in a practical way, the offering unit can suggest programming-related internships. Alternatively, if the user wants to gain experience in data science, the offering unit can suggest volunteer projects in data science. Furthermore, if the user wants to gain experience in a specific industry, the offering unit can suggest internship and volunteer opportunities in that industry. This allows the user to have opportunities to gain practical experience and help advance their career.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives information from the user. Information from the user includes interests, experience, skills, past achievements, and educational background. The reception unit can receive text information entered by the user, uploaded image data, and information entered by voice. The reception unit also converts the text information entered by the user into a format that is easy to analyze. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit analyzes information such as the user's interests, experience, skills, past achievements, and educational background to predict future potential. Step 3: The identification unit uses the generation AI to identify areas of expertise and future potential based on the information analyzed by the analysis unit. The identification unit identifies areas of expertise, such as technical fields or business fields, and identifies the user's future potential. The identification unit also provides specific advice based on the user's information. Step 4: The provision unit provides the user with the information identified by the identification unit. The information is provided in text or image format. The provision unit suggests specific advice, career paths, and necessary skill sets to the user, and suggests future potential.

[0067] (Example 2) A system according to an embodiment of the present invention uses a generative AI to identify an individual's areas of expertise and future potential. In this system, a user inputs information such as their interests, experience, skills, past achievements, and educational background. The generative AI analyzes this information and identifies the user's areas of expertise and future potential. For example, if a user is interested in programming and has previously worked on several projects, the generative AI uses this information to suggest that the user has high potential in programming. The generative AI also predicts the fields in which the user is likely to succeed in the future and provides specific advice. For example, if a user is interested in data science, the generative AI suggests a career path in the field of data science and the necessary skill sets. This system allows users to understand their strengths and use this information to help them make future career choices. This allows the system to efficiently analyze user information, identify and provide information about their areas of expertise and future potential. For example, users can understand their strengths and use this information to help them make future career choices. Furthermore, users can navigate at their own pace and intuitively without complex operations. This simple structure makes the system easy to use for everyone, from children to the elderly, and is popular with everyone.

[0068] The system according to the embodiment includes a receiving unit, an analyzing unit, an identifying unit, and a providing unit. The receiving unit receives information from a user. The information from the user includes, but is not limited to, interests, experience, skills, past achievements, and educational background. The receiving unit receives, for example, text information entered by the user. The receiving unit can also receive image data uploaded by the user. The receiving unit can also receive information entered by voice from the user. For example, the receiving unit converts the text information entered by the user into a format that is easy to analyze. The analyzing unit uses a generating AI to analyze the information received by the receiving unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, these examples. For example, the analyzing unit uses the generating AI to analyze information such as the user's interests, experience, and skills. The analyzing unit can also use the generating AI to analyze information such as the user's past achievements and educational background. The analyzing unit can also use the generating AI to predict future potential based on the user's information. For example, the generating AI receives user information and outputs areas of expertise and future potential. The identification unit uses the generation AI to identify the user's areas of expertise and future potential based on the information analyzed by the analysis unit. The identification may, for example, identify an area of ​​expertise such as a technical field or a business field, but is not limited to such examples. For example, the identification unit may use the generation AI to identify the user's areas of expertise. The identification unit may also use the generation AI to identify the user's future potential. The identification unit may also use the generation AI to provide specific advice based on the user's information. For example, the generation AI inputs the user's information and outputs a career path and a required skill set. The provision unit provides the user with the information identified by the identification unit. The provision may, for example, be in text format or image format, but is not limited to such examples. For example, the provision unit provides the user with specific advice. The provision unit may also suggest a career path and a required skill set to the user. The provision unit may also suggest future potential to the user. For example, the provision unit may suggest a career path and a required skill set in the field of data science to the user.As a result, the system according to the embodiment can efficiently analyze user information, identify areas of expertise and future potential, and provide them to users. For example, users can understand their own strengths and use them to help them choose their future careers.

[0069] The reception unit can accept information including the user's interests, experience, skills, past achievements, and educational background. The reception unit, for example, accepts text information entered by the user. For example, information about the user's fields of interest and hobbies is entered. The reception unit can also accept information about projects and job roles the user has previously experienced. For example, details and results of projects the user has previously participated in are entered. The reception unit can also accept information about the user's skills. For example, information about programming languages ​​and technologies the user has mastered is entered. The reception unit can also accept information about the user's past achievements. For example, information about awards the user has received and goals the user has achieved is entered. The reception unit can also accept information about the user's educational background. For example, information about the degree the user has obtained and field of major is entered. This allows for more accurate analysis by accepting a variety of user 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 can input the text information entered by the user to a generation AI and convert it into a format that is easy for the generation AI to analyze.

[0070] The analysis unit can analyze the information received by the reception unit using the generation AI. The analysis unit, for example, uses the generation AI to analyze information such as the user's interests, experience, and skills. For example, the generation AI analyzes information about the user's interests and hobbies entered by the user to identify the user's areas of expertise. The analysis unit can also use the generation AI to analyze information such as the user's past achievements and educational background. For example, the generation AI analyzes details and results of projects the user has participated in in the past to predict the user's future potential. The analysis unit can also use the generation AI to predict future potential based on the user's information. For example, the generation AI takes the user's information as input and outputs the user's areas of expertise and future potential. This improves the accuracy of information analysis by using the generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information entered by the user to the generation AI, which then performs analysis.

[0071] The identification unit can use the generation AI to identify the user's areas of expertise and future potential based on the information analyzed by the analysis unit. The identification unit, for example, uses the generation AI to identify the user's areas of expertise. For example, the generation AI analyzes information about interests and hobbies entered by the user to identify the user's areas of expertise. The identification unit can also use the generation AI to identify the user's future potential. For example, the generation AI analyzes details and results of projects the user has previously participated in to predict the user's future potential. The identification unit can also use the generation AI to provide specific advice based on the user's information. For example, the generation AI inputs the user's information and outputs a career path and required skill set. This improves the accuracy of identifying the user's areas of expertise and future potential by using the generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input information entered by the user to the generation AI, and the generation AI can identify the user's areas of expertise and future potential.

[0072] The providing unit can provide specific advice to the user based on the information identified by the identifying unit. The providing unit, for example, provides specific advice to the user. For example, the providing unit suggests a career path and a required skill set to the user. The providing unit can also suggest future potential to the user. For example, the providing unit suggests a career path and a required skill set in the field of data science to the user. This makes it possible to provide specific advice to the user and support career selection. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the information identified by the identifying unit to a generating AI, which can generate specific advice.

[0073] The providing unit can propose a career path and a required skill set. The providing unit, for example, proposes a career path and a required skill set to the user. For example, the providing unit proposes a career path and a required skill set in the field of data science to the user. The providing unit can also propose a career path in a technical field, a business field, or the like to the user. For example, the providing unit proposes a required skill set, such as programming skills or project management skills, to the user. This allows the user to be supported in making future career choices by proposing specific career paths and skill sets to the user. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the information identified by the identifying unit into a generating AI, which can then propose a career path and a required skill set.

[0074] The reception unit can estimate the user's emotions and adjust the information input method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. For example, the reception unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, the reception unit can record the user's voice and estimate the user's emotions using voice analysis technology. This allows for more appropriate information input by adjusting the information input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion and adjust the input method.

[0075] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that the user will use in a specific time period based on the user's past input history. For example, the reception unit can analyze information that the user has input in the past and provide an optimal input interface. In this way, by analyzing the past input history, an optimal input interface can be provided to the user. 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 input history data to a generation AI, which can then suggest an optimal input interface.

[0076] When receiving information, the reception unit can filter the input content based on the user's current situation and areas of interest. For example, the reception unit preferentially receives information related to areas in which the user is currently interested. The reception unit can also filter related information based on the user's current situation (e.g., occupational or academic status). The reception unit can also filter information related to areas of interest by referring to the user's past search history. For example, the reception unit preferentially receives information related to areas in which the user is currently interested. In this way, by filtering the input content based on the user's current situation and areas of interest, more relevant information can be received. 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 data on the user's current situation and areas of interest to a generation AI, and the generation AI can filter the input content.

[0077] When receiving information, the reception unit can select an appropriate input means depending on the user's input method. 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 also provide a text input interface. Furthermore, if the user desires image input, the reception unit can also provide an input interface using image recognition technology. For example, if the user desires voice input, the reception unit provides a voice input interface. This facilitates information input by selecting the optimal input means depending on 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 using AI. For example, the reception unit can input data on the user's input method into a generation AI, which can then select the optimal input means.

[0078] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize input of important information. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the most important information. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Thus, by prioritizing input content based on the user's emotions, important information can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into a generation AI, which can then estimate the emotion and determine the priority of the input content.

[0079] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, the reception unit prioritizes receiving information related to the user's current location. The reception unit can also suggest related events and activities based on the user's geographical location information. The reception unit can also prioritize receiving region-specific information based on the user's geographical location information. For example, the reception unit prioritizes receiving information related to the user's current location. This makes it possible to prioritize receiving highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information into a generation AI and prioritize receiving information that is highly relevant according to the generation AI.

[0080] The reception unit may analyze the user's social media activity and receive related information when receiving information. For example, the reception unit may receive related information based on information shared by the user on social media. The reception unit may also analyze the user's social media activity and suggest related information. The reception unit may also receive related information by referring to the activity of the user's friends on social media. For example, the reception unit may receive related information based on information shared by the user on social media. This allows the user's social media activity to be analyzed and related information to be received efficiently. 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 data on the user's social media activity into a generation AI and receive related information from the generation AI.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when receiving information. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input interface by reflecting the user's past feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. In this way, the input method can be optimized by reflecting the user's past feedback. 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, which can customize the input method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for adjusting the presentation method of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then infer the emotion and adjust the way the analysis is expressed.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of the information and performs a detailed analysis on important information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a specialized analysis algorithm to technical information. The analysis unit can also apply an experience-based analysis algorithm to information based on experience. The analysis unit can also apply an education-based analysis algorithm to information based on educational background. For example, the analysis unit applies different analysis algorithms depending on the category of information. This improves the accuracy of analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input information category data to a generation AI, and have the generation AI apply different analysis algorithms.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then infer the emotion and adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit determines the priority of analysis based on the time of submission of information. This enables efficient analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of information to a generation AI, and the generation AI can determine the priority of analysis.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit evaluates the relevance of the information and prioritizes analysis of highly relevant information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to a generation AI, and the generation AI can adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simpler terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and, if the user has technical expertise, provide analysis results that use a lot of technical terms. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI, which can then adjust the use of technical terms in the analysis.

[0090] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user emotions. For example, if the user is nervous, the identification unit can perform identification using simple criteria. If the user is relaxed, the identification unit can perform identification using detailed criteria. If the user is in a hurry, the identification unit can use criteria for quick identification. For example, the identification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The identification unit can also record the user's voice and estimate the emotion using voice analysis technology. This enables more appropriate identification by adjusting the identification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using AI, for example, or without AI. For example, the identification unit can input the user's facial expression data into a generation AI, have the generation AI estimate the emotion, and adjust the identification criteria.

[0091] The identification unit can improve the accuracy of identification by taking into account the interrelationships of information during identification. For example, the identification unit analyzes the interrelationships of information and improves the accuracy of identification. The identification unit can also improve the accuracy of identification by taking into account the relevance of information. The identification unit can also improve the accuracy of identification based on the interrelationships of information. For example, the identification unit analyzes the interrelationships of information and improves the accuracy of identification. In this way, the accuracy of identification is improved by taking the interrelationships of information into account. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input interrelationship data of information to a generation AI, and the generation AI can improve the accuracy of identification.

[0092] The identification unit can perform identification by taking into consideration the attribute information of the information submitter. The identification unit can perform identification by taking into consideration, for example, the occupation and educational background of the information submitter. The identification unit can also perform identification by taking into consideration the experience and skills of the information submitter. The identification unit can also perform identification based on the attribute information of the information submitter. For example, the identification unit can perform identification by taking into consideration the occupation and educational background of the information submitter. By taking into consideration the attribute information of the information submitter, the accuracy of identification is improved. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input attribute information data of the information submitter into a generation AI, and the generation AI can perform identification.

[0093] During identification, the identification unit can assign a specific weight based on the frequency of submission of information. For example, the identification unit can assign a higher weight to information that is submitted frequently. The identification unit can also assign a lower weight to information that is submitted infrequently. The identification unit can also adjust the specific weight based on the submission frequency. For example, the identification unit can assign a higher weight to information that is submitted frequently. By assigning a specific weight based on the frequency of submission of information, the accuracy of identification is improved. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input information submission frequency data to a generation AI, and the generation AI can assign a specific weight.

[0094] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user emotions. For example, if the user is nervous, the identification unit can prioritize displaying important results. Furthermore, if the user is relaxed, the identification unit can also display detailed results. Furthermore, if the user is in a hurry, the identification unit can prioritize displaying results that highlight the key points. For example, the identification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the identification unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more appropriate information provision by adjusting the order in which specific results are displayed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or without AI. For example, the determination unit can input the user's facial expression data into the generation AI, have the generation AI estimate the emotion, and adjust the order in which the specific results are displayed.

[0095] The identification unit can perform identification by taking into account the geographical distribution of the information. For example, the identification unit analyzes the geographical distribution of the information to improve the accuracy of the identification. The identification unit can also prioritize the identification of information with high geographic relevance. The identification unit can also adjust the identification results based on the geographical distribution. For example, the identification unit analyzes the geographical distribution of the information to improve the accuracy of the identification. As a result, the accuracy of the identification is improved by taking the geographical distribution of the information into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical distribution data of the information into a generation AI, and the generation AI can perform identification.

[0096] During identification, the identification unit can improve the accuracy of identification by referring to literature related to the information. The identification unit, for example, refers to related literature to improve the accuracy of identification. The identification unit can also complement the identification results based on information in the related literature. The identification unit can also analyze related literature to improve the accuracy of identification. For example, the identification unit refers to related literature to improve the accuracy of identification. As a result, the accuracy of identification is improved by referring to literature related to the information. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input literature data related to the information into a generation AI, and the generation AI can improve the accuracy of identification.

[0097] The identification unit can perform identification taking into consideration the market value of the information. For example, the identification unit prioritizes identification of information with high market value. The identification unit can also adjust the weighting of identification based on the market value. The identification unit can also analyze market value information to improve the accuracy of identification. For example, the identification unit prioritizes identification of information with high market value. In this way, the accuracy of identification is improved by taking the market value of the information into consideration. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input market value data of the information into a generation AI, and the generation AI can perform identification.

[0098] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. This enables more appropriate information to be provided by adjusting the presentation method of the information based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generating AI, which can then estimate the emotion and adjust the way the information is expressed.

[0099] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for important information. The providing unit can also provide a simplified explanation for information with low importance. The providing unit can also determine the priority of the information provided based on the importance of the information. For example, the providing unit evaluates the importance of the information and provides a detailed explanation for important information. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information importance data to a generating AI, and the generating AI can adjust the level of detail of the information provided.

[0100] The providing unit can apply different provision algorithms depending on the category of information when providing the information. For example, the providing unit applies a specialized provision algorithm to technical information. The providing unit can also apply an experience-based provision algorithm to information based on experience. The providing unit can also apply an education-based provision algorithm to information based on educational background. For example, the providing unit applies different provision algorithms depending on the category of information. This improves the accuracy of information provision by applying different provision algorithms depending on the category of information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information category data to a generation AI, and have the generation AI apply different provision algorithms.

[0101] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of provision by referring to the user's past provision results. The providing unit can also analyze the user's past provision results and propose an optimal provision method. For example, the providing unit adjusts the provision algorithm based on the user's past provision results. As a result, the accuracy of provision is improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into a generation AI, and the generation AI can improve the accuracy of provision.

[0102] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the user is excited, the providing unit can provide visually stimulating information. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. This allows for more appropriate information to be provided by adjusting the length of the information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's facial expression data into the generating AI, which can then estimate the emotion and adjust the length of the information.

[0103] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit prioritizes the provision of the latest information. The providing unit can also postpone the provision of information that was submitted earlier. The providing unit can also adjust the provision schedule based on the time of submission. For example, the providing unit determines the priority of provision based on the time of submission of information. This enables efficient information provision by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information submission time data into a generation AI, and the generation AI can determine the priority of provision.

[0104] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit provides highly relevant information with priority. The providing unit can also postpone the provision of less relevant information. The providing unit can also adjust the order of provision based on the relevance of the information. For example, the providing unit evaluates the relevance of the information and provides highly relevant information with priority. This enables efficient information provision by adjusting the order of provision based on the relevance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the information to a generating AI, and the generating AI can adjust the order of provision.

[0105] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information in simple language. Furthermore, the providing unit can adjust the way information is presented according to the user's level of expertise. For example, the providing unit can evaluate the user's level of expertise and, if the user has technical expertise, provide information that uses a lot of technical terminology. This allows for the provision of information that is easier to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without AI. For example, the providing unit can input the user's level of expertise data into a generating AI, which can adjust the use of technical terminology in the provided information. === 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 is realized by the reception device 38 of the smart device 14 and receives text information, image data, and voice input entered by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes user information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies areas of expertise and future potential using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides specific advice and career paths to the user. === 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 is realized by the microphone 238 of the smart glasses 214 and receives voice information input by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes user information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies areas of expertise and future potential using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with specific advice and career paths. === 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 is realized by the microphone 238 of the headset-type terminal 314 and receives voice information input by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes user information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies areas of expertise and future potential using a generation AI. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the user with specific advice and career paths. === 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 is realized by the microphone 238 of the robot 414 and receives voice information input by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes user information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies areas of expertise and future potential using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with specific advice and career paths.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also provide the latest trend information related to the user's interests and skills based on the user's input information. For example, if the user is interested in programming, the reception unit can provide information on the latest programming languages ​​and technologies. If the user is interested in data science, the reception unit can provide information on the latest data science research and tools. Furthermore, if the user is interested in a particular industry, the reception unit can provide the latest news and trends in that industry. This allows the user to obtain the latest information related to their interests and skills, enabling more effective career choices.

[0108] The analysis unit can compare the user's skills and experience with those of other users based on the user's input information. For example, if the user has programming skills, the analysis unit can compare them with the skill levels of other programmers to evaluate the user's skill level. Also, if the user has experience participating in a specific project, the analysis unit can compare them with the experience of other project participants to evaluate the value of the user's experience. Furthermore, the analysis unit can compare the user's educational background and work history with those of other users to evaluate the user's potential. This allows the user to understand the level of their skills and experience compared to those of other users and identify directions for self-improvement.

[0109] The identification unit can suggest learning resources and training programs suitable for the user based on the user's input information. For example, if the user wants to improve their programming skills, the identification unit can suggest online courses and learning materials. If the user wants to acquire data science skills, the identification unit can suggest data science training programs and workshops. Furthermore, if the user wants to obtain a specific qualification, the identification unit can suggest learning resources and exam preparation related to that qualification. This allows the user to obtain resources to effectively improve their skills and knowledge.

[0110] The provider can suggest networking events and communities suitable for the user based on the user's input information. For example, if the user is interested in programming, the provider can suggest programming-related meetups and conferences. If the user is interested in data science, the provider can suggest data science communities and forums. Furthermore, if the user is interested in a specific industry, the provider can suggest professional networking events in that industry. This allows the user to access networking opportunities related to their interests and skills, which can help with career development.

[0111] The offering unit can suggest internship and volunteer opportunities suitable for the user based on the user's input information. For example, if the user wants to improve their programming skills in a practical way, the offering unit can suggest programming-related internships. Alternatively, if the user wants to gain experience in data science, the offering unit can suggest volunteer projects in data science. Furthermore, if the user wants to gain experience in a specific industry, the offering unit can suggest internship and volunteer opportunities in that industry. This allows the user to have opportunities to gain practical experience and help advance their career.

[0112] The reception unit can estimate the user's emotions and provide the user with a relaxing environment based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can display relaxing music or scenery images. If the user is feeling tense, the reception unit can also provide deep breathing or meditation guidance. Furthermore, if the user is tired, the reception unit can suggest a short break and provide refreshing content. This allows the user to enter information in a relaxed state, and more accurate information can be provided.

[0113] The analysis unit can estimate the user's emotions and adjust the feedback of the analysis results based on the estimated user emotions. For example, if the user has positive emotions, the analysis unit can provide detailed feedback and emphasize the user's successful experiences. If the user has negative emotions, the analysis unit can also provide feedback including encouraging messages. Furthermore, if the user is feeling anxious, the analysis unit can indicate specific areas for improvement and suggest next steps. This allows the user to receive feedback that corresponds to their emotions, making it easier for them to maintain their motivation.

[0114] The identification unit can estimate the user's emotions and customize the identified results based on the estimated user emotions. For example, if the user is excited, the identification unit can suggest a challenging career path. If the user is anxious, the identification unit can also suggest a stable career path. Furthermore, if the user is relaxed, the identification unit can provide a variety of options and allow the user to choose freely. This allows the user to select a career path that suits their emotions, resulting in a more satisfying choice.

[0115] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user prefers visual information, the providing unit can provide information using infographics or visual aids. If the user prefers text-based information, the providing unit can provide a detailed text report. Furthermore, if the user prefers audio information, the providing unit can provide information in the form of an audio guide or podcast. This allows the user to receive information in a format that suits their emotions and preferences, making it easier to understand.

[0116] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user emotions. For example, if the user is concentrating, the providing unit can provide detailed information all at once. Also, if the user is tired, the providing unit can provide information in small chunks. Furthermore, if the user is in a hurry, the providing unit can quickly provide information that covers the main points. This allows the user to receive information at a timing that suits their emotions and situation, and to utilize information efficiently.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives information from the user. Information from the user includes interests, experience, skills, past achievements, and educational background. The reception unit can receive text information entered by the user, uploaded image data, and information entered by voice. The reception unit also converts the text information entered by the user into a format that is easy to analyze. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit analyzes information such as the user's interests, experience, skills, past achievements, and educational background to predict future potential. Step 3: The identification unit uses the generation AI to identify areas of expertise and future potential based on the information analyzed by the analysis unit. The identification unit identifies areas of expertise, such as technical fields or business fields, and identifies the user's future potential. The identification unit also provides specific advice based on the user's information. Step 4: The provision unit provides the user with the information identified by the identification unit. The information is provided in text or image format. The provision unit suggests specific advice, career paths, and necessary skill sets to the user, and suggests future potential.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 information from a user; an analysis unit that analyzes the information received by the reception unit; an identification unit that identifies a specialty or future potential based on the information analyzed by the analysis unit; a providing unit that provides the information identified by the identifying unit to a user. A system characterized by:

2. The reception unit Accepts information including user interests, experience, skills, past achievements, and educational background 2. The system of claim 1.

3. The analysis unit The generation AI analyzes the information received by the reception unit.

2. The system of claim 1.

4. The identification unit The generation AI identifies areas of expertise and future potential based on the information analyzed by the analysis unit.

2. The system of claim 1.

5. The providing unit Providing specific advice to the user based on the information identified by the identifying unit 2. The system of claim 1.

6. The providing unit Suggest career paths and required skill sets 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the information input method based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past input history and provide an appropriate input interface 2. The system of claim 1.

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