system

Abstract

The system according to the embodiment aims to efficiently provide solutions to questions and problems within a user community. [Solution] A system according to an embodiment includes a sharing unit, a learning unit, and a providing unit. The sharing unit shares questions or problems within a user community and provides solutions. The learning unit learns from data obtained from the sharing unit and provides answers to new questions. The providing unit provides users with the answers obtained by the learning unit or specific business procedures or operating methods.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not efficiently provide solutions to questions and problems within user communities, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide solutions to questions and problems within a user community. [Means for solving the problem]

[0006] The system according to the embodiment includes a sharing unit, a learning unit, and a providing unit. The sharing unit shares questions or problems within a user community and provides solutions. The learning unit learns data obtained from the sharing unit and provides answers to new questions. The providing unit provides users with the answers obtained by the learning unit or specific business procedures or operating methods. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide solutions to questions and problems within a user community. [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) An application system according to an embodiment of the present invention provides users with a series of work procedures and software operation methods, thereby providing a method for completing specific tasks within a company. This application system allows users to share questions and problems within a user community and provides solutions. Furthermore, the application system uses AI to learn from past Q&A data and automatically provide appropriate answers to newly-asked questions. For example, the application system allows users to search for specific work procedures or software operation methods. For example, if a user searches for "how to create a pivot table in Excel," the application system provides a guide that explains the procedure in detail. This guide provides step-by-step instructions and is designed to be easy for users to understand. Next, the application system provides methods for completing specific tasks within a company. For example, the application system provides specific procedures related to the work, such as how to start a new project or how to create a specific report. This allows employees to perform their work efficiently. Furthermore, the application system allows users to share questions and problems within a user community and provide solutions. Users can post their questions and problems to the community and receive answers from other users. For example, if a user posts "what to do when a specific software error message appears," other users will provide solutions. Finally, the application system uses AI to learn from past Q&A data and automatically provide appropriate answers to newly asked questions. AI analyzes past Q&A data and generates answers to similar questions. For example, it learns from past questions and answers about "how to create a pivot table in Excel" and automatically provides answers when a similar question is newly asked. This allows the application system to efficiently teach users work procedures and operations, facilitating smooth internal business operations. In addition, by utilizing the user community, questions and problems can be resolved quickly. Furthermore, the AI's learning function makes it possible to obtain appropriate answers to new questions.

[0029] An application system according to an embodiment includes a sharing unit, a learning unit, and a providing unit. The sharing unit allows users to share questions or problems within a user community and provide solutions. For example, the sharing unit allows users to post questions about error messages for specific software and receive answers from other users. The sharing unit also allows users to share questions or problems in various formats, such as text, image, and video. The learning unit learns from data obtained from the sharing unit and provides answers to new questions. For example, the learning unit analyzes past Q&A data and generates answers to similar questions. The learning unit can use a machine learning algorithm to perform learning based on the type of data and the frequency of learning. The providing unit provides users with the answers obtained by the learning unit or specific business procedures or operating methods. For example, the providing unit provides a guide on "how to create a pivot table in Excel" that the user searched for. The providing unit can provide users with information based on the notification method, the timing of the provision, and the format of the provision. This allows the application system according to an embodiment to share questions or problems within a user community and provide solutions.

[0030] The learning unit can learn from past Q&A data and provide answers to new questions. Past Q&A data includes, but is not limited to, data from a specific period or a specific category. For example, the learning unit learns answers to similar questions based on past Q&A data. The learning unit can also extract frequently occurring questions and problems from past Q&A data and optimize the learning algorithm. Furthermore, the learning unit can analyze past Q&A data and adjust the learning algorithm to improve the accuracy of answers. In this way, by learning from past Q&A data, appropriate answers can be provided to new questions. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past Q&A data into a generation AI and cause the generation AI to generate answers.

[0031] The providing unit can provide specific business procedures or operation methods to a user. Specific business procedures and operation methods include, but are not limited to, software operation procedures and business process procedures. For example, the providing unit can provide a guide for a user's search for "how to create a pivot table in Excel." The providing unit can also provide specific business-related procedures, such as procedures for starting a new project or creating a specific report. Furthermore, the providing unit can provide a guide designed to show operation methods step by step and make them easy for users to understand. This allows users to work efficiently by providing specific business procedures and operation methods. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input keywords searched by a user into a generating AI and have the generating AI execute a guide for the related business procedures or operation methods.

[0032] The providing unit can provide the user with the answer obtained by the learning unit. The answer may be, for example, in text format or video format, but is not limited to these examples. The providing unit can provide the user with the answer obtained by the learning unit, for example. The providing unit can also provide the user with the answer generated by the learning unit based on the notification method and the timing of the provision. Furthermore, the providing unit can customize the answer generated by the learning unit according to the user's needs and provide it. This allows the user to quickly resolve their question by providing the answer obtained by the learning unit. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the answer generated by the learning unit into a generation AI and convert it into an optimal format for providing to the user.

[0033] The sharing unit can analyze a user's past posting history and select the optimal sharing method. For example, the sharing unit can prioritize and suggest sharing methods (text, image, etc.) that the user has frequently used in the past. The sharing unit can also suggest the optimal sharing method for a specific time period based on the user's past posting history. Furthermore, the sharing unit can analyze the content of a user's past posts and automatically share related information. In this way, the optimal sharing method can be selected by analyzing the user's past posting history. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's past posting data into a generation AI and have the generation AI select the optimal sharing method.

[0034] When a question or problem is shared, the sharing unit can filter the shared question or problem based on the user's current project or area of ​​interest. For example, the sharing unit prioritizes sharing questions or problems related to the project the user is currently working on. The sharing unit can also filter and share related questions or problems based on the user's area of ​​interest. Furthermore, the sharing unit can share questions or problems at an appropriate time depending on the progress of the user's project. This allows highly relevant information to be shared by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the sharing unit may be performed using, or without, AI, for example. For example, the sharing unit can input the user's project data into a generation AI and have the generation AI filter related questions and problems.

[0035] When sharing a question or problem, the sharing unit can select the optimal sharing means depending on the user's input method. For example, if the user uses voice input, the sharing unit can share the question or problem using voice recognition technology. Furthermore, if the user uses text input, the sharing unit can also share the question or problem using text analysis technology. Furthermore, if the user uses image input, the sharing unit can also share the question or problem using image recognition technology. This allows questions and problems to be shared efficiently by selecting the optimal sharing means depending on the user's input method. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0036] When sharing a question or problem, the sharing unit can prioritize sharing highly relevant questions or problems by taking into account the user's geographical location information. For example, if the user is in a specific area, the sharing unit prioritizes sharing questions or problems related to that area. The sharing unit can also prioritize sharing nearby problems and solutions based on the user's current location. Furthermore, the sharing unit can prioritize sharing related questions or problems by taking into account the user's movement history. In this way, highly relevant questions or problems can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the sharing unit may be performed using, or without, AI. For example, the sharing unit can input the user's geographical location data into the generation AI and cause the generation AI to filter related questions and problems.

[0037] When a question or problem is shared, the sharing unit can analyze the user's social media activity and share related questions or problems. For example, the sharing unit can analyze content posted by the user on social media and share related questions or problems. The sharing unit can also share related questions or problems by referring to the activity of the user's friends on social media. Furthermore, the sharing unit can share related questions or problems based on the user's check-in information on social media. In this way, related questions or problems can be shared by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's social media data into the generation AI and have the generation AI analyze related questions or problems.

[0038] The sharing unit can customize the sharing method by reflecting the user's past feedback when sharing a question or problem. For example, the sharing unit can suggest an optimal sharing method based on feedback provided by the user in the past. The sharing unit can also preferentially select a specific sharing method based on the user's past feedback. Furthermore, the sharing unit can also reflect the user's feedback to continuously improve the sharing method. In this way, the optimal sharing method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's feedback data into the generation AI and have the generation AI customize the sharing method.

[0039] During learning, the learning unit can adjust the learning algorithm by referring to past Q&A data. For example, the learning unit learns answers to similar questions based on past Q&A data. The learning unit can also extract frequently occurring questions and problems from past Q&A data and optimize the learning algorithm. Furthermore, the learning unit can analyze past Q&A data and adjust the learning algorithm to improve the accuracy of answers. In this way, the learning algorithm can be optimized by referring to past Q&A data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past Q&A data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0040] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also improve answers to specific questions or problems based on user feedback. Furthermore, the learning unit can continuously update the learning data by reflecting user feedback. This allows the learning data to be continuously updated by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed, for example, using AI or without AI. For example, the learning unit can input user feedback data into a generation AI and cause the generation AI to update the learning data.

[0041] During learning, the learning unit can apply different learning algorithms depending on the category of question or problem. For example, the learning unit applies a specialized learning algorithm to technical questions. The learning unit can also apply a general-purpose learning algorithm to general questions. Furthermore, the learning unit can apply a learning algorithm specialized for a specific category to improve the accuracy of the answer. In this way, by applying different learning algorithms depending on the category of question or problem, the accuracy of the answer is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input category data of questions or problems into the generation AI and cause the generation AI to apply the optimal learning algorithm.

[0042] During learning, the learning unit can weight the learning data based on the time when the question or problem was submitted. For example, the learning unit weights the learning data for recently submitted questions or problems. The learning unit can also weight the learning data for questions or problems submitted during a specific time period. Furthermore, the learning unit can weight the learning data for questions or problems related to seasons or events. In this way, by weighting the learning data based on the time when the question or problem was submitted, more relevant answers can be provided. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time when the question or problem was submitted to the generation AI and have the generation AI perform weighting of the learning data.

[0043] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate information from social media to enrich the learning data. The learning unit can also integrate information from corporate databases to enrich the learning data. Furthermore, the learning unit can also integrate information from open data to enrich the learning data. This allows the learning data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the learning data.

[0044] During learning, the learning unit can adjust the learning algorithm according to the user's level of expertise. For example, the learning unit applies a basic learning algorithm to a novice user. The learning unit can also apply an applied learning algorithm to an intermediate user. Furthermore, the learning unit can also apply a specialized learning algorithm to an advanced user. This allows for adjusting the learning algorithm according to the user's level of expertise to provide more appropriate answers. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0045] The providing unit can adjust the level of detail of the provided information based on the importance of the answer or procedure when providing the information. For example, the providing unit provides detailed information for answers or procedures with high importance. The providing unit can also provide concise information for answers or procedures with low importance. Furthermore, the providing unit can adjust the level of detail of the answers or procedures according to the user's needs. In this way, by adjusting the level of detail based on the importance of the answers or procedures, it is possible to provide information according to the user's needs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the answers or procedures to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0046] The providing unit can apply different providing algorithms depending on the category of the answer or procedure when providing the answer or procedure. For example, the providing unit can apply a specialized providing algorithm to technical answers or procedures. The providing unit can also apply a general-purpose providing algorithm to general answers or procedures. Furthermore, the providing unit can apply a providing algorithm specialized for a specific category to improve the accuracy of the answer or procedure. In this way, by applying a providing algorithm depending on the category of the answer or procedure, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of the answer or procedure into the generation AI and cause the generation AI to apply the optimal providing algorithm.

[0047] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the answer. The providing unit, for example, provides optimal answers or procedures based on the user's past provision results. The providing unit can also preferentially provide specific answers or procedures from the user's past provision results. Furthermore, the providing unit can continuously improve the accuracy of the provision by reflecting user feedback. In this way, the accuracy of the provision can be continuously improved by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0048] The providing unit can determine the priority of providing answers and procedures based on the time of submission of the answers and procedures when providing them. For example, the providing unit can prioritize providing answers and procedures that have been submitted recently. The providing unit can also prioritize providing answers and procedures submitted during a specific time period. Furthermore, the providing unit can prioritize providing answers and procedures related to a season or an event. This allows timely information to be provided by determining the priority based on the time of submission of the answers and procedures. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the time of submission of answers and procedures into the generation AI and have the generation AI determine the priority of providing.

[0049] The providing unit can adjust the order of providing answers and procedures based on the relevance of the answers and procedures when providing them. For example, the providing unit can prioritize providing answers and procedures that are most relevant to the user's current question or problem. The providing unit can also prioritize providing answers and procedures related to the user's past questions or problems. Furthermore, the providing unit can prioritize providing highly relevant answers and procedures based on the user's areas of interest. In this way, by adjusting the order of providing answers and procedures based on the relevance of the answers and procedures, it is possible to prioritize providing the most relevant information to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of answers and procedures to the generating AI and cause the generating AI to adjust the order of providing them.

[0050] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, the providing unit can provide answers and procedures in simple language, avoiding technical terminology, to a novice user. The providing unit can also provide answers and procedures using a moderate amount of technical terminology to an intermediate user. Furthermore, the providing unit can provide detailed answers and procedures using a lot of technical terminology to an advanced user. This allows for the provision of more easily understandable information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terminology.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The providing unit can analyze the user's past search history and provide related information preferentially. For example, it can provide related answers or procedures based on keywords the user has searched for in the past. The providing unit can also provide information related to a specific category from the user's past search history preferentially. Furthermore, the providing unit can analyze the user's past search history and provide frequently searched information preferentially. This makes it possible to provide highly relevant information by analyzing the user's past search history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's search history data into a generation AI and cause the generation AI to provide related information.

[0053] The providing unit can monitor the user's current work status and provide information at an appropriate time. For example, it can provide information on the next related step immediately after the user completes a specific task. The providing unit can also provide an immediate solution if the user is experiencing difficulty during work. Furthermore, the providing unit can also provide necessary preparatory information before the user starts work. In this way, by monitoring the user's current work status, information can be provided at an appropriate time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's work status data into the generation AI and have the generation AI determine the timing of information provision.

[0054] The providing unit can provide region-specific information by taking into account the user's geographical location information. For example, if the user is in a specific region, it can provide business procedures and answers related to that region. The providing unit can also provide region-specific problems and solutions based on the user's current location. Furthermore, the providing unit can provide related information by taking into account the user's movement history. This makes it possible to provide region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's geographical location data into the generating AI and cause the generating AI to provide region-specific information.

[0055] The providing unit can analyze the user's social media activity and provide related information. For example, it can provide related answers or procedures based on content posted by the user on social media. The providing unit can also provide related information by referring to the activity of the user's friends on social media. Furthermore, the providing unit can also provide related information based on the user's check-in information on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to provide related information.

[0056] The providing unit can customize the format and content of the information to be provided by reflecting the user's past feedback. For example, the providing unit can suggest an optimal information format based on feedback provided by the user in the past. The providing unit can also prioritize providing specific information content based on the user's past feedback. Furthermore, the providing unit can also continuously improve the format and content of the information by reflecting the user's feedback. This makes it possible to provide optimal information by reflecting the user's past feedback. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to customize the format and content of the information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The sharing section allows users to share their questions or problems within the user community and provide solutions. For example, users can post questions about error messages for specific software and get answers from other users. The sharing section also allows users to share their questions or problems in various formats, including text, images, and videos. Step 2: The learning unit learns from the data obtained from the sharing unit and provides answers to new questions. For example, the learning unit analyzes past Q&A data and generates answers to similar questions. The learning unit can use machine learning algorithms to learn based on the type of data and learning frequency. Step 3: The providing unit provides the answer obtained by the learning unit or specific business procedures or operation methods to the user. For example, the providing unit provides a guide on "how to create a pivot table in Excel" that the user searched for. The providing unit can provide information to the user based on the notification method, timing of provision, and format of provision.

[0059] (Example 2) An application system according to an embodiment of the present invention provides users with a series of work procedures and software operation methods, thereby providing a method for completing specific tasks within a company. This application system allows users to share questions and problems within a user community and provides solutions. Furthermore, the application system uses AI to learn from past Q&A data and automatically provide appropriate answers to newly-asked questions. For example, the application system allows users to search for specific work procedures or software operation methods. For example, if a user searches for "how to create a pivot table in Excel," the application system provides a guide that explains the procedure in detail. This guide provides step-by-step instructions and is designed to be easy for users to understand. Next, the application system provides methods for completing specific tasks within a company. For example, the application system provides specific procedures related to the work, such as how to start a new project or how to create a specific report. This allows employees to perform their work efficiently. Furthermore, the application system allows users to share questions and problems within a user community and provide solutions. Users can post their questions and problems to the community and receive answers from other users. For example, if a user posts "what to do when a specific software error message appears," other users will provide solutions. Finally, the application system uses AI to learn from past Q&A data and automatically provide appropriate answers to newly asked questions. AI analyzes past Q&A data and generates answers to similar questions. For example, it learns from past questions and answers about "how to create a pivot table in Excel" and automatically provides answers when a similar question is newly asked. This allows the application system to efficiently teach users work procedures and operations, facilitating smooth internal business operations. In addition, by utilizing the user community, questions and problems can be resolved quickly. Furthermore, the AI's learning function makes it possible to obtain appropriate answers to new questions.

[0060] An application system according to an embodiment includes a sharing unit, a learning unit, and a providing unit. The sharing unit allows users to share questions or problems within a user community and provide solutions. For example, the sharing unit allows users to post questions about error messages for specific software and receive answers from other users. The sharing unit also allows users to share questions or problems in various formats, such as text, image, and video. The learning unit learns from data obtained from the sharing unit and provides answers to new questions. For example, the learning unit analyzes past Q&A data and generates answers to similar questions. The learning unit can use a machine learning algorithm to perform learning based on the type of data and the frequency of learning. The providing unit provides users with the answers obtained by the learning unit or specific business procedures or operating methods. For example, the providing unit provides a guide on "how to create a pivot table in Excel" that the user searched for. The providing unit can provide users with information based on the notification method, the timing of the provision, and the format of the provision. This allows the application system according to an embodiment to share questions or problems within a user community and provide solutions.

[0061] The learning unit can learn from past Q&A data and provide answers to new questions. Past Q&A data includes, but is not limited to, data from a specific period or a specific category. For example, the learning unit learns answers to similar questions based on past Q&A data. The learning unit can also extract frequently occurring questions and problems from past Q&A data and optimize the learning algorithm. Furthermore, the learning unit can analyze past Q&A data and adjust the learning algorithm to improve the accuracy of answers. In this way, by learning from past Q&A data, appropriate answers can be provided to new questions. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past Q&A data into a generation AI and cause the generation AI to generate answers.

[0062] The providing unit can provide specific business procedures or operation methods to a user. Specific business procedures and operation methods include, but are not limited to, software operation procedures and business process procedures. For example, the providing unit can provide a guide for a user's search for "how to create a pivot table in Excel." The providing unit can also provide specific business-related procedures, such as procedures for starting a new project or creating a specific report. Furthermore, the providing unit can provide a guide designed to show operation methods step by step and make them easy for users to understand. This allows users to work efficiently by providing specific business procedures and operation methods. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input keywords searched by a user into a generating AI and have the generating AI execute a guide for the related business procedures or operation methods.

[0063] The providing unit can provide the user with the answer obtained by the learning unit. The answer may be, for example, in text format or video format, but is not limited to these examples. The providing unit can provide the user with the answer obtained by the learning unit, for example. The providing unit can also provide the user with the answer generated by the learning unit based on the notification method and the timing of the provision. Furthermore, the providing unit can customize the answer generated by the learning unit according to the user's needs and provide it. This allows the user to quickly resolve their question by providing the answer obtained by the learning unit. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the answer generated by the learning unit into a generation AI and convert it into an optimal format for providing to the user.

[0064] The sharing unit can estimate the user's emotions and adjust the timing of sharing questions or problems based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can quickly share the question or problem and provide a solution as soon as possible. Furthermore, if the user is relaxed, the sharing unit can gradually share the question or problem and provide detailed information. Furthermore, if the user is in a hurry, the sharing unit can select a simple sharing method and quickly provide a solution. This allows the timing of sharing questions or problems to be adjusted according to the user's emotions, enabling sharing at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sharing unit may be performed using, for example, an AI. For example, the sharing unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The sharing unit can analyze a user's past posting history and select the optimal sharing method. For example, the sharing unit can prioritize and suggest sharing methods (text, image, etc.) that the user has frequently used in the past. The sharing unit can also suggest the optimal sharing method for a specific time period based on the user's past posting history. Furthermore, the sharing unit can analyze the content of a user's past posts and automatically share related information. In this way, the optimal sharing method can be selected by analyzing the user's past posting history. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's past posting data into a generation AI and have the generation AI select the optimal sharing method.

[0066] When a question or problem is shared, the sharing unit can filter the shared question or problem based on the user's current project or area of ​​interest. For example, the sharing unit prioritizes sharing questions or problems related to the project the user is currently working on. The sharing unit can also filter and share related questions or problems based on the user's area of ​​interest. Furthermore, the sharing unit can share questions or problems at an appropriate time depending on the progress of the user's project. This allows highly relevant information to be shared by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing in the sharing unit may be performed using, or without, AI, for example. For example, the sharing unit can input the user's project data into a generation AI and have the generation AI filter related questions and problems.

[0067] When sharing a question or problem, the sharing unit can select the optimal sharing means depending on the user's input method. For example, if the user uses voice input, the sharing unit can share the question or problem using voice recognition technology. Furthermore, if the user uses text input, the sharing unit can also share the question or problem using text analysis technology. Furthermore, if the user uses image input, the sharing unit can also share the question or problem using image recognition technology. This allows questions and problems to be shared efficiently by selecting the optimal sharing means depending on the user's input method. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the user's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0068] The sharing unit can estimate the user's emotions and prioritize questions and problems to be shared based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can prioritize sharing questions and problems of high importance. Furthermore, if the user is relaxed, the sharing unit can prioritize sharing questions and problems that include detailed information. Furthermore, if the user is in a hurry, the sharing unit can prioritize sharing concise questions and problems. This allows important problems to be shared quickly by prioritizing questions and problems according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sharing unit can be performed using, for example, an AI. For example, the sharing unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When sharing a question or problem, the sharing unit can prioritize sharing highly relevant questions or problems by taking into account the user's geographical location information. For example, if the user is in a specific area, the sharing unit prioritizes sharing questions or problems related to that area. The sharing unit can also prioritize sharing nearby problems and solutions based on the user's current location. Furthermore, the sharing unit can prioritize sharing related questions or problems by taking into account the user's movement history. In this way, highly relevant questions or problems can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the sharing unit may be performed using, or without, AI. For example, the sharing unit can input the user's geographical location data into the generation AI and cause the generation AI to filter related questions and problems.

[0070] When a question or problem is shared, the sharing unit can analyze the user's social media activity and share related questions or problems. For example, the sharing unit can analyze content posted by the user on social media and share related questions or problems. The sharing unit can also share related questions or problems by referring to the activity of the user's friends on social media. Furthermore, the sharing unit can share related questions or problems based on the user's check-in information on social media. In this way, related questions or problems can be shared by analyzing the user's social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's social media data into the generation AI and have the generation AI analyze related questions or problems.

[0071] The sharing unit can customize the sharing method by reflecting the user's past feedback when sharing a question or problem. For example, the sharing unit can suggest an optimal sharing method based on feedback provided by the user in the past. The sharing unit can also preferentially select a specific sharing method based on the user's past feedback. Furthermore, the sharing unit can also reflect the user's feedback to continuously improve the sharing method. In this way, the optimal sharing method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the user's feedback data into the generation AI and have the generation AI customize the sharing method.

[0072] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit selects training data that is concise and easy to understand. Furthermore, if the user is relaxed, the learning unit can select training data that includes detailed information. Furthermore, if the user is in a hurry, the learning unit can select training data that focuses on the main points. This allows for more appropriate learning by selecting training data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0073] During learning, the learning unit can adjust the learning algorithm by referring to past Q&A data. For example, the learning unit learns answers to similar questions based on past Q&A data. The learning unit can also extract frequently occurring questions and problems from past Q&A data and optimize the learning algorithm. Furthermore, the learning unit can analyze past Q&A data and adjust the learning algorithm to improve the accuracy of answers. In this way, the learning algorithm can be optimized by referring to past Q&A data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past Q&A data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0074] During learning, the learning unit can update the learning data by reflecting user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also improve answers to specific questions or problems based on user feedback. Furthermore, the learning unit can continuously update the learning data by reflecting user feedback. This allows the learning data to be continuously updated by reflecting user feedback. Some or all of the above-described processing in the learning unit may be performed, for example, using AI or without AI. For example, the learning unit can input user feedback data into a generation AI and cause the generation AI to update the learning data.

[0075] During learning, the learning unit can apply different learning algorithms depending on the category of question or problem. For example, the learning unit applies a specialized learning algorithm to technical questions. The learning unit can also apply a general-purpose learning algorithm to general questions. Furthermore, the learning unit can apply a learning algorithm specialized for a specific category to improve the accuracy of the answer. In this way, by applying different learning algorithms depending on the category of question or problem, the accuracy of the answer is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input category data of questions or problems into the generation AI and cause the generation AI to apply the optimal learning algorithm.

[0076] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can reduce the frequency of learning to reduce the burden. Furthermore, if the user is relaxed, the learning unit can increase the frequency of learning and provide detailed information. Furthermore, if the user is in a hurry, the learning unit can adjust the frequency of learning and provide quick answers. This allows for effective learning while reducing the burden by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the frequency of learning.

[0077] During learning, the learning unit can weight the learning data based on the time when the question or problem was submitted. For example, the learning unit weights the learning data for recently submitted questions or problems. The learning unit can also weight the learning data for questions or problems submitted during a specific time period. Furthermore, the learning unit can weight the learning data for questions or problems related to seasons or events. In this way, by weighting the learning data based on the time when the question or problem was submitted, more relevant answers can be provided. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time when the question or problem was submitted to the generation AI and have the generation AI perform weighting of the learning data.

[0078] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate information from social media to enrich the learning data. The learning unit can also integrate information from corporate databases to enrich the learning data. Furthermore, the learning unit can also integrate information from open data to enrich the learning data. This allows the learning data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the learning data.

[0079] During learning, the learning unit can adjust the learning algorithm according to the user's level of expertise. For example, the learning unit applies a basic learning algorithm to a novice user. The learning unit can also apply an applied learning algorithm to an intermediate user. Furthermore, the learning unit can also apply a specialized learning algorithm to an advanced user. This allows for adjusting the learning algorithm according to the user's level of expertise to provide more appropriate answers. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0080] The providing unit can estimate the user's emotions and adjust the way the answers and procedures are presented based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a concise and easy-to-understand way of presentation. Furthermore, if the user is relaxed, the providing unit can provide a way of presentation that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a way of presentation that focuses on the main points. This allows for adjusting the way the answers and procedures are presented according to the user's emotions, thereby providing more understandable information. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the answers and procedures are presented.

[0081] The providing unit can adjust the level of detail of the provided information based on the importance of the answer or procedure when providing the information. For example, the providing unit provides detailed information for answers or procedures with high importance. The providing unit can also provide concise information for answers or procedures with low importance. Furthermore, the providing unit can adjust the level of detail of the answers or procedures according to the user's needs. In this way, by adjusting the level of detail based on the importance of the answers or procedures, it is possible to provide information according to the user's needs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the answers or procedures to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0082] The providing unit can apply different providing algorithms depending on the category of the answer or procedure when providing the answer or procedure. For example, the providing unit can apply a specialized providing algorithm to technical answers or procedures. The providing unit can also apply a general-purpose providing algorithm to general answers or procedures. Furthermore, the providing unit can apply a providing algorithm specialized for a specific category to improve the accuracy of the answer or procedure. In this way, by applying a providing algorithm depending on the category of the answer or procedure, more accurate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of the answer or procedure into the generation AI and cause the generation AI to apply the optimal providing algorithm.

[0083] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the answer. The providing unit, for example, provides optimal answers or procedures based on the user's past provision results. The providing unit can also preferentially provide specific answers or procedures from the user's past provision results. Furthermore, the providing unit can continuously improve the accuracy of the provision by reflecting user feedback. In this way, the accuracy of the provision can be continuously improved by referring to the user's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0084] The providing unit can estimate the user's emotions and adjust the length of the answers and steps to provide based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide short, concise answers and steps with detailed explanations if the user is relaxed. Furthermore, if the user is in a hurry, the providing unit can provide concise, quickly understandable answers and steps. This allows for more appropriate information to be provided by adjusting the length of answers and steps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the answers and steps.

[0085] The providing unit can determine the priority of providing answers and procedures based on the time of submission of the answers and procedures when providing them. For example, the providing unit can prioritize providing answers and procedures that have been submitted recently. The providing unit can also prioritize providing answers and procedures submitted during a specific time period. Furthermore, the providing unit can prioritize providing answers and procedures related to a season or an event. This allows timely information to be provided by determining the priority based on the time of submission of the answers and procedures. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the time of submission of answers and procedures into the generation AI and have the generation AI determine the priority of providing.

[0086] The providing unit can adjust the order of providing answers and procedures based on the relevance of the answers and procedures when providing them. For example, the providing unit can prioritize providing answers and procedures that are most relevant to the user's current question or problem. The providing unit can also prioritize providing answers and procedures related to the user's past questions or problems. Furthermore, the providing unit can prioritize providing highly relevant answers and procedures based on the user's areas of interest. In this way, by adjusting the order of providing answers and procedures based on the relevance of the answers and procedures, it is possible to prioritize providing the most relevant information to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of answers and procedures to the generating AI and cause the generating AI to adjust the order of providing them.

[0087] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, the providing unit can provide answers and procedures in simple language, avoiding technical terminology, to a novice user. The providing unit can also provide answers and procedures using a moderate amount of technical terminology to an intermediate user. Furthermore, the providing unit can provide detailed answers and procedures using a lot of technical terminology to an advanced user. This allows for the provision of more easily understandable information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned sharing unit, learning unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the smart device 14, allowing users to post questions or problems and receive answers from other users. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing past Q&A data and generating answers to new questions. The providing unit is realized, for example, by the control unit 46A of the smart device 14, providing information and business procedures searched by users. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned sharing unit, learning unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the smart glasses 214, allowing users to post questions or problems and receive answers from other users. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing past Q&A data and generating answers to new questions. The providing unit is realized, for example, by the control unit 46A of the smart glasses 214, providing information and business procedures searched by users. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned sharing unit, learning unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the headset type terminal 314, and allows users to post questions or problems and receive answers from other users. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past Q&A data and generates answers to new questions. The providing unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides information and business procedures searched by users. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned sharing unit, learning unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sharing unit is realized by the control unit 46A of the robot 414, and allows users to post questions or problems and receive answers from other users. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past Q&A data and generates answers to new questions. The providing unit is realized, for example, by the control unit 46A of the robot 414, and provides information and business procedures searched by users.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide concise text-formatted information. If the user is relaxed, the providing unit can provide a detailed video guide. If the user is in a hurry, the providing unit can provide a concise infographic. This allows for more effective information provision by adjusting the format of information according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the format of the information.

[0090] The learning unit can estimate the user's emotions and adjust the presentation order of the learning data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can present important points first. Alternatively, if the user is relaxed, the learning unit can present the data in an order that includes detailed background information. Furthermore, if the user is in a hurry, the learning unit can present the data in a concise order that focuses on the main points. This allows for more effective learning by adjusting the presentation order of the learning data according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation order of the learning data.

[0091] The providing unit can estimate the user's emotions and adjust the level of detail of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise, to-the-point information. If the user is relaxed, the providing unit can provide information with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide information that can be understood in a short amount of time. This allows for more appropriate information to be provided by adjusting the level of detail of the information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the information.

[0092] The learning unit can estimate the user's emotions and adjust the learning progress speed based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can slow down the learning speed to allow the user to progress at an easy-to-understand pace. Alternatively, if the user is relaxed, the learning unit can speed up the learning speed to allow the user to progress efficiently. Furthermore, if the user is in a hurry, the learning unit can speed up the learning speed while focusing on the key points. This allows for more effective learning by adjusting the learning progress speed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the learning progress speed.

[0093] The providing unit can estimate the user's emotions and adjust the visual presentation of the information provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, visually easy-to-understand graphic. If the user is relaxed, the providing unit can provide a detailed illustration or infographic. Furthermore, if the user is in a hurry, the providing unit can provide a concise visual that emphasizes the main points. This allows for more effective information provision by adjusting the visual presentation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the visual presentation.

[0094] The providing unit can analyze the user's past search history and provide related information preferentially. For example, it can provide related answers or procedures based on keywords the user has searched for in the past. The providing unit can also provide information related to a specific category from the user's past search history preferentially. Furthermore, the providing unit can analyze the user's past search history and provide frequently searched information preferentially. This makes it possible to provide highly relevant information by analyzing the user's past search history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's search history data into a generation AI and cause the generation AI to provide related information.

[0095] The providing unit can monitor the user's current work status and provide information at an appropriate time. For example, it can provide information on the next related step immediately after the user completes a specific task. The providing unit can also provide an immediate solution if the user is experiencing difficulty during work. Furthermore, the providing unit can also provide necessary preparatory information before the user starts work. In this way, by monitoring the user's current work status, information can be provided at an appropriate time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's work status data into the generation AI and have the generation AI determine the timing of information provision.

[0096] The providing unit can provide region-specific information by taking into account the user's geographical location information. For example, if the user is in a specific region, it can provide business procedures and answers related to that region. The providing unit can also provide region-specific problems and solutions based on the user's current location. Furthermore, the providing unit can provide related information by taking into account the user's movement history. This makes it possible to provide region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's geographical location data into the generating AI and cause the generating AI to provide region-specific information.

[0097] The providing unit can analyze the user's social media activity and provide related information. For example, it can provide related answers or procedures based on content posted by the user on social media. The providing unit can also provide related information by referring to the activity of the user's friends on social media. Furthermore, the providing unit can also provide related information based on the user's check-in information on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to provide related information.

[0098] The providing unit can customize the format and content of the information to be provided by reflecting the user's past feedback. For example, the providing unit can suggest an optimal information format based on feedback provided by the user in the past. The providing unit can also prioritize providing specific information content based on the user's past feedback. Furthermore, the providing unit can also continuously improve the format and content of the information by reflecting the user's feedback. This makes it possible to provide optimal information by reflecting the user's past feedback. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to customize the format and content of the information.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The sharing section allows users to share their questions or problems within the user community and provide solutions. For example, users can post questions about error messages for specific software and get answers from other users. The sharing section also allows users to share their questions or problems in various formats, including text, images, and videos. Step 2: The learning unit learns from the data obtained from the sharing unit and provides answers to new questions. For example, the learning unit analyzes past Q&A data and generates answers to similar questions. The learning unit can use machine learning algorithms to learn based on the type of data and learning frequency. Step 3: The providing unit provides the answer obtained by the learning unit or specific business procedures or operation methods to the user. For example, the providing unit provides a guide on "how to create a pivot table in Excel" that the user searched for. The providing unit can provide information to the user based on the notification method, timing of provision, and format of provision.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 sharing section where users can share their questions or problems and provide solutions within the user community; a learning unit that learns from the data obtained from the sharing unit and provides answers to new questions; a providing unit that provides the answer obtained by the learning unit or a specific business procedure or operation method to the user. A system characterized by:

2. The learning unit Learn from past Q&A data and provide answers to new questions 2. The system of claim 1.

3. The providing unit Providing specific work procedures or operation methods to users 2. The system of claim 1.

4. The providing unit The answer obtained by the learning unit is provided to the user.

2. The system of claim 1.

5. The common part is Inferring user emotions and adjusting the timing of sharing questions or problems based on the estimated user emotions 2. The system of claim 1.

6. The common part is Analyze users' past posting history and select the appropriate sharing method 2. The system of claim 1.

7. The common part is When sharing a question or issue, filter based on your current project or area of ​​interest 2. The system of claim 1.

8. The common part is When sharing a question or problem, choose the appropriate sharing method based on the user's input method 2. The system of claim 1.

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