system

Abstract

The system according to the embodiment aims to provide a prompt and appropriate answer when a user asks a question. [Solution] A system according to an embodiment includes an analysis unit, an extraction unit, a reception unit, and a generation unit. The analysis unit analyzes the contents of a manual. The extraction unit extracts important information based on the contents analyzed by the analysis unit. The reception unit receives questions from users. The generation unit generates answers to the questions received by the reception unit.

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 have had the problem of making it difficult to quickly find necessary information from thick manuals, resulting in low user convenience.

[0005] The system according to the embodiment aims to provide a prompt and appropriate answer when a user asks a question. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an extraction unit, a reception unit, and a generation unit. The analysis unit analyzes the contents of the manual. The extraction unit extracts important information based on the contents analyzed by the analysis unit. The reception unit receives questions from users. The generation unit generates answers to the questions received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide an appropriate answer when a user asks a question. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A manual search system according to an embodiment of the present invention uses a generation AI to efficiently search through a thick manual and provide quick and accurate answers to user questions. This manual search system first trains the generation AI with the contents of the manual. The generation AI analyzes the entire contents of the manual and extracts important information. Next, when a user inputs a question, the generation AI generates an optimal answer for that question. This system eliminates the need for users to read through a thick manual and allows them to quickly obtain the information they need. The generation AI also constantly learns the latest information, allowing it to keep up with manual updates. Furthermore, this system is multilingual, learning manuals in different languages ​​and providing answers in the user's selected language. This allows users of different languages ​​to use the system equally. For example, when training the generation AI with the contents of the manual, the generation AI analyzes each chapter and section of the manual and extracts important keywords and phrases. This allows the generation AI to grasp the overall picture of the manual and generate the optimal answer to the user's question. Next, when a user asks, "How do I use a specific function?", the generation AI searches the relevant section of the manual and provides specific instructions. This eliminates the need for users to read thick manuals and allows them to quickly obtain the information they need. Furthermore, the generative AI constantly learns the latest information, allowing it to keep up with manual updates. For example, when new features are added, users can obtain the latest information by having the generative AI learn that information. This ensures that the system is always up-to-date. This system is also multilingual, allowing it to learn manuals in different languages ​​and provide answers in the language selected by the user. For example, by having the generative AI learn manuals in different languages, such as English, Japanese, and Chinese, users who speak different languages ​​can use the system in the same way. This allows it to accommodate a global user base. In this way, a manual search system using generative AI can significantly improve user convenience. Users no longer need to read thick manuals and can quickly obtain the information they need.In addition, the generation AI is constantly learning the latest information, so it can keep up with manual updates. Furthermore, it is multilingual, so users who speak different languages ​​can use the system in the same way. This allows the manual search system to provide quick and accurate answers to user questions.

[0029] A manual search system according to an embodiment includes an analysis unit, an extraction unit, a reception unit, and a generation unit. The analysis unit analyzes the contents of a manual. The analysis unit analyzes the entire contents of the manual using, for example, a generation AI. The generation AI can use a natural language generation model such as GPT-4 (registered trademark) or Gemini. The analysis unit analyzes, for example, each chapter or section of the manual and extracts important keywords and phrases. The extraction unit extracts important information based on the content analyzed by the analysis unit. The extraction unit extracts important information using, for example, a generation AI. Important information includes, for example, frequently occurring keywords and information related to a specific topic. The reception unit receives a user's question. The reception unit can receive the user's question in, for example, text format or audio format. The reception unit can also receive the user's question using, for example, a generation AI. The generation unit generates an answer to the question received by the reception unit. The generation unit generates an optimal answer to the user's question using, for example, a generation AI. The generation unit may use a natural language generation model such as GPT-4 or Gemini. For example, the generation unit searches for a relevant portion of a manual and explains specific usage methods to generate an optimal answer to a user's question. This allows the manual search system according to the embodiment to provide a quick and accurate answer to a user's question. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit may generate an answer using a generation AI model that receives a user's question as input and outputs an optimal answer. Furthermore, the generation unit may learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit may train the generation AI to learn that information, allowing the user to obtain the latest information. For example, the generation unit may use the generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. For example, the generation unit may train the generation AI on manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way.As a result, the manual search system according to the embodiment can significantly improve user convenience.

[0030] The generation unit can generate an optimal answer to a user's question using a generation AI. The generation unit generates an optimal answer to a user's question using, for example, a generation AI. The generation AI can use, for example, a natural language generation model such as GPT-4 or Gemini. To generate an optimal answer to a user's question, the generation unit searches for a relevant section of a manual and explains specific usage instructions. For example, when a user asks, "How do I use a specific function?", the generation unit causes the generation AI to search for the relevant section of the manual and explain specific usage instructions. This improves the accuracy of generating an optimal answer to a user's question by using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can generate an answer using a generation AI model that takes a user's question as input and outputs an optimal answer. Furthermore, the generation unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. The generation unit can, for example, use a generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. By having the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, the generation unit can enable users who speak different languages ​​to use the system in the same way. This allows the generation unit to significantly improve user convenience.

[0031] The analysis unit can analyze the contents of the manual using a generation AI. The analysis unit, for example, analyzes the entire contents of the manual using a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The analysis unit, for example, analyzes each chapter or section of the manual and extracts important keywords and phrases. This allows the generation AI to efficiently analyze the contents of the manual. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the contents of the manual and outputs analysis results. Furthermore, the analysis unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing the user to obtain the latest information. The analysis unit, for example, can learn manuals in different languages ​​using a generation AI and provide answers in the language selected by the user. The analysis unit can train the generation AI with manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the analysis unit to significantly improve user convenience.

[0032] The extraction unit can extract important information using a generation AI. The extraction unit extracts important information using, for example, a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. Important information can be, for example, frequently occurring keywords or information related to a specific topic. This allows for efficient extraction of important information using the generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs the content analyzed by the analysis unit and outputs important information. Furthermore, the extraction unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when new features are added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. For example, the extraction unit can use the generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. The extraction unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the extraction unit to significantly improve user convenience.

[0033] The reception unit can accept user questions. The reception unit can accept user questions in text format or audio format, for example. The reception unit can also accept user questions using, for example, a generation AI. User questions may include, for example, questions about how to use a specific function or troubleshooting. This allows for efficient acceptance of user questions. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can accept questions using a generation AI model that accepts questions, taking user questions as input. Furthermore, the reception unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. The reception unit can, for example, use the generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. The reception unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the reception unit to significantly improve user convenience.

[0034] The generation unit can learn updated information about the manual using a generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The generation unit can learn updated information about the manual using a generation AI and provide the latest information. This allows the generation AI to always provide the latest information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can provide information using a generation AI model that inputs updated information about the manual and outputs the latest information. Furthermore, the generation unit can learn manuals in different languages ​​using a generation AI and provide answers in a language selected by the user. For example, the generation unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the generation unit to significantly improve user convenience.

[0035] The generation unit can use a generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. For example, the generation unit can use a generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The generation unit can train the generation AI with manuals in different languages, such as English, Japanese, and Chinese, so that users who speak different languages ​​can use the system in the same way. This allows answers to be provided in multiple languages ​​using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input manuals in different languages ​​and provide answers using a generation AI model that extracts similarities and differences. Furthermore, the generation unit can learn updated information about the manuals using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. This significantly improves user convenience.

[0036] When analyzing the contents of the manual, the analysis unit can improve the accuracy of the analysis by referring to past user question histories. The analysis unit, for example, uses a generation AI to reference past user question histories. The generation AI can use, for example, a database format or a search algorithm. For example, the analysis unit can have the generation AI analyze past question histories and prioritize analysis of frequently asked questions. The analysis unit can also have the generation AI extract common issues from past question histories and improve the accuracy of the analysis based on the extracted common issues. Furthermore, the analysis unit can have the generation AI refer to past question histories and customize the analysis based on specific user trends. By referencing past question histories, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without the generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs past question histories and outputs analysis results. Furthermore, the analysis unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0037] When analyzing the contents of a manual, the analysis unit can apply different analysis algorithms depending on the structure and format of the manual. For example, the analysis unit can use a generation AI to apply different analysis algorithms depending on the structure and format of the manual. The generation AI can use, for example, text mining or natural language processing algorithms. For example, the generation AI can apply different analysis algorithms to each chapter to optimally analyze the content of each chapter. The analysis unit can also apply special analysis algorithms to sections containing tables and figures to effectively analyze visual information. Furthermore, the analysis unit can analyze bulleted or list-formatted sections by each list item to extract detailed information. This improves the accuracy of the analysis by applying analysis algorithms depending on the structure and format of the manual. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the structure and format of the manual and outputs analysis results. Furthermore, the analysis unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0038] When analyzing the contents of a manual, the analysis unit can simultaneously analyze manuals from different fields and extract similarities and differences. The analysis unit can, for example, use a generation AI to simultaneously analyze manuals from different fields. The generation AI can use technologies such as text mining and clustering. For example, the analysis unit can simultaneously analyze technical manuals and user guides to extract common operating procedures. The analysis unit can also simultaneously analyze product manuals and service manuals to clarify differences. Furthermore, the analysis unit can simultaneously analyze manuals for different products and extract common troubleshooting procedures. By analyzing manuals from different fields simultaneously, similarities and differences can be clarified. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs manuals from different fields and outputs similarities and differences. Furthermore, the analysis unit can learn updates to the manuals using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0039] When analyzing the contents of a manual, the analysis unit can improve the accuracy of the analysis by referencing related external databases. The analysis unit, for example, uses a generation AI to reference related external databases. The generation AI can use, for example, a patent database or an academic paper database. The analysis unit, for example, uses the generation AI to reference external technical databases and compare them with the contents of the manual to improve the accuracy of the analysis. The analysis unit can also have the generation AI refer to external user reviews and incorporate feedback on the contents of the manual into the analysis. Furthermore, the analysis unit can also have the generation AI refer to an external product specification database to complement the technical details of the manual. This improves the accuracy of the analysis by referencing related external databases. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs an external database and outputs analysis results. Furthermore, the analysis unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0040] When extracting important information, the extraction unit can improve the accuracy of the extraction by referring to past user question history. The extraction unit, for example, uses a generation AI to reference past user question history. The generation AI can use, for example, a database format or a search algorithm. For example, the extraction unit analyzes past question history and prioritizes extraction of frequently asked questions. The extraction unit can also improve the accuracy of the extraction by having the generation AI extract common issues from past question history. Furthermore, the extraction unit can also customize the extraction based on specific user trends by referring to past question history. By referring to past question history, the accuracy of the extraction can be improved. Some or all of the above-described processing in the extraction unit can be performed, for example, using the generation AI, or can be performed without the generation AI. For example, the extraction unit can extract information using a generation AI model that inputs past question history and outputs extraction results. Furthermore, the extraction unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0041] When extracting important information, the extraction unit can apply different extraction algorithms to each category or chapter of the manual. For example, the extraction unit can use a generation AI to apply different extraction algorithms to each category or chapter of the manual. The generation AI can use, for example, text mining or natural language processing algorithms. For example, the extraction unit can use a generation AI to apply different extraction algorithms to each chapter to optimally extract important information from each chapter. The extraction unit can also apply a special extraction algorithm to sections containing tables or figures to effectively extract visual information. Furthermore, the extraction unit can extract each list item from sections in bulleted or list format to provide detailed information. Applying different extraction algorithms to each category or chapter of the manual improves extraction accuracy. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI, or can be performed without a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs the manual's categories and chapters and outputs extraction results. Furthermore, the extraction unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0042] When extracting important information, the extraction unit can simultaneously analyze manuals in different languages ​​and extract similarities and differences. The extraction unit can, for example, use a generation AI to simultaneously analyze manuals in different languages. The generation AI can use technologies such as text mining and clustering. For example, the extraction unit can simultaneously analyze English and Japanese manuals using the generation AI to extract common operating procedures. The extraction unit can also simultaneously analyze Chinese and French manuals using the generation AI to clarify differences. Furthermore, the extraction unit can simultaneously analyze manuals in different languages ​​using the generation AI to extract common troubleshooting procedures. By analyzing manuals in different languages ​​simultaneously, similarities and differences can be clarified. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs manuals in different languages ​​and outputs similarities and differences. Furthermore, the extraction unit can learn updates to the manuals using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0043] When extracting important information, the extraction unit can improve the accuracy of the extraction by referencing related external databases. For example, the extraction unit uses a generation AI to reference related external databases. The generation AI can use, for example, a patent database or an academic paper database. For example, the extraction unit can improve the accuracy of the extraction by having the generation AI reference an external technical database and compare it with the contents of the manual. The extraction unit can also have the generation AI refer to external user reviews and incorporate feedback on the contents of the manual into the extraction. Furthermore, the extraction unit can have the generation AI reference an external product specification database to complement the technical details of the manual. This improves the accuracy of the extraction by referring to related external databases. Some or all of the above-mentioned processing in the extraction unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs an external database and outputs extraction results. Furthermore, the extraction unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0044] When accepting a question, the reception unit can select the optimal reception method by referring to the user's past question history. The reception unit, for example, uses a generation AI to reference the user's past question history. The generation AI can use, for example, a database format or a search algorithm. The reception unit, for example, analyzes the past question history and prioritizes frequently asked questions. The reception unit can also optimize the reception method based on common issues extracted from the past question history. Furthermore, the reception unit can customize the reception method based on the trends of a specific user by referring to the past question history. This improves the accuracy of the reception method by referring to the past question history. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit can select the reception method using a generation AI model that inputs the past question history and outputs the reception method. Furthermore, the reception unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0045] When accepting questions, the reception unit may filter questions based on the user's current situation and areas of interest. The reception unit may, for example, use a generation AI to identify the user's current situation and areas of interest. The generation AI may use, for example, survey results and past behavioral history. The reception unit may, for example, allow the generation AI to preferentially accept questions related to the user's current situation (e.g., time of day, location). The reception unit may also allow the generation AI to filter questions based on the user's areas of interest (e.g., specific products or services). Furthermore, the reception unit may allow the generation AI to refer to the user's past behavioral history and preferentially accept questions with high relevance. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be accepted. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit may filter questions using a generation AI model that inputs the user's current situation and areas of interest and outputs filtering results. Furthermore, the reception unit may learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0046] When accepting questions, the reception unit can prioritize highly relevant questions based on the user's geographical location information. The reception unit, for example, uses a generation AI to acquire the user's geographical location information. The generation AI can use, for example, GPS data or an IP address. The reception unit, for example, uses the generation AI to refer to the user's current location and prioritize questions related to the region. The reception unit can also prioritize highly relevant questions based on the user's past location information. Furthermore, the reception unit can prioritize providing information related to a specific region based on the user's location information. This allows highly relevant questions to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can accept questions using a generation AI model that inputs the user's geographical location information and outputs highly relevant questions. Furthermore, the reception unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0047] When accepting a question, the reception unit can analyze the user's social media activity and accept related questions. The reception unit can analyze the user's social media activity using, for example, a generation AI. The generation AI can use, for example, an analysis of post content and followers. The reception unit can, for example, analyze the user's social media activity using the generation AI and prioritize relevant questions based on the content of recent posts. The reception unit can also analyze the user's social media interests using the generation AI and filter questions based on the analysis. Furthermore, the reception unit can prioritize relevant questions by using the generation AI to refer to the posts of the user's followers and friends on social media. In this way, by analyzing the user's social media activity, it is possible to accept highly relevant questions. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can accept questions using a generation AI model that inputs the user's social media activity and outputs related questions. Furthermore, the reception unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0048] When generating an optimal answer to a question, the generation unit can improve the accuracy of the generation by referring to past user question history. The generation unit, for example, uses a generation AI to refer to past user question history. The generation AI can use, for example, a database format or a search algorithm. For example, the generation unit can analyze past question history and generate an answer based on frequently asked questions. The generation unit can also extract common issues from past question history and improve the accuracy of the answer based on that. Furthermore, the generation unit can refer to past question history and customize the answer based on the tendencies of a specific user. By referring to past question history, the accuracy of the answer can be improved. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs past question history and outputs an answer. Furthermore, the generation unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0049] When generating an optimal answer to a question, the generation unit can apply different generation algorithms depending on the question category and content. The generation unit, for example, uses a generation AI to apply different generation algorithms depending on the question category and content. The generation AI can use, for example, a text generation algorithm or template-based generation. For example, the generation unit applies an algorithm in which the generation AI generates answers containing detailed technical information for technical questions. The generation unit can also apply an algorithm in which the generation AI generates concise and easy-to-understand answers for general questions. Furthermore, the generation unit can also apply an algorithm in which the generation AI generates answers containing specific procedures for troubleshooting questions. In this way, applying a generation algorithm depending on the question category and content improves the accuracy of the answer. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs the question category and content and outputs an answer. Furthermore, the generation unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0050] When generating an optimal answer to a question, the generation unit can improve the accuracy of the generation by referring to a relevant external database. For example, the generation unit uses a generation AI to reference the relevant external database. The generation AI can use, for example, a patent database or an academic paper database. For example, the generation unit can have the generation AI reference an external technical database to complement the content of the answer. The generation unit can also have the generation AI reference external user reviews and incorporate feedback on the answer. Furthermore, the generation unit can have the generation AI reference an external product specification database to generate an answer that includes technical details. This improves the accuracy of the answer by referring to the relevant external database. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate an answer using a generation AI model that uses an external database as input and outputs an answer. Furthermore, the generation unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0051] When generating an optimal answer to a question, the generation unit can simultaneously analyze manuals in different languages ​​and extract similarities and differences. The generation unit, for example, uses a generation AI to simultaneously analyze manuals in different languages. The generation AI can use technologies such as text mining and clustering. For example, the generation unit can simultaneously analyze English and Japanese manuals and extract common operating procedures. The generation unit can also simultaneously analyze Chinese and French manuals and clarify differences. Furthermore, the generation unit can simultaneously analyze manuals in different languages ​​and extract common troubleshooting procedures. By analyzing manuals in different languages ​​simultaneously, similarities and differences can be clarified. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs manuals in different languages ​​and outputs similarities and differences. Furthermore, the generation unit can learn updates to the manuals using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When analyzing the contents of a manual, the analysis unit can improve the accuracy of the analysis by referring to the question history of past users. For example, the generation AI analyzes past question history and prioritizes the analysis of frequently asked questions. It can also extract common problems and improve the accuracy of the analysis based on them. Furthermore, it can customize the analysis based on the tendencies of specific users. This improves the accuracy of the analysis by referring to the question history of past users.

[0054] When extracting important information, the extraction unit can improve the accuracy of extraction by referring to past user question history. For example, the generation AI analyzes past question history and prioritizes the extraction of frequently asked questions. It can also extract common problems and improve the accuracy of extraction based on them. Furthermore, extraction can be customized based on the tendencies of specific users. This improves the accuracy of extraction by referring to past question history.

[0055] When accepting a question, the reception unit can refer to the user's past question history to select the optimal reception method. For example, the generation AI can analyze past question history and prioritize frequently asked questions. It can also extract common problems and optimize the reception method based on them. Furthermore, it can customize the reception method based on the tendencies of specific users. By referring to past question history, the accuracy of the reception method can be improved.

[0056] When generating the optimal answer to a question, the generator can improve the accuracy of the generation by referring to the user's past question history. For example, the generation AI can analyze the past question history and generate an answer based on frequently asked questions. It can also extract common problems and improve the accuracy of the answer based on them. Furthermore, it can customize answers based on the tendencies of specific users. In this way, the accuracy of the answer can be improved by referring to the past question history.

[0057] When generating the optimal answer to a question, the generation unit can improve the accuracy of the generation by referring to related external databases. For example, the generation AI can refer to an external technical database to complement the content of the answer. It can also refer to external user reviews and reflect feedback on the answer. It can also refer to an external product specification database to generate an answer that includes technical details. This improves the accuracy of the answer by referring to related external databases.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The analysis unit analyzes the contents of the manual. The analysis unit analyzes the entire contents of the manual, for example, using a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The analysis unit analyzes each chapter and section of the manual, for example, and extracts important keywords and phrases. Step 2: The extraction unit extracts important information based on the content analyzed by the analysis unit. For example, the extraction unit uses generative AI to extract important information. Important information can be, for example, frequently occurring keywords or information related to a specific topic. Step 3: The reception unit receives the user's question. The reception unit can receive the user's question in text format or voice format, for example. The reception unit can also receive the user's question using a generation AI, for example. Step 4: The generation unit generates an answer to the question received by the reception unit. The generation unit generates an optimal answer to the user's question using, for example, a generation AI. The generation unit can use, for example, a natural language generation model such as GPT-4 or Gemini. For example, to generate an optimal answer to the user's question, the generation unit searches for the relevant part of the manual and explains specific usage methods. Furthermore, the generation unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. For example, the generation unit can use the generation AI to learn manuals in different languages ​​and provide an answer in the language selected by the user. For example, the generation unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way.

[0060] (Example 2) A manual search system according to an embodiment of the present invention uses a generation AI to efficiently search through a thick manual and provide quick and accurate answers to user questions. This manual search system first trains the generation AI with the contents of the manual. The generation AI analyzes the entire contents of the manual and extracts important information. Next, when a user inputs a question, the generation AI generates an optimal answer for that question. This system eliminates the need for users to read through a thick manual and allows them to quickly obtain the information they need. The generation AI also constantly learns the latest information, allowing it to keep up with manual updates. Furthermore, this system is multilingual, learning manuals in different languages ​​and providing answers in the user's selected language. This allows users of different languages ​​to use the system equally. For example, when training the generation AI with the contents of the manual, the generation AI analyzes each chapter and section of the manual and extracts important keywords and phrases. This allows the generation AI to grasp the overall picture of the manual and generate the optimal answer to the user's question. Next, when a user asks, "How do I use a specific function?", the generation AI searches the relevant section of the manual and provides specific instructions. This eliminates the need for users to read thick manuals and allows them to quickly obtain the information they need. Furthermore, the generative AI constantly learns the latest information, allowing it to keep up with manual updates. For example, when new features are added, users can obtain the latest information by having the generative AI learn that information. This ensures that the system is always up-to-date. This system is also multilingual, allowing it to learn manuals in different languages ​​and provide answers in the language selected by the user. For example, by having the generative AI learn manuals in different languages, such as English, Japanese, and Chinese, users who speak different languages ​​can use the system in the same way. This allows it to accommodate a global user base. In this way, a manual search system using generative AI can significantly improve user convenience. Users no longer need to read thick manuals and can quickly obtain the information they need.In addition, the generation AI is constantly learning the latest information, so it can keep up with manual updates. Furthermore, it is multilingual, so users who speak different languages ​​can use the system in the same way. This allows the manual search system to provide quick and accurate answers to user questions.

[0061] A manual search system according to an embodiment includes an analysis unit, an extraction unit, a reception unit, and a generation unit. The analysis unit analyzes the contents of a manual. The analysis unit analyzes the entire contents of the manual using, for example, a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The analysis unit analyzes, for example, each chapter or section of the manual and extracts important keywords and phrases. The extraction unit extracts important information based on the content analyzed by the analysis unit. The extraction unit extracts important information using, for example, a generation AI. Important information includes, for example, frequently occurring keywords and information related to a specific topic. The reception unit receives user questions. The reception unit can receive user questions in, for example, text format or audio format. The reception unit can also receive user questions using, for example, a generation AI. The generation unit generates answers to the questions received by the reception unit. The generation unit generates optimal answers to the user questions using, for example, a generation AI. The generation unit may use a natural language generation model such as GPT-4 or Gemini. For example, the generation unit searches for a relevant portion of a manual and explains specific usage methods to generate an optimal answer to a user's question. This allows the manual search system according to the embodiment to provide a quick and accurate answer to a user's question. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI. For example, the generation unit may generate an answer using a generation AI model that receives a user's question as input and outputs an optimal answer. Furthermore, the generation unit may learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit may train the generation AI to learn that information, allowing the user to obtain the latest information. For example, the generation unit may use the generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. For example, the generation unit may train the generation AI on manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way.As a result, the manual search system according to the embodiment can significantly improve user convenience.

[0062] The generation unit can generate an optimal answer to a user's question using a generation AI. The generation unit generates an optimal answer to a user's question using, for example, a generation AI. The generation AI can use, for example, a natural language generation model such as GPT-4 or Gemini. To generate an optimal answer to a user's question, the generation unit searches for a relevant section of a manual and explains specific usage instructions. For example, when a user asks, "How do I use a specific function?", the generation unit causes the generation AI to search for the relevant section of the manual and explain specific usage instructions. This improves the accuracy of generating an optimal answer to a user's question by using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can generate an answer using a generation AI model that takes a user's question as input and outputs an optimal answer. Furthermore, the generation unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. The generation unit can, for example, use a generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. By having the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, the generation unit can enable users who speak different languages ​​to use the system in the same way. This allows the generation unit to significantly improve user convenience.

[0063] The analysis unit can analyze the contents of the manual using a generation AI. The analysis unit, for example, analyzes the entire contents of the manual using a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The analysis unit, for example, analyzes each chapter or section of the manual and extracts important keywords and phrases. This allows the generation AI to efficiently analyze the contents of the manual. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the contents of the manual and outputs analysis results. Furthermore, the analysis unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing the user to obtain the latest information. The analysis unit, for example, can learn manuals in different languages ​​using a generation AI and provide answers in the language selected by the user. The analysis unit can train the generation AI with manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the analysis unit to significantly improve user convenience.

[0064] The extraction unit can extract important information using a generation AI. The extraction unit extracts important information using, for example, a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. Important information can be, for example, frequently occurring keywords or information related to a specific topic. This allows for efficient extraction of important information using the generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs the content analyzed by the analysis unit and outputs important information. Furthermore, the extraction unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when new features are added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. For example, the extraction unit can use the generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. The extraction unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the extraction unit to significantly improve user convenience.

[0065] The reception unit can accept user questions. The reception unit can accept user questions in text format or audio format, for example. The reception unit can also accept user questions using, for example, a generation AI. User questions may include, for example, questions about how to use a specific function or troubleshooting. This allows for efficient acceptance of user questions. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can accept questions using a generation AI model that accepts questions, taking user questions as input. Furthermore, the reception unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. The reception unit can, for example, use the generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. The reception unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the reception unit to significantly improve user convenience.

[0066] The generation unit can learn updated information about the manual using a generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The generation unit can learn updated information about the manual using a generation AI and provide the latest information. This allows the generation AI to always provide the latest information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can provide information using a generation AI model that inputs updated information about the manual and outputs the latest information. Furthermore, the generation unit can learn manuals in different languages ​​using a generation AI and provide answers in a language selected by the user. For example, the generation unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way. This allows the generation unit to significantly improve user convenience.

[0067] The generation unit can use a generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. For example, the generation unit can use a generation AI to learn manuals in different languages ​​and provide answers in the language selected by the user. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The generation unit can train the generation AI with manuals in different languages, such as English, Japanese, and Chinese, so that users who speak different languages ​​can use the system in the same way. This allows answers to be provided in multiple languages ​​using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input manuals in different languages ​​and provide answers using a generation AI model that extracts similarities and differences. Furthermore, the generation unit can learn updated information about the manuals using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. This significantly improves user convenience.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the generation AI can prioritize analysis of important information and provide it quickly. Furthermore, when the user is relaxed, the analysis unit can perform analysis including detailed information and provide a comprehensive answer. Furthermore, when the user is in a hurry, the analysis unit can prioritize analysis of the most relevant information and generate an answer quickly. This allows for more appropriate information to be provided by adjusting the analysis priority according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the user's emotions and outputs analysis priorities. Furthermore, the analysis unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0069] When analyzing the contents of the manual, the analysis unit can improve the accuracy of the analysis by referring to past user question histories. The analysis unit, for example, uses a generation AI to reference past user question histories. The generation AI can use, for example, a database format or a search algorithm. For example, the analysis unit can have the generation AI analyze past question histories and prioritize analysis of frequently asked questions. The analysis unit can also have the generation AI extract common issues from past question histories and improve the accuracy of the analysis based on the extracted common issues. Furthermore, the analysis unit can have the generation AI refer to past question histories and customize the analysis based on specific user trends. By referencing past question histories, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without the generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs past question histories and outputs analysis results. Furthermore, the analysis unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0070] When analyzing the contents of a manual, the analysis unit can apply different analysis algorithms depending on the structure and format of the manual. For example, the analysis unit can use a generation AI to apply different analysis algorithms depending on the structure and format of the manual. The generation AI can use, for example, text mining or natural language processing algorithms. For example, the generation AI can apply different analysis algorithms to each chapter to optimally analyze the content of each chapter. The analysis unit can also apply special analysis algorithms to sections containing tables and figures to effectively analyze visual information. Furthermore, the analysis unit can analyze bulleted or list-formatted sections by each list item to extract detailed information. This improves the accuracy of the analysis by applying analysis algorithms depending on the structure and format of the manual. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the structure and format of the manual and outputs analysis results. Furthermore, the analysis unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate information to be provided by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without a generation AI. For example, the analysis unit can adjust the display method using a generation AI model that inputs the user's emotions and outputs a display method. Furthermore, the analysis unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0072] When analyzing the contents of a manual, the analysis unit can simultaneously analyze manuals from different fields and extract similarities and differences. The analysis unit can, for example, use a generation AI to simultaneously analyze manuals from different fields. The generation AI can use technologies such as text mining and clustering. For example, the analysis unit can simultaneously analyze technical manuals and user guides to extract common operating procedures. The analysis unit can also simultaneously analyze product manuals and service manuals to clarify differences. Furthermore, the analysis unit can simultaneously analyze manuals for different products and extract common troubleshooting procedures. By analyzing manuals from different fields simultaneously, similarities and differences can be clarified. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs manuals from different fields and outputs similarities and differences. Furthermore, the analysis unit can learn updates to the manuals using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0073] When analyzing the contents of a manual, the analysis unit can improve the accuracy of the analysis by referencing related external databases. The analysis unit, for example, uses a generation AI to reference related external databases. The generation AI can use, for example, a patent database or an academic paper database. The analysis unit, for example, uses the generation AI to reference external technical databases and compare them with the contents of the manual to improve the accuracy of the analysis. The analysis unit can also have the generation AI refer to external user reviews and incorporate feedback on the contents of the manual into the analysis. Furthermore, the analysis unit can also have the generation AI refer to an external product specification database to complement the technical details of the manual. This improves the accuracy of the analysis by referencing related external databases. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs an external database and outputs analysis results. Furthermore, the analysis unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the analysis unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the analysis unit to significantly improve user convenience.

[0074] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. The extraction unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is stressed, the extraction unit can prioritize the extraction of important information and provide it quickly. Furthermore, when the user is relaxed, the extraction unit can prioritize the extraction of detailed information and provide a comprehensive answer. Furthermore, when the user is in a hurry, the extraction unit can prioritize the extraction of the most relevant information and generate an answer quickly. This allows for more appropriate information to be provided by determining the priority of information according to the user's emotions. Some or all of the above-described processing in the extraction unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs the user's emotions and outputs the priority of information. Furthermore, the extraction unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0075] When extracting important information, the extraction unit can improve the accuracy of the extraction by referring to past user question history. The extraction unit, for example, uses a generation AI to reference past user question history. The generation AI can use, for example, a database format or a search algorithm. For example, the extraction unit analyzes past question history and prioritizes extraction of frequently asked questions. The extraction unit can also improve the accuracy of the extraction by having the generation AI extract common issues from past question history. Furthermore, the extraction unit can also customize the extraction based on specific user trends by referring to past question history. By referring to past question history, the accuracy of the extraction can be improved. Some or all of the above-described processing in the extraction unit can be performed, for example, using the generation AI, or can be performed without the generation AI. For example, the extraction unit can extract information using a generation AI model that inputs past question history and outputs extraction results. Furthermore, the extraction unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0076] When extracting important information, the extraction unit can apply different extraction algorithms to each category or chapter of the manual. For example, the extraction unit can use a generation AI to apply different extraction algorithms to each category or chapter of the manual. The generation AI can use, for example, text mining or natural language processing algorithms. For example, the extraction unit can use a generation AI to apply different extraction algorithms to each chapter to optimally extract important information from each chapter. The extraction unit can also apply a special extraction algorithm to sections containing tables or figures to effectively extract visual information. Furthermore, the extraction unit can extract each list item from sections in bulleted or list format to provide detailed information. Applying different extraction algorithms to each category or chapter of the manual improves extraction accuracy. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI, or can be performed without a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs the manual's categories and chapters and outputs extraction results. Furthermore, the extraction unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0077] The extraction unit can estimate the user's emotions and adjust the display method of the extraction results based on the estimated user emotions. The extraction unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the extraction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a display method that focuses on the main points. This allows for more appropriate information to be provided by adjusting the display method of the extraction results according to the user's emotions. Some or all of the above-described processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can adjust the display method using a generation AI model that inputs the user's emotions and outputs a display method. Furthermore, the extraction unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0078] When extracting important information, the extraction unit can simultaneously analyze manuals in different languages ​​and extract similarities and differences. The extraction unit can, for example, use a generation AI to simultaneously analyze manuals in different languages. The generation AI can use technologies such as text mining and clustering. For example, the extraction unit can simultaneously analyze English and Japanese manuals using the generation AI to extract common operating procedures. The extraction unit can also simultaneously analyze Chinese and French manuals using the generation AI to clarify differences. Furthermore, the extraction unit can simultaneously analyze manuals in different languages ​​using the generation AI to extract common troubleshooting procedures. By analyzing manuals in different languages ​​simultaneously, similarities and differences can be clarified. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs manuals in different languages ​​and outputs similarities and differences. Furthermore, the extraction unit can learn updates to the manuals using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0079] When extracting important information, the extraction unit can improve the accuracy of the extraction by referencing related external databases. For example, the extraction unit uses a generation AI to reference related external databases. The generation AI can use, for example, a patent database or an academic paper database. For example, the extraction unit can improve the accuracy of the extraction by having the generation AI reference an external technical database and compare it with the contents of the manual. The extraction unit can also have the generation AI refer to external user reviews and incorporate feedback on the contents of the manual into the extraction. Furthermore, the extraction unit can have the generation AI reference an external product specification database to complement the technical details of the manual. This improves the accuracy of the extraction by referring to related external databases. Some or all of the above-mentioned processing in the extraction unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the extraction unit can extract information using a generation AI model that inputs an external database and outputs extraction results. Furthermore, the extraction unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when a new function is added, the extraction unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the extraction unit to significantly improve user convenience.

[0080] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated user emotions. The reception unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept questions. This allows for more appropriate question reception by adjusting the question reception method according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can adjust the reception method using a generation AI model that inputs the user's emotions and outputs a reception method. Furthermore, the reception unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0081] When accepting a question, the reception unit can select the optimal reception method by referring to the user's past question history. The reception unit, for example, uses a generation AI to reference the user's past question history. The generation AI can use, for example, a database format or a search algorithm. The reception unit, for example, analyzes the past question history and prioritizes frequently asked questions. The reception unit can also optimize the reception method based on common issues extracted from the past question history. Furthermore, the reception unit can customize the reception method based on the trends of a specific user by referring to the past question history. This improves the accuracy of the reception method by referring to the past question history. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit can select the reception method using a generation AI model that inputs the past question history and outputs the reception method. Furthermore, the reception unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0082] When accepting questions, the reception unit may filter questions based on the user's current situation and areas of interest. The reception unit may, for example, use a generation AI to identify the user's current situation and areas of interest. The generation AI may use, for example, survey results and past behavioral history. The reception unit may, for example, allow the generation AI to preferentially accept questions related to the user's current situation (e.g., time of day, location). The reception unit may also allow the generation AI to filter questions based on the user's areas of interest (e.g., specific products or services). Furthermore, the reception unit may allow the generation AI to refer to the user's past behavioral history and preferentially accept questions with high relevance. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be accepted. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit may filter questions using a generation AI model that inputs the user's current situation and areas of interest and outputs filtering results. Furthermore, the reception unit may learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0083] The reception unit can estimate the user's emotions and prioritize questions based on the estimated user emotions. The reception unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, when the user is nervous, the reception unit can cause the generation AI to prioritize urgent questions. Furthermore, when the user is relaxed, the reception unit can cause the generation AI to prioritize detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize questions that require a quick answer. This allows questions to be prioritized based on the user's emotions, enabling more appropriate questions to be prioritized. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can prioritize questions using a generation AI model that inputs the user's emotions and outputs a priority order for the questions. Furthermore, the reception unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0084] When accepting questions, the reception unit can prioritize highly relevant questions based on the user's geographical location information. The reception unit, for example, uses a generation AI to acquire the user's geographical location information. The generation AI can use, for example, GPS data or an IP address. The reception unit, for example, uses the generation AI to refer to the user's current location and prioritize questions related to the region. The reception unit can also prioritize highly relevant questions based on the user's past location information. Furthermore, the reception unit can prioritize providing information related to a specific region based on the user's location information. This allows highly relevant questions to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can accept questions using a generation AI model that inputs the user's geographical location information and outputs highly relevant questions. Furthermore, the reception unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0085] When accepting a question, the reception unit can analyze the user's social media activity and accept related questions. The reception unit can analyze the user's social media activity using, for example, a generation AI. The generation AI can use, for example, an analysis of post content and followers. The reception unit can, for example, analyze the user's social media activity using the generation AI and prioritize relevant questions based on the content of recent posts. The reception unit can also analyze the user's social media interests using the generation AI and filter questions based on the analysis. Furthermore, the reception unit can prioritize relevant questions by using the generation AI to refer to the posts of the user's followers and friends on social media. In this way, by analyzing the user's social media activity, it is possible to accept highly relevant questions. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI. For example, the reception unit can accept questions using a generation AI model that inputs the user's social media activity and outputs related questions. Furthermore, the reception unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the reception unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the reception unit to significantly improve user convenience.

[0086] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the generation AI can generate an answer in a calm tone. Furthermore, if the user is relaxed, the generation AI can generate an answer in a friendly tone. Furthermore, if the user is in a hurry, the generation AI can generate a concise and quick answer. This allows for providing a more appropriate answer by adjusting the way the answer is expressed based on the user's emotions. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs the user's emotions and outputs the way the answer is expressed. Furthermore, the generation unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0087] When generating an optimal answer to a question, the generation unit can improve the accuracy of the generation by referring to past user question history. The generation unit, for example, uses a generation AI to refer to past user question history. The generation AI can use, for example, a database format or a search algorithm. For example, the generation unit can analyze past question history and generate an answer based on frequently asked questions. The generation unit can also extract common issues from past question history and improve the accuracy of the answer based on that. Furthermore, the generation unit can refer to past question history and customize the answer based on the tendencies of a specific user. By referring to past question history, the accuracy of the answer can be improved. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs past question history and outputs an answer. Furthermore, the generation unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0088] When generating an optimal answer to a question, the generation unit can apply different generation algorithms depending on the question category and content. The generation unit, for example, uses a generation AI to apply different generation algorithms depending on the question category and content. The generation AI can use, for example, a text generation algorithm or template-based generation. For example, the generation unit applies an algorithm in which the generation AI generates answers containing detailed technical information for technical questions. The generation unit can also apply an algorithm in which the generation AI generates concise and easy-to-understand answers for general questions. Furthermore, the generation unit can also apply an algorithm in which the generation AI generates answers containing specific procedures for troubleshooting questions. In this way, applying a generation algorithm depending on the question category and content improves the accuracy of the answer. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs the question category and content and outputs an answer. Furthermore, the generation unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0089] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions. The generation AI can use technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the generation AI can generate a short, to-the-point answer. Furthermore, if the user is relaxed, the generation AI can generate a longer answer with detailed explanations. Furthermore, if the user is excited, the generation AI can generate an answer with visually stimulating effects. This allows for adjusting the length of the answer according to the user's emotions, thereby providing a more appropriate answer. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs the user's emotions and outputs the length of the answer. Furthermore, the generation unit can learn updates to the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0090] When generating an optimal answer to a question, the generation unit can improve the accuracy of the generation by referring to a relevant external database. For example, the generation unit uses a generation AI to reference the relevant external database. The generation AI can use, for example, a patent database or an academic paper database. For example, the generation unit can have the generation AI reference an external technical database to complement the content of the answer. The generation unit can also have the generation AI reference external user reviews and incorporate feedback on the answer. Furthermore, the generation unit can have the generation AI reference an external product specification database to generate an answer that includes technical details. This improves the accuracy of the answer by referring to the relevant external database. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate an answer using a generation AI model that uses an external database as input and outputs an answer. Furthermore, the generation unit can learn updated information from the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience.

[0091] When generating an optimal answer to a question, the generation unit can simultaneously analyze manuals in different languages ​​and extract similarities and differences. The generation unit, for example, uses a generation AI to simultaneously analyze manuals in different languages. The generation AI can use technologies such as text mining and clustering. For example, the generation unit can simultaneously analyze English and Japanese manuals and extract common operating procedures. The generation unit can also simultaneously analyze Chinese and French manuals and clarify differences. Furthermore, the generation unit can simultaneously analyze manuals in different languages ​​and extract common troubleshooting procedures. By analyzing manuals in different languages ​​simultaneously, similarities and differences can be clarified. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the generation unit can generate an answer using a generation AI model that inputs manuals in different languages ​​and outputs similarities and differences. Furthermore, the generation unit can learn updates to the manuals using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing users to obtain the latest information. This allows the generation unit to significantly improve user convenience. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, reception unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the contents of the manual using a generation AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information based on the analyzed contents. The reception unit is realized by the reception device 38 of the smart device 14 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the user's question. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, reception unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the contents of the manual using a generation AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information based on the analyzed contents. The reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the user's question. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, reception unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the contents of the manual using a generation AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information based on the analyzed contents. The reception unit is realized by the microphone 238 of the headset type terminal 314 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the user's question. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, reception unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the contents of the manual using a generation AI. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts important information based on the analyzed contents. The reception unit is realized by the microphone 238 of the robot 414 and receives a user's question. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal answer to the user's question.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of important information and provide it quickly. Also, if the user is relaxed, the analysis unit can perform analysis including detailed information and provide a comprehensive answer. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of the most relevant information and generate an answer quickly. In this way, by adjusting the analysis priority according to the user's emotions, more appropriate information can be provided.

[0094] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate an answer in a calm tone. If the user is relaxed, the generation unit can also generate an answer in a friendly tone. Furthermore, if the user is in a hurry, the generation unit can also generate a concise and quick answer. In this way, by adjusting the way the answer is expressed according to the user's emotions, it is possible to provide a more appropriate answer.

[0095] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly receive questions. In this way, by adjusting the method of receiving questions according to the user's emotions, more appropriate questions can be received.

[0096] The extraction unit can estimate the user's emotions and determine the priority of information to be extracted based on the estimated user emotions. For example, if the user is feeling stressed, important information can be preferentially extracted and quickly provided. Also, if the user is relaxed, detailed information can be extracted and a comprehensive answer can be provided. Furthermore, if the user is in a hurry, the most relevant information can be preferentially extracted and an answer can be quickly generated. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be provided.

[0097] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is in a hurry, a short, to-the-point answer can be generated. If the user is relaxed, a longer answer including detailed explanations can be generated. Furthermore, if the user is excited, an answer with visually stimulating effects can be generated. In this way, by adjusting the length of the answer according to the user's emotions, more appropriate answers can be provided.

[0098] When analyzing the contents of a manual, the analysis unit can improve the accuracy of the analysis by referring to the question history of past users. For example, the generation AI analyzes past question history and prioritizes the analysis of frequently asked questions. It can also extract common problems and improve the accuracy of the analysis based on them. Furthermore, it can customize the analysis based on the tendencies of specific users. This improves the accuracy of the analysis by referring to the question history of past users.

[0099] When extracting important information, the extraction unit can improve the accuracy of extraction by referring to past user question history. For example, the generation AI analyzes past question history and prioritizes the extraction of frequently asked questions. It can also extract common problems and improve the accuracy of extraction based on them. Furthermore, extraction can be customized based on the tendencies of specific users. This improves the accuracy of extraction by referring to past question history.

[0100] When accepting a question, the reception unit can refer to the user's past question history to select the optimal reception method. For example, the generation AI can analyze past question history and prioritize frequently asked questions. It can also extract common problems and optimize the reception method based on them. Furthermore, it can customize the reception method based on the tendencies of specific users. By referring to past question history, the accuracy of the reception method can be improved.

[0101] When generating the optimal answer to a question, the generator can improve the accuracy of the generation by referring to the user's past question history. For example, the generation AI can analyze the past question history and generate an answer based on frequently asked questions. It can also extract common problems and improve the accuracy of the answer based on them. Furthermore, it can customize answers based on the tendencies of specific users. In this way, the accuracy of the answer can be improved by referring to the past question history.

[0102] When generating the optimal answer to a question, the generation unit can improve the accuracy of the generation by referring to related external databases. For example, the generation AI can refer to an external technical database to complement the content of the answer. It can also refer to external user reviews and reflect feedback on the answer. It can also refer to an external product specification database to generate an answer that includes technical details. This improves the accuracy of the answer by referring to related external databases.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The analysis unit analyzes the contents of the manual. The analysis unit analyzes the entire contents of the manual, for example, using a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The analysis unit analyzes each chapter and section of the manual, for example, and extracts important keywords and phrases. Step 2: The extraction unit extracts important information based on the content analyzed by the analysis unit. For example, the extraction unit uses generative AI to extract important information. Important information can be, for example, frequently occurring keywords or information related to a specific topic. Step 3: The reception unit receives the user's question. The reception unit can receive the user's question in text format or voice format, for example. The reception unit can also receive the user's question using a generation AI, for example. Step 4: The generation unit generates an answer to the question received by the reception unit. The generation unit generates an optimal answer to the user's question using, for example, a generation AI. The generation unit can use, for example, a natural language generation model such as GPT-4 or Gemini. For example, to generate an optimal answer to the user's question, the generation unit searches for the relevant part of the manual and explains specific usage methods. Furthermore, the generation unit can learn updated information about the manual using the generation AI and provide the latest information. For example, when a new function is added, the generation unit can have the generation AI learn that information, allowing the user to obtain the latest information. For example, the generation unit can use the generation AI to learn manuals in different languages ​​and provide an answer in the language selected by the user. For example, the generation unit can have the generation AI learn manuals in different languages, such as English, Japanese, and Chinese, allowing users who speak different languages ​​to use the system in the same way.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. an analysis unit that analyzes the contents of the manual; an extraction unit that extracts important information based on the content analyzed by the analysis unit; a reception unit that receives questions from users; a generation unit that generates an answer to the question received by the reception unit; Equipped with A system characterized by:

2. The generation unit Generative AI generates optimal answers to user questions The system of claim 1 .

3. The analysis unit Analyzing the contents of the manual using generative AI The system of claim 1 .

4. The extraction unit Extracting important information with generative AI The system of claim 1 .

5. The reception unit Accepting user questions The system of claim 1 .

6. The generation unit Generative AI learns about updated manual information and provides the latest information The system of claim 1 .

7. The generation unit Generative AI learns manuals in different languages ​​and provides answers in the language selected by the user. The system of claim 1 .

8. The analysis unit Estimate user emotions and adjust analysis priorities based on the estimated user emotions The system of claim 1 .

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