System

Abstract

An object of the system according to the embodiment is to provide an optimal solution or opportunity for a user's trouble or skill.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit receives a user's problem or skill. The analysis unit analyzes the information input by the reception unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit. The customization unit customizes the content proposed by the proposal unit in accordance with the needs of 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 do not adequately provide optimal solutions and opportunities for users' concerns and skills, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal solutions and opportunities for users' concerns and skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs the user's concerns or skills. The analysis unit analyzes the information input by the reception unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The customization unit customizes the content proposed by the proposal unit to meet the user's needs. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal solutions and opportunities for users' concerns and skills. [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 efficiently analyzes a user's concerns and skills and provides optimal solutions and opportunities for utilizing those skills. This system allows users to input their concerns, problems, or skills they wish to utilize, and a generation AI analyzes the information and proposes optimal solutions and opportunities for utilizing those skills. The proposed solutions and opportunities are customized to fit the user's lifestyle and needs. This allows the system to efficiently analyze a user's concerns and skills and provide optimal solutions and opportunities for utilizing those skills. For example, using a housekeeping service can reduce the burden of housework and allow users to use their time more effectively. Furthermore, using an online educational program can alleviate concerns about their children's education. Furthermore, participating in freelance programming projects allows users to utilize their skills and earn income.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs a user's concerns or skills. For example, the user can input specific content such as "I'm too busy with housework and don't have time," "I'm worried about my child's education," or "I want to use my programming skills." The analysis unit uses a generation AI to analyze the information input by the reception unit. The generation AI generates optimal solutions for the user based on past data and similar cases. For example, the generation AI makes specific proposals such as "proposing the use of a housekeeping service," "introducing an online educational program," or "introducing freelance programming jobs." The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit presents the solutions and skill-utilizing opportunities proposed by the generation AI to the user. The customization unit customizes the content proposed by the proposal unit to suit the user's needs. For example, a proposal for a housekeeping service may introduce services tailored to the user's area and budget. Furthermore, a proposal for an online educational program may introduce programs tailored to the child's age and learning style. Furthermore, freelance programming jobs are introduced that match the user's skill level and interests. This allows the system according to the embodiment to efficiently analyze the user's concerns and skills and provide optimal solutions and opportunities to utilize their skills.

[0030] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the worries and skills that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest worries and skills that will be input during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0031] The reception unit can filter the input content based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the reception unit preferentially displays worries and skills related to that situation. The reception unit can also filter and display related worries and skills based on the user's areas of interest. The reception unit can also automatically filter the input content based on the user's living situation and areas of interest and suggest optimal candidates. This makes it possible to provide optimal input content according to the user's living situation and areas of interest.

[0032] The reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the user's worries and skills using voice recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the input content using text analysis technology. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology. This makes it possible to provide the optimal input means according to the user's input method.

[0033] The reception unit can prioritize acquiring highly relevant input content in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying concerns and skills related to that area. The reception unit can also filter and display related concerns and skills based on the user's geographical location information. The reception unit can also automatically suggest optimal input content in consideration of the user's geographical location information. This makes it possible to provide optimal input content based on the user's geographical location information.

[0034] The reception unit can analyze the user's social media activity and acquire related input content. For example, the reception unit can analyze content posted by the user on social media and suggest related concerns and skills. The reception unit can also filter and display related concerns and skills based on the user's social media activity history. The reception unit can also suggest related concerns and skills by referring to the activities of the user's friends on social media. This makes it possible to provide optimal input content based on the user's social media activity.

[0035] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. This makes it possible to provide the optimal input method based on the user's past feedback.

[0036] The analysis unit can improve the accuracy of the analysis by referring to past data and similar cases. The analysis unit can improve the accuracy of the analysis by referring to similar cases based on past data, for example. The analysis unit can also analyze similar cases and apply the optimal analysis method. The analysis unit can also improve the accuracy of the analysis by referring to past data. This makes it possible to provide optimal analysis accuracy based on past data and similar cases.

[0037] The analysis unit can apply different analysis methods depending on the category of the user's input content. For example, if the user's input content is related to housework, the analysis unit applies an analysis method specialized for housework. Furthermore, if the user's input content is related to education, the analysis unit can also apply an analysis method specialized for education. Furthermore, if the user's input content is related to skill utilization, the analysis unit can also apply an analysis method specialized for skill utilization. This makes it possible to provide the optimal analysis method according to the category of the user's input content.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past analysis results. The analysis unit can also apply an optimal analysis method by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to provide optimal analysis accuracy based on the user's past analysis results.

[0039] The analysis unit can determine the priority of analysis based on the time of submission of the user's input content. For example, if the user is in a hurry, the analysis unit determines the priority of analysis based on the time of submission. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide the results. Furthermore, if the user is feeling stressed, the analysis unit can perform a quick analysis and provide the results. This makes it possible to provide optimal analysis priorities based on the time of submission of the user's input content.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the user's input content. For example, if the user's input content is highly relevant, the analysis unit performs analysis with priority. Also, if the user's input content is less relevant, the analysis unit can postpone analysis. Also, the analysis unit can adjust the order of analysis based on the relevance of the user's input content. This makes it possible to provide an optimal analysis order based on the relevance of the user's input content.

[0041] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the solution or skill utilization when making a proposal. For example, the suggestion unit provides a detailed proposal for a solution or skill utilization that is highly important. The suggestion unit can also provide a concise proposal for a solution or skill utilization that is less important. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the solution or skill utilization. This makes it possible to provide an optimal level of detail of the proposal based on the importance of the solution or skill utilization.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution or skill utilization. For example, in the case of a solution related to housework, the proposal unit applies a proposal algorithm specialized for housework. In addition, in the case of a solution related to education, the proposal unit can also apply a proposal algorithm specialized for education. In addition, in the case of a solution related to skill utilization, the proposal unit can also apply a proposal algorithm specialized for skill utilization. This makes it possible to provide the optimal proposal algorithm depending on the category of the solution or skill utilization.

[0044] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit improves the accuracy of the proposal, for example, based on the user's past proposal results. The suggestion unit can also apply an optimal suggestion method by referring to the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. This makes it possible to provide optimal suggestion accuracy based on the user's past proposal results.

[0045] The suggestion unit can determine the priority of proposals based on the time of submission of solutions and skill utilization when making proposals. For example, the suggestion unit preferentially proposes solutions and skill utilizations that are submitted early. The suggestion unit can also postpone the proposal of solutions and skill utilizations that are submitted later. The suggestion unit can also adjust the priority of proposals based on the time of submission. This makes it possible to provide optimal priority of proposals based on the time of submission of solutions and skill utilization.

[0046] The suggestion unit can adjust the order of proposals based on the relevance of solutions and skill utilization when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant solutions and skill utilization. The suggestion unit can also postpone proposing less relevant solutions and skill utilization. The suggestion unit can also adjust the order of proposals based on the relevance of solutions and skill utilization. This makes it possible to provide an optimal order of proposals based on the relevance of solutions and skill utilization.

[0047] The suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. For example, if the user has technical expertise, the suggestion unit can provide a suggestion that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a concise and easy-to-understand suggestion. Furthermore, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. This makes it possible to provide the most appropriate technical terminology in the suggestion according to the user's level of expertise.

[0048] During customization, the customization unit can analyze the user's past customization history and select the optimal customization method. For example, the customization unit can propose the optimal customization method based on the user's past customization history. The customization unit can also apply the optimal customization technique by referring to the user's past customization history. The customization unit can also analyze the user's past customization history and select the optimal customization method. This makes it possible to provide the optimal customization method based on the user's past customization history.

[0049] During customization, the customization unit can customize the customization means based on the user's current living situation and needs. The customization unit, for example, proposes the optimal customization means based on the user's current living situation. The customization unit can also adjust the customization means based on the user's needs. The customization unit can also customize the customization means based on the user's living situation and needs. This makes it possible to provide the optimal customization means based on the user's current living situation and needs.

[0050] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit improves the customization method based on user feedback. The customization unit can also refer to user feedback to apply an optimal customization technique. The customization unit can also analyze user feedback and improve the customization method. This makes it possible to provide an optimal customization method based on user feedback.

[0051] During customization, the customization unit can select a customization method based on the user's geographical location information. For example, the customization unit can propose an optimal customization method based on the user's geographical location information. The customization unit can also adjust the customization means based on the user's geographical location information. The customization unit can also select an optimal customization method taking the user's geographical location information into consideration. This makes it possible to provide an optimal customization method based on the user's geographical location information.

[0052] During customization, the customization unit can analyze the user's social media activity and suggest a customization method. For example, the customization unit can suggest an optimal customization method based on the user's social media activity. The customization unit can also adjust the customization method by referring to the user's social media activity. The customization unit can also analyze the user's social media activity and suggest an optimal customization method. This makes it possible to provide an optimal customization method based on the user's social media activity.

[0053] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit can adjust the customization method based on the user's past feedback, for example. The customization unit can also apply an optimal customization technique by referring to the user's past feedback. The customization unit can also analyze the user's past feedback and customize the customization method. This makes it possible to provide an optimal customization method based on the user's past feedback.

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

[0055] The reception unit can also monitor the user's health condition and adjust the input content. For example, if the user feels tired, simple input options are provided to reduce the user's burden. Also, if the user's health condition is good, detailed input options are provided to collect more information. Furthermore, if the user has a specific health problem, input options related to that problem can be displayed preferentially. This makes it possible to provide the optimal input method according to the user's health condition.

[0056] The analysis unit can also analyze the user's past behavioral patterns and predict analysis results. For example, if the user has repeatedly performed a specific behavior in the past, the analysis result can be predicted based on that behavior. Also, if the user has performed a specific time period in the past, the analysis result can be predicted based on that time period. Furthermore, future behavior can be predicted based on the user's past behavioral patterns, and analysis results can be provided. This makes it possible to provide optimal analysis results based on the user's past behavioral patterns.

[0057] The suggestion unit can also monitor the user's current activity status and adjust the suggestion content. For example, if the user is exercising, it can prioritize exercise-related suggestions. If the user is working, it can prioritize work-related suggestions. Furthermore, if the user is taking a break, it can make suggestions related to relaxation. This makes it possible to provide optimal suggestions according to the user's current activity status.

[0058] The customization unit can also analyze the user's device usage status and adjust the customization method. For example, if the user frequently uses a smartphone, a customization method optimized for the smartphone can be provided. If the user frequently uses a tablet, a customization method optimized for the tablet can be provided. Furthermore, if the user frequently uses a desktop, a customization method optimized for the desktop can be provided. This makes it possible to provide the optimal customization method according to the user's device usage status.

[0059] The reception unit can also analyze the user's input content in real time to improve the accuracy of the input content. For example, if the user makes a typo or omission while inputting, the reception unit can suggest corrections in real time. It can also suggest that the user add related information while inputting. Furthermore, if the user is unclear about something while inputting, the reception unit can ask questions in real time to improve the accuracy of the input content. This makes it possible to improve the accuracy of the user's input content in real time.

[0060] The analysis unit can also analyze the user's input on the cloud and provide the analysis results quickly. For example, if a user inputs a large amount of data, the analysis can be performed on the cloud and the results can be provided quickly. Also, if a user inputs data from multiple devices, the data can be integrated on the cloud and the analysis results can be provided. Furthermore, if a user inputs data from a remote location, the analysis can be performed on the cloud and the results can be provided quickly. This allows the user's input to be analyzed on the cloud and the analysis results to be provided quickly.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs the user's concerns or skills. For example, the user can input specific details such as "I'm busy with housework and don't have time," "I'm worried about my child's education," or "I want to use my programming skills." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI generates the optimal solution for the user based on past data and similar cases. For example, it makes specific suggestions such as "proposing the use of a housekeeping service," "introducing online educational programs," or "introducing freelance programming jobs." Step 3: The proposal unit makes a proposal based on the analysis results obtained by the analysis unit. For example, it presents the user with solutions or skill utilization opportunities proposed by the generation AI. Step 4: The customization department customizes the content proposed by the proposal department to fit the user's needs. For example, a proposal for a housekeeping service will introduce services tailored to the user's area and budget. A proposal for an online education program will introduce programs tailored to the child's age and learning style. Furthermore, a proposal for freelance programming jobs will introduce jobs tailored to the user's skill level and interests.

[0063] (Example 2) A system according to an embodiment of the present invention efficiently analyzes a user's concerns and skills and provides optimal solutions and opportunities for utilizing those skills. This system allows users to input their concerns, problems, or skills they wish to utilize, and a generation AI analyzes the information and proposes optimal solutions and opportunities for utilizing those skills. The proposed solutions and opportunities are customized to fit the user's lifestyle and needs. This allows the system to efficiently analyze a user's concerns and skills and provide optimal solutions and opportunities for utilizing those skills. For example, using a housekeeping service can reduce the burden of housework and allow users to use their time more effectively. Furthermore, using an online educational program can alleviate concerns about their children's education. Furthermore, participating in freelance programming projects allows users to utilize their skills and earn income.

[0064] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a customization unit. The reception unit inputs a user's concerns or skills. For example, the user can input specific content such as "I'm too busy with housework and don't have time," "I'm worried about my child's education," or "I want to use my programming skills." The analysis unit uses a generation AI to analyze the information input by the reception unit. The generation AI generates optimal solutions for the user based on past data and similar cases. For example, the generation AI makes specific proposals such as "proposing the use of a housekeeping service," "introducing an online educational program," or "introducing freelance programming jobs." The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit presents the solutions and skill-utilizing opportunities proposed by the generation AI to the user. The customization unit customizes the content proposed by the proposal unit to suit the user's needs. For example, a proposal for a housekeeping service may introduce services tailored to the user's area and budget. Furthermore, a proposal for an online educational program may introduce programs tailored to the child's age and learning style. Furthermore, freelance programming jobs are introduced that match the user's skill level and interests. This allows the system according to the embodiment to efficiently analyze the user's concerns and skills and provide optimal solutions and opportunities to utilize their skills.

[0065] The reception unit estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input their concerns and skills. This makes it possible to provide an optimal input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the worries and skills that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest worries and skills that will be input during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0067] The reception unit can filter the input content based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the reception unit preferentially displays worries and skills related to that situation. The reception unit can also filter and display related worries and skills based on the user's areas of interest. The reception unit can also automatically filter the input content based on the user's living situation and areas of interest and suggest optimal candidates. This makes it possible to provide optimal input content according to the user's living situation and areas of interest.

[0068] The reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the user's worries and skills using voice recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the input content using text analysis technology. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology. This makes it possible to provide the optimal input means according to the user's input method.

[0069] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize and display urgent concerns. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception unit can also prioritize voice input to enable the user to quickly input concerns and skills. This makes it possible to provide optimal input content priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The reception unit can prioritize acquiring highly relevant input content in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying concerns and skills related to that area. The reception unit can also filter and display related concerns and skills based on the user's geographical location information. The reception unit can also automatically suggest optimal input content in consideration of the user's geographical location information. This makes it possible to provide optimal input content based on the user's geographical location information.

[0071] The reception unit can analyze the user's social media activity and acquire related input content. For example, the reception unit can analyze content posted by the user on social media and suggest related concerns and skills. The reception unit can also filter and display related concerns and skills based on the user's social media activity history. The reception unit can also suggest related concerns and skills by referring to the activities of the user's friends on social media. This makes it possible to provide optimal input content based on the user's social media activity.

[0072] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. The reception unit can also analyze the user's past feedback and improve the input method. This makes it possible to provide the optimal input method based on the user's past feedback.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can quickly perform an analysis and provide the results. If the user is relaxed, the analysis unit can also perform a detailed analysis and provide the results. If the user is in a hurry, the analysis unit can also perform a concise analysis and provide the results. This makes it possible to provide an optimal analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The analysis unit can improve the accuracy of the analysis by referring to past data and similar cases. The analysis unit can improve the accuracy of the analysis by referring to similar cases based on past data, for example. The analysis unit can also analyze similar cases and apply the optimal analysis method. The analysis unit can also improve the accuracy of the analysis by referring to past data. This makes it possible to provide optimal analysis accuracy based on past data and similar cases.

[0075] The analysis unit can apply different analysis methods depending on the category of the user's input content. For example, if the user's input content is related to housework, the analysis unit applies an analysis method specialized for housework. Furthermore, if the user's input content is related to education, the analysis unit can also apply an analysis method specialized for education. Furthermore, if the user's input content is related to skill utilization, the analysis unit can also apply an analysis method specialized for skill utilization. This makes it possible to provide the optimal analysis method according to the category of the user's input content.

[0076] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past analysis results. The analysis unit can also apply an optimal analysis method by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to provide optimal analysis accuracy based on the user's past analysis results.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to provide an optimal display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The analysis unit can determine the priority of analysis based on the time of submission of the user's input content. For example, if the user is in a hurry, the analysis unit determines the priority of analysis based on the time of submission. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide the results. Furthermore, if the user is feeling stressed, the analysis unit can perform a quick analysis and provide the results. This makes it possible to provide optimal analysis priorities based on the time of submission of the user's input content.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the user's input content. For example, if the user's input content is highly relevant, the analysis unit performs analysis with priority. Also, if the user's input content is less relevant, the analysis unit can postpone analysis. Also, the analysis unit can adjust the order of analysis based on the relevance of the user's input content. This makes it possible to provide an optimal analysis order based on the relevance of the user's input content.

[0080] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0081] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide a simple, highly visible suggestion method. If the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. This makes it possible to provide an optimal way of expressing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The suggestion unit can adjust the level of detail of the proposal based on the importance of the solution or skill utilization when making a proposal. For example, the suggestion unit provides a detailed proposal for a solution or skill utilization that is highly important. The suggestion unit can also provide a concise proposal for a solution or skill utilization that is less important. The suggestion unit can also adjust the level of detail of the proposal based on the importance of the solution or skill utilization. This makes it possible to provide an optimal level of detail of the proposal based on the importance of the solution or skill utilization.

[0083] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution or skill utilization. For example, in the case of a solution related to housework, the proposal unit applies a proposal algorithm specialized for housework. In addition, in the case of a solution related to education, the proposal unit can also apply a proposal algorithm specialized for education. In addition, in the case of a solution related to skill utilization, the proposal unit can also apply a proposal algorithm specialized for skill utilization. This makes it possible to provide the optimal proposal algorithm depending on the category of the solution or skill utilization.

[0084] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit improves the accuracy of the proposal, for example, based on the user's past proposal results. The suggestion unit can also apply an optimal suggestion method by referring to the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. This makes it possible to provide optimal suggestion accuracy based on the user's past proposal results.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can also provide concise, quick suggestions. This allows the suggestion unit to provide the optimal length of suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The suggestion unit can determine the priority of proposals based on the time of submission of solutions and skill utilization when making proposals. For example, the suggestion unit preferentially proposes solutions and skill utilizations that are submitted early. The suggestion unit can also postpone the proposal of solutions and skill utilizations that are submitted later. The suggestion unit can also adjust the priority of proposals based on the time of submission. This makes it possible to provide optimal priority of proposals based on the time of submission of solutions and skill utilization.

[0087] The suggestion unit can adjust the order of proposals based on the relevance of solutions and skill utilization when making a proposal. For example, the suggestion unit prioritizes proposing highly relevant solutions and skill utilization. The suggestion unit can also postpone proposing less relevant solutions and skill utilization. The suggestion unit can also adjust the order of proposals based on the relevance of solutions and skill utilization. This makes it possible to provide an optimal order of proposals based on the relevance of solutions and skill utilization.

[0088] The suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise when making a suggestion. For example, if the user has technical expertise, the suggestion unit can provide a suggestion that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also provide a concise and easy-to-understand suggestion. Furthermore, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. This makes it possible to provide the most appropriate technical terminology in the suggestion according to the user's level of expertise.

[0089] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, if the user is feeling stressed, the customization unit provides a simple and highly visible customization method. Furthermore, if the user is relaxed, the customization unit can provide a customization method that includes detailed information. Furthermore, if the user is in a hurry, the customization unit can provide a customization method that focuses on the main points. This makes it possible to provide an optimal customization method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] During customization, the customization unit can analyze the user's past customization history and select the optimal customization method. For example, the customization unit can propose the optimal customization method based on the user's past customization history. The customization unit can also apply the optimal customization technique by referring to the user's past customization history. The customization unit can also analyze the user's past customization history and select the optimal customization method. This makes it possible to provide the optimal customization method based on the user's past customization history.

[0091] During customization, the customization unit can customize the customization means based on the user's current living situation and needs. The customization unit, for example, proposes the optimal customization means based on the user's current living situation. The customization unit can also adjust the customization means based on the user's needs. The customization unit can also customize the customization means based on the user's living situation and needs. This makes it possible to provide the optimal customization means based on the user's current living situation and needs.

[0092] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit improves the customization method based on user feedback. The customization unit can also refer to user feedback to apply an optimal customization technique. The customization unit can also analyze user feedback and improve the customization method. This makes it possible to provide an optimal customization method based on user feedback.

[0093] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, if the user is feeling stressed, the customization unit can prioritize customization with a high degree of urgency. The customization unit can also provide detailed customization when the user is relaxed. The customization unit can also quickly perform customization when the user is in a hurry. This makes it possible to provide optimal customization priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] During customization, the customization unit can select a customization method based on the user's geographical location information. For example, the customization unit can propose an optimal customization method based on the user's geographical location information. The customization unit can also adjust the customization means based on the user's geographical location information. The customization unit can also select an optimal customization method taking the user's geographical location information into consideration. This makes it possible to provide an optimal customization method based on the user's geographical location information.

[0095] During customization, the customization unit can analyze the user's social media activity and suggest a customization method. For example, the customization unit can suggest an optimal customization method based on the user's social media activity. The customization unit can also adjust the customization method by referring to the user's social media activity. The customization unit can also analyze the user's social media activity and suggest an optimal customization method. This makes it possible to provide an optimal customization method based on the user's social media activity.

[0096] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. The customization unit can adjust the customization method based on the user's past feedback, for example. The customization unit can also apply an optimal customization technique by referring to the user's past feedback. The customization unit can also analyze the user's past feedback and customize the customization method. This makes it possible to provide an optimal customization method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's concerns and skills. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes suggestions based on the analysis results. The customization unit is realized by the control unit 46A of the smart device 14 and customizes the suggested content to suit the user's needs. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, suggestion unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's concerns and skills. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes suggestions based on the analysis results. The customization unit is realized by the control unit 46A of the smart glasses 214 and customizes the suggested content to suit the user's needs. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and customization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the user's concerns and skills. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes proposals based on the analysis results. The customization unit is realized by the control unit 46A of the headset type terminal 314 and customizes the proposed content to suit the user's needs. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's concerns and skills. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes proposals based on the analysis results. The customization unit is realized by the control unit 46A of the robot 414 and customizes the proposed content to meet the user's needs.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can also monitor the user's health condition and adjust the input content. For example, if the user feels tired, simple input options are provided to reduce the user's burden. Also, if the user's health condition is good, detailed input options are provided to collect more information. Furthermore, if the user has a specific health problem, input options related to that problem can be displayed preferentially. This makes it possible to provide the optimal input method according to the user's health condition.

[0099] The analysis unit can also estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the notification can be made less severe, and if the user is relaxed, the notification can be made more detailed. Also, if the user is in a hurry, the notification can be made concise to provide information quickly. This makes it possible to provide the optimal notification method of the analysis results according to the user's emotions.

[0100] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make less suggestions, and if the user is relaxed, the suggestion unit can make more suggestions. Also, if the user is in a hurry, the suggestion unit can make quick suggestions to save the user time. This makes it possible to provide the optimal timing of suggestions according to the user's emotions.

[0101] The customization unit can also estimate the user's emotions and adjust the frequency of customization based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of customization can be reduced, and if the user is relaxed, the frequency of customization can be increased. Also, if the user is in a hurry, the customization can be performed quickly to save the user's time. This makes it possible to provide an optimal frequency of customization according to the user's emotions.

[0102] The receiving unit can also estimate the user's emotions and adjust the feedback method for the input content based on the estimated user emotions. For example, if the user is feeling stressed, positive feedback can be prioritized, and if the user is relaxed, detailed feedback can be provided. Also, if the user is in a hurry, brief feedback can be provided to quickly provide information. This makes it possible to provide the optimal feedback method for the input content according to the user's emotions.

[0103] The analysis unit can also analyze the user's past behavioral patterns and predict analysis results. For example, if the user has repeatedly performed a specific behavior in the past, the analysis result can be predicted based on that behavior. Also, if the user has performed a specific time period in the past, the analysis result can be predicted based on that time period. Furthermore, future behavior can be predicted based on the user's past behavioral patterns, and analysis results can be provided. This makes it possible to provide optimal analysis results based on the user's past behavioral patterns.

[0104] The suggestion unit can also monitor the user's current activity status and adjust the suggestion content. For example, if the user is exercising, it can prioritize exercise-related suggestions. If the user is working, it can prioritize work-related suggestions. Furthermore, if the user is taking a break, it can make suggestions related to relaxation. This makes it possible to provide optimal suggestions according to the user's current activity status.

[0105] The customization unit can also analyze the user's device usage status and adjust the customization method. For example, if the user frequently uses a smartphone, a customization method optimized for the smartphone can be provided. If the user frequently uses a tablet, a customization method optimized for the tablet can be provided. Furthermore, if the user frequently uses a desktop, a customization method optimized for the desktop can be provided. This makes it possible to provide the optimal customization method according to the user's device usage status.

[0106] The reception unit can also analyze the user's input content in real time to improve the accuracy of the input content. For example, if the user makes a typo or omission while inputting, the reception unit can suggest corrections in real time. It can also suggest that the user add related information while inputting. Furthermore, if the user is unclear about something while inputting, the reception unit can ask questions in real time to improve the accuracy of the input content. This makes it possible to improve the accuracy of the user's input content in real time.

[0107] The analysis unit can also analyze the user's input on the cloud and provide the analysis results quickly. For example, if a user inputs a large amount of data, the analysis can be performed on the cloud and the results can be provided quickly. Also, if a user inputs data from multiple devices, the data can be integrated on the cloud and the analysis results can be provided. Furthermore, if a user inputs data from a remote location, the analysis can be performed on the cloud and the results can be provided quickly. This allows the user's input to be analyzed on the cloud and the analysis results to be provided quickly.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit inputs the user's concerns or skills. For example, the user can input specific details such as "I'm busy with housework and don't have time," "I'm worried about my child's education," or "I want to use my programming skills." Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The generation AI generates the optimal solution for the user based on past data and similar cases. For example, it makes specific suggestions such as "proposing the use of a housekeeping service," "introducing online educational programs," or "introducing freelance programming jobs." Step 3: The proposal unit makes a proposal based on the analysis results obtained by the analysis unit. For example, it presents the user with solutions or skill utilization opportunities proposed by the generation AI. Step 4: The customization department customizes the content proposed by the proposal department to fit the user's needs. For example, a proposal for a housekeeping service will introduce services tailored to the user's area and budget. A proposal for an online education program will introduce programs tailored to the child's age and learning style. Furthermore, a proposal for freelance programming jobs will introduce jobs tailored to the user's skill level and interests.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

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

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

[0140] In the 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.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0144] 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.

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

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 for inputting user's concerns or skills; an analysis unit that analyzes the information input by the reception unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; a customization unit that customizes the content proposed by the proposal unit to meet the needs of the user. A system characterized by:

2. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

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

4. The reception unit Filtering input based on the user's current life situation or interests 2. The system of claim 1.

5. The reception unit Select an input method according to the user's input method 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Prioritize relevant input based on the user's geographic location 2. The system of claim 1.

8. The reception unit Analyze users' social media activity and capture relevant inputs 2. The system of claim 1.

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