system

Abstract

The system according to the embodiment aims to identify relevant manuals using keywords or voice and automatically paste them onto a dedicated sheet. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a storage unit, and an attachment unit. The reception unit receives keyword or voice input. The analysis unit analyzes the information received by the reception unit and identifies the relevant manual name. The storage unit temporarily stores the manual name identified by the analysis unit. The attachment unit attaches the manual name stored in the storage unit to a dedicated sheet.

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 technology was unable to identify relevant manuals using keywords or voice and automatically paste them onto dedicated sheets, requiring manual work, which was inefficient.

[0005] The system according to the embodiment aims to identify relevant manuals using keywords or voice and automatically paste them onto a dedicated sheet. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a storage unit, and an attachment unit. The reception unit receives keyword or voice input. The analysis unit analyzes the information received by the reception unit and identifies the relevant manual name. The storage unit temporarily stores the manual name identified by the analysis unit. The attachment unit attaches the manual name stored in the storage unit to a dedicated sheet. [Effects of the Invention]

[0007] The system according to the embodiment can identify relevant manuals using keywords or voice and automatically paste them onto a dedicated sheet. [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) The AI ​​service according to an embodiment of the present invention is a system that allows anyone to easily enter keywords and automatically paste various manual titles onto a dedicated sheet. This AI service supports not only keyword input but also voice input, making it easy to use even for workers who are busy. Specifically, it consists of the following steps: First, the user inputs keywords or issues voice instructions. Next, the AI ​​analyzes the input keywords or voice instructions and searches for related manual titles. The generated manual titles are automatically pasted onto the dedicated sheet. This eliminates the need for users to manually enter manual titles. Furthermore, voice input is supported, making the service easy to use even for workers who are busy. This service allows users to easily find the necessary manuals and improves work efficiency. For example, if a user enters "operation manual," the AI ​​searches for related operation manuals and automatically pastes them onto the dedicated sheet. Alternatively, if a user instructs the AI ​​to "search for operation manuals by voice," the AI ​​analyzes the voice instruction, similarly searches for operation manuals, and pastes them onto the dedicated sheet. This eliminates the need for users to manually enter manual titles. Furthermore, voice input is supported, making the service easy to use even for workers who are busy. This service allows users to easily find the manuals they need, improving work efficiency. The AI ​​service analyzes information entered by users using keywords or voice, and automatically pastes the names of related manuals into a dedicated sheet.

[0029] The AI ​​service according to the embodiment includes a reception unit, an analysis unit, a storage unit, and an attachment unit. The reception unit receives keyword or voice input. For example, when a user inputs a keyword, the input is in text format. When a user inputs voice, the input is in voice format. The analysis unit analyzes the information received by the reception unit and identifies the name of a related manual. The analysis unit performs the analysis using, for example, natural language processing technology or voice recognition technology. The attachment unit temporarily stores the manual name identified by the analysis unit. The storage unit can store the manual name in memory, for example, and delete it after a certain period of time. The attachment unit attaches the manual name stored in the storage unit to a dedicated sheet. The dedicated sheet includes, for example, an Excel spreadsheet or a specific application. As a result, the AI ​​service according to the embodiment can analyze information input by a user using keywords or voice, and automatically attach the name of a related manual to the dedicated sheet.

[0030] The reception unit can accommodate both keyword input and voice input. For example, the reception unit provides an interface that accommodates both keyword input and voice input. For example, a text box for the user to enter keywords and a microphone icon for voice input are provided. In the case of voice input, voice recognition technology is used to convert the voice into text. This allows the user to use both keyword input and voice input.

[0031] The analysis unit can perform analysis based on a pre-trained database. The analysis unit performs analysis based on, for example, a pre-trained database. The pre-trained database includes, for example, a data set and a learning algorithm to be used. This allows the analysis unit to improve the accuracy of analysis based on the pre-trained database.

[0032] The pasting unit can automatically paste the manual name into an Excel spreadsheet or a dedicated application. The pasting unit automatically pastes the manual name into, for example, an Excel spreadsheet or a dedicated application. In an Excel spreadsheet, the manual name can be pasted into, for example, a specific cell. In a dedicated application, the manual name can be pasted into, for example, a specific field. This makes it possible to automatically paste the manual name into an Excel spreadsheet or a dedicated application.

[0033] The analysis unit can use AI to analyze input keywords and voice instructions and search for related manual names. The analysis unit can, for example, use AI to analyze input keywords and voice instructions and search for related manual names. AI technology includes, for example, machine learning algorithms and deep learning models. This allows the analysis unit to use AI to efficiently search for related manual names.

[0034] The storage unit temporarily stores the analysis results and can efficiently manage them. The storage unit, for example, temporarily stores the analysis results and can efficiently manage them. The storage unit can, for example, store the analysis results in memory and delete them after a certain period of time. This allows the analysis results to be efficiently managed.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, keywords that the user has frequently used in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest keywords that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the past input history. The analysis of the past input history is performed using, for example, data mining technology or statistical analysis. This allows the reception unit to analyze the user's past input history and suggest the optimal input method.

[0036] The reception unit can adjust the priority of input based on the user's current work situation when inputting data. The reception unit, for example, adjusts the priority of input based on the user's current work situation when inputting data. For example, if the user is busy working, voice input can be prioritized. Also, if the user is doing desk work, keyword input can be prioritized. Furthermore, if the user is on the move, simple voice commands can be prioritized. This makes it possible to provide an optimal input method depending on the user's work situation. The current work situation is identified, for example, by analyzing a work log or using real-time monitoring. This allows the reception unit to adjust the priority of input based on the user's current work situation when inputting data.

[0037] The reception unit can prioritize presenting highly relevant input methods based on the user's geographical location information at the time of input. For example, the reception unit prioritizes presenting highly relevant input methods taking into account the user's geographical location information at the time of input. For example, when the user is in the office, keyword input can be prioritized. Also, when the user is out, voice input can be prioritized. Furthermore, when the user is at home, detailed input options can be provided. This makes it possible to provide the optimal input method based on the user's geographical location information. The geographical location information is identified using, for example, GPS data or a location information service. This makes it possible for the reception unit to prioritize presenting highly relevant input methods taking into account the user's geographical location information at the time of input.

[0038] The reception unit can analyze the user's social media activity at the time of input and suggest a related input method. The reception unit, for example, analyzes the user's social media activity at the time of input and suggests a related input method. For example, keywords frequently used by the user on social media can be automatically displayed as candidates. The reception unit can also suggest the optimal input method (voice, text, etc.) based on the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest the name of a related manual. This makes it possible to provide the optimal input method based on the user's social media activity. Analysis of social media activity is performed, for example, using an analysis of the content of posts and the trends of followers. This makes it possible for the reception unit to analyze the user's social media activity at the time of input and suggest a related input method.

[0039] The analysis unit can improve the analysis accuracy by taking into account the input keywords and the context of the voice during analysis. The analysis unit can improve the analysis accuracy by taking into account, for example, the input keywords and the context of the voice during analysis. For example, the analysis unit can analyze the context before and after the keywords to identify a highly relevant manual name. It can also analyze the intonation and emphasis of the voice instructions to identify an appropriate manual name. It can also resolve ambiguous instructions by taking into account the keywords and the context of the voice. In this way, the analysis accuracy is improved by taking into account the keywords and the context of the voice. The context can be taken into account by, for example, analyzing the context before and after the keywords and extracting related topics. In this way, the analysis unit can improve the analysis accuracy by taking into account the input keywords and the context of the voice during analysis.

[0040] The analysis unit can optimize the analysis results by referring to the user's past search history during analysis. The analysis unit, for example, can optimize the analysis results by referring to the user's past search history during analysis. For example, the analysis unit can suggest highly relevant manual names based on manual names that the user has searched for in the past. It can also prioritize analysis of frequently used keywords from the user's past search history. It can also analyze the user's past search history and provide optimal analysis results. This makes it possible to optimize the analysis results by referring to the past search history. The past search history is referred to using, for example, a method for saving search logs or a method for analyzing history data. This makes it possible for the analysis unit to optimize the analysis results by referring to the user's past search history during analysis.

[0041] The analysis unit can determine the analysis priority based on the submission time of the input keywords and voice during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the input keywords and voice during analysis. For example, the analysis unit gives priority to the most recent keywords and voice instructions. It can also postpone the analysis of keywords and voice instructions that were submitted earlier. Furthermore, the analysis priority can also be dynamically adjusted based on the submission time. This makes it possible to dynamically adjust the analysis priority based on the submission time. The submission time is identified using, for example, a method for recording the submission date and time or a priority algorithm. This makes it possible for the analysis unit to determine the analysis priority based on the submission time of the input keywords and voice during analysis.

[0042] The analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis. The analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis, for example. For example, the analysis unit can refer to an external technical database to identify the name of a related manual. The analysis unit can also improve the accuracy of the analysis results by referring to an external industry standard database. Furthermore, the analysis can be performed based on the latest information by referring to an external academic database. In this way, the analysis accuracy is improved by referring to an external database. The external database is referred to using, for example, the API usage method or the type of database. In this way, the analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis.

[0043] The storage unit can optimize the storage algorithm by referring to past stored data when saving. The storage unit, for example, optimizes the storage algorithm by referring to past stored data when saving. For example, the storage unit selects an optimal storage format based on past stored data. It is also possible to prioritize saving frequently used data formats from the past stored data. It is also possible to analyze past stored data and apply an efficient storage algorithm. This makes it possible to select an optimal storage format based on the past stored data. The optimization of the storage algorithm is performed using, for example, data compression technology or selection of a storage format. This makes it possible for the storage unit to optimize the storage algorithm by referring to past stored data when saving.

[0044] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit adjusts the level of detail of storage based on the importance of the data when storing the data. For example, data with high importance is stored in detail. Data with low importance can also be stored concisely. Furthermore, the level of detail of storage can be dynamically adjusted based on the importance of the data. This makes it possible to dynamically adjust the level of detail of storage according to the importance of the data. The adjustment of the level of detail of storage is performed using, for example, the granularity of the data or the storage format. This makes it possible for the storage unit to adjust the level of detail of storage based on the importance of the data when storing the data.

[0045] The storage unit can determine the priority of the stored data based on the time of submission of the data when the data is saved. The storage unit, for example, determines the priority of the stored data based on the time of submission of the data when the data is saved. For example, the most recent data is saved preferentially. Data that was submitted earlier can also be saved later. Furthermore, the priority of the stored data can be dynamically adjusted based on the time of submission. This makes it possible to dynamically adjust the priority of the stored data based on the time of submission. The priority of the stored data is determined using, for example, the importance of the data or the date and time of submission. This allows the storage unit to determine the priority of the stored data based on the time of submission of the data when the data is saved.

[0046] The storage unit can improve storage accuracy by referencing a related external database when saving. The storage unit can improve storage accuracy by, for example, referencing a related external database when saving. For example, the storage unit can refer to an external technical database and save related data. The storage unit can also improve storage accuracy by referencing an external industry standard database. Furthermore, the storage unit can also refer to an external academic database and perform storage based on the latest information. In this way, storage accuracy is improved by referencing an external database. The external database is referenced using, for example, an API usage method or a database type. In this way, the storage unit can improve storage accuracy by referencing a related external database when saving.

[0047] The pasting unit can select the optimal pasting method by referring to the user's past pasting history when pasting. The pasting unit, for example, selects the optimal pasting method by referring to the user's past pasting history when pasting. For example, the pasting unit can suggest the optimal method based on past pasting methods used by the user. It can also preferentially suggest frequently used methods based on the user's past pasting history. Furthermore, it can analyze the user's past pasting history and provide an efficient pasting method. This makes it possible to provide the optimal pasting method based on the past pasting history. The pasting history is referenced using, for example, a method for saving history data or a method for analyzing history data. This makes it possible for the pasting unit to select the optimal pasting method by referring to the user's past pasting history when pasting.

[0048] The pasting unit can adjust the priority of pasting based on the user's current work situation when pasting. The pasting unit, for example, adjusts the priority of pasting based on the user's current work situation when pasting. For example, when the user is busy working, pasting can be performed by voice instruction. Also, when the user is doing desk work, pasting can be performed by keyword input. Furthermore, when the user is on the move, pasting can be performed by a simple voice command. This makes it possible to provide an optimal pasting method depending on the user's work situation. The work situation is identified, for example, by using work log analysis or real-time monitoring. This allows the pasting unit to adjust the priority of pasting based on the user's current work situation when pasting.

[0049] The pasting unit can prioritize presenting highly relevant pasting methods in consideration of the user's geographical location information when pasting. For example, the pasting unit prioritizes presenting highly relevant pasting methods in consideration of the user's geographical location information when pasting. For example, when the user is in the office, keyword input can be prioritized. Also, when the user is out, voice input can be prioritized. Furthermore, when the user is at home, detailed input options can be provided. This makes it possible to provide an optimal pasting method based on the user's geographical location information. The geographical location information is identified using, for example, GPS data or a location information service. This makes it possible for the pasting unit to prioritize presenting highly relevant pasting methods in consideration of the user's geographical location information when pasting.

[0050] The pasting unit can analyze the user's social media activity at the time of pasting and suggest a related pasting method. The pasting unit, for example, analyzes the user's social media activity at the time of pasting and suggests a related pasting method. For example, keywords frequently used by the user on social media can be automatically displayed as candidates. The pasting unit can also suggest the optimal pasting method (audio, text, etc.) based on the user's social media activity. Furthermore, the user's social media activity can be analyzed and the name of a related manual can be suggested. This makes it possible to provide the optimal pasting method based on the user's social media activity. The analysis of social media activity is performed, for example, using an analysis of the content of posts and the trends of followers. This makes it possible for the pasting unit to analyze the user's social media activity at the time of pasting and suggest a related pasting method.

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

[0052] The reception unit can also automatically search for and provide related images and videos based on the user's input. For example, if the user inputs "operation manual," it will search for videos and images of related operating procedures and paste them onto a dedicated sheet. Also, if the user inputs "tell me how to operate it," it can automatically play related videos. This allows the user to obtain visual information, making it easier for them to understand.

[0053] The analysis unit can also automatically search for and provide related FAQs and support information based on the user's input. For example, if the user types "troubleshooting," it will search for related FAQs and support information and paste it onto a dedicated sheet. It can also automatically display related support information when the user uses voice input to say, "Tell me how to solve the problem." This allows the user to quickly obtain information to solve the problem.

[0054] The storage unit can also automatically display and reuse related past search history based on the user's input. For example, if the user inputs "operation manual," related operation manuals searched in the past are automatically displayed. Also, if the user gives a voice command such as "Display the previous operation manual," related manuals from the past search history can be automatically displayed. This allows the user to easily reuse past search history.

[0055] The pasting unit can also automatically select and provide a related template based on the user's input. For example, if the user types "report template," it will search for a related report template and paste it onto a dedicated sheet. Also, if the user gives the voice command "Display report templates," it can automatically display related templates. This allows the user to easily find the appropriate template.

[0056] The analysis unit can also automatically search for and provide related external resources based on user input. For example, if a user types "technical documentation," related external technical documentation will be searched for and pasted onto a dedicated sheet. Also, if the user gives the command "search for technical documentation" via voice input, related external resources can be automatically displayed. This allows users to easily use external resources.

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

[0058] Step 1: The accepting unit accepts input of a keyword or voice. For example, when a user inputs a keyword, the input is in text format, and when a user inputs a voice, the input is in voice format. Step 2: The analysis unit analyzes the information received by the reception unit and identifies the name of the associated manual. The analysis unit performs the analysis using, for example, natural language processing technology or voice recognition technology. Step 3: The storage unit temporarily stores the manual name identified by the analysis unit. The storage unit can store the manual name in a memory, for example, and delete it after a certain period of time. Step 4: The pasting unit pastes the name of the manual stored in the storage unit onto a dedicated sheet, such as an Excel spreadsheet or a specific application.

[0059] (Example 2) The AI ​​service according to an embodiment of the present invention is a system that allows anyone to easily enter keywords and automatically paste various manual titles onto a dedicated sheet. This AI service supports not only keyword input but also voice input, making it easy to use even for workers who are busy. Specifically, it consists of the following steps: First, the user inputs keywords or issues voice instructions. Next, the AI ​​analyzes the input keywords or voice instructions and searches for related manual titles. The generated manual titles are automatically pasted onto the dedicated sheet. This eliminates the need for users to manually enter manual titles. Furthermore, voice input is supported, making the service easy to use even for workers who are busy. This service allows users to easily find the necessary manuals and improves work efficiency. For example, if a user enters "operation manual," the AI ​​searches for related operation manuals and automatically pastes them onto the dedicated sheet. Alternatively, if a user instructs the AI ​​to "search for operation manuals by voice," the AI ​​analyzes the voice instruction, similarly searches for operation manuals, and pastes them onto the dedicated sheet. This eliminates the need for users to manually enter manual titles. Furthermore, voice input is supported, making the service easy to use even for workers who are busy. This service allows users to easily find the manuals they need, improving work efficiency. The AI ​​service analyzes information entered by users using keywords or voice, and automatically pastes the names of related manuals into a dedicated sheet.

[0060] The AI ​​service according to the embodiment includes a reception unit, an analysis unit, a storage unit, and an attachment unit. The reception unit receives keyword or voice input. For example, when a user inputs a keyword, the input is in text format. When a user inputs voice, the input is in voice format. The analysis unit analyzes the information received by the reception unit and identifies the name of a related manual. The analysis unit performs the analysis using, for example, natural language processing technology or voice recognition technology. The attachment unit temporarily stores the manual name identified by the analysis unit. The storage unit can store the manual name in memory, for example, and delete it after a certain period of time. The attachment unit attaches the manual name stored in the storage unit to a dedicated sheet. The dedicated sheet includes, for example, an Excel spreadsheet or a specific application. As a result, the AI ​​service according to the embodiment can analyze information input by a user using keywords or voice, and automatically attach the name of a related manual to the dedicated sheet.

[0061] The reception unit can accommodate both keyword input and voice input. For example, the reception unit provides an interface that accommodates both keyword input and voice input. For example, a text box for the user to enter keywords and a microphone icon for voice input are provided. In the case of voice input, voice recognition technology is used to convert the voice into text. This allows the user to use both keyword input and voice input.

[0062] The analysis unit can perform analysis based on a pre-trained database. The analysis unit performs analysis based on, for example, a pre-trained database. The pre-trained database includes, for example, a data set and a learning algorithm to be used. This allows the analysis unit to improve the accuracy of analysis based on the pre-trained database.

[0063] The pasting unit can automatically paste the manual name into an Excel spreadsheet or a dedicated application. The pasting unit automatically pastes the manual name into, for example, an Excel spreadsheet or a dedicated application. In an Excel spreadsheet, the manual name can be pasted into, for example, a specific cell. In a dedicated application, the manual name can be pasted into, for example, a specific field. This makes it possible to automatically paste the manual name into an Excel spreadsheet or a dedicated application.

[0064] The analysis unit can use AI to analyze input keywords and voice instructions and search for related manual names. The analysis unit can, for example, use AI to analyze input keywords and voice instructions and search for related manual names. AI technology includes, for example, machine learning algorithms and deep learning models. This allows the analysis unit to use AI to efficiently search for related manual names.

[0065] The storage unit temporarily stores the analysis results and can efficiently manage them. The storage unit, for example, temporarily stores the analysis results and can efficiently manage them. The storage unit can, for example, store the analysis results in memory and delete them after a certain period of time. This allows the analysis results to be efficiently managed.

[0066] The reception unit can estimate the user's emotion and automatically switch the input method based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and automatically switches the input method based on the estimated user's emotion. For example, if the user is feeling stressed, voice input can be prioritized to reduce the effort required. Also, if the user is relaxed, detailed keyword input options can be provided. Furthermore, if the user is in a hurry, simple keyword input can be prioritized to speed up processing. This makes it possible to provide the optimal input method according to the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. This allows the reception unit to estimate the user's emotion and automatically switch the input method based on the estimated user's emotion.

[0067] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, keywords that the user has frequently used in the past can be automatically displayed as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest keywords that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the past input history. The analysis of the past input history is performed using, for example, data mining technology or statistical analysis. This allows the reception unit to analyze the user's past input history and suggest the optimal input method.

[0068] The reception unit can adjust the priority of input based on the user's current work situation when inputting data. The reception unit, for example, adjusts the priority of input based on the user's current work situation when inputting data. For example, if the user is busy working, voice input can be prioritized. Also, if the user is doing desk work, keyword input can be prioritized. Furthermore, if the user is on the move, simple voice commands can be prioritized. This makes it possible to provide an optimal input method depending on the user's work situation. The current work situation is identified, for example, by analyzing a work log or using real-time monitoring. This allows the reception unit to adjust the priority of input based on the user's current work situation when inputting data.

[0069] The reception unit can estimate the user's emotion and provide input feedback based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and provides input feedback based on the estimated user's emotion. For example, if the user is feeling stressed, an encouraging message can be displayed. Also, if the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, brief feedback can be provided. In this way, feedback according to the user's emotion can be provided. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. In this way, the reception unit can estimate the user's emotion and provide input feedback based on the estimated user's emotion.

[0070] The reception unit can prioritize presenting highly relevant input methods based on the user's geographical location information at the time of input. For example, the reception unit prioritizes presenting highly relevant input methods taking into account the user's geographical location information at the time of input. For example, when the user is in the office, keyword input can be prioritized. Also, when the user is out, voice input can be prioritized. Furthermore, when the user is at home, detailed input options can be provided. This makes it possible to provide the optimal input method based on the user's geographical location information. The geographical location information is identified using, for example, GPS data or a location information service. This makes it possible for the reception unit to prioritize presenting highly relevant input methods taking into account the user's geographical location information at the time of input.

[0071] The reception unit can analyze the user's social media activity at the time of input and suggest a related input method. The reception unit, for example, analyzes the user's social media activity at the time of input and suggests a related input method. For example, keywords frequently used by the user on social media can be automatically displayed as candidates. The reception unit can also suggest the optimal input method (voice, text, etc.) based on the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest the name of a related manual. This makes it possible to provide the optimal input method based on the user's social media activity. Analysis of social media activity is performed, for example, using an analysis of the content of posts and the trends of followers. This makes it possible for the reception unit to analyze the user's social media activity at the time of input and suggest a related input method.

[0072] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis algorithm based on the estimated user's emotion. For example, if the user is relaxed, a detailed analysis can be performed. Also, if the user is in a hurry, a quick analysis can be performed. Furthermore, if the user is feeling stressed, a concise analysis can be performed. This allows the analysis algorithm to be adjusted according to the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. This allows the analysis unit to estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion.

[0073] The analysis unit can improve the analysis accuracy by taking into account the input keywords and the context of the voice during analysis. The analysis unit can improve the analysis accuracy by taking into account, for example, the input keywords and the context of the voice during analysis. For example, the analysis unit can analyze the context before and after the keywords to identify a highly relevant manual name. It can also analyze the intonation and emphasis of the voice instructions to identify an appropriate manual name. It can also resolve ambiguous instructions by taking into account the keywords and the context of the voice. In this way, the analysis accuracy is improved by taking into account the keywords and the context of the voice. The context can be taken into account by, for example, analyzing the context before and after the keywords and extracting related topics. In this way, the analysis unit can improve the analysis accuracy by taking into account the input keywords and the context of the voice during analysis.

[0074] The analysis unit can optimize the analysis results by referring to the user's past search history during analysis. The analysis unit, for example, can optimize the analysis results by referring to the user's past search history during analysis. For example, the analysis unit can suggest highly relevant manual names based on manual names that the user has searched for in the past. It can also prioritize analysis of frequently used keywords from the user's past search history. It can also analyze the user's past search history and provide optimal analysis results. This makes it possible to optimize the analysis results by referring to the past search history. The past search history is referred to using, for example, a method for saving search logs or a method for analyzing history data. This makes it possible for the analysis unit to optimize the analysis results by referring to the user's past search history during analysis.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method of the analysis results can be adjusted according to the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. In this way, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions.

[0076] The analysis unit can determine the analysis priority based on the submission time of the input keywords and voice during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the input keywords and voice during analysis. For example, the analysis unit gives priority to the most recent keywords and voice instructions. It can also postpone the analysis of keywords and voice instructions that were submitted earlier. Furthermore, the analysis priority can also be dynamically adjusted based on the submission time. This makes it possible to dynamically adjust the analysis priority based on the submission time. The submission time is identified using, for example, a method for recording the submission date and time or a priority algorithm. This makes it possible for the analysis unit to determine the analysis priority based on the submission time of the input keywords and voice during analysis.

[0077] The analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis. The analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis, for example. For example, the analysis unit can refer to an external technical database to identify the name of a related manual. The analysis unit can also improve the accuracy of the analysis results by referring to an external industry standard database. Furthermore, the analysis can be performed based on the latest information by referring to an external academic database. In this way, the analysis accuracy is improved by referring to an external database. The external database is referred to using, for example, the API usage method or the type of database. In this way, the analysis unit can improve the accuracy of the analysis by referring to a related external database during analysis.

[0078] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. The storage unit, for example, estimates the user's emotions and selects data to be saved based on the estimated user emotions. For example, if the user is relaxed, detailed data can be saved. Also, if the user is in a hurry, data that focuses on the main points can be saved. Furthermore, if the user is feeling stressed, concise data can be saved. This makes it possible to select optimal data to be saved based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology. This makes it possible for the storage unit to estimate the user's emotions and select data to be saved based on the estimated user emotions.

[0079] The storage unit can optimize the storage algorithm by referring to past stored data when saving. The storage unit, for example, optimizes the storage algorithm by referring to past stored data when saving. For example, the storage unit selects an optimal storage format based on past stored data. It is also possible to prioritize saving frequently used data formats from the past stored data. It is also possible to analyze past stored data and apply an efficient storage algorithm. This makes it possible to select an optimal storage format based on the past stored data. The optimization of the storage algorithm is performed using, for example, data compression technology or selection of a storage format. This makes it possible for the storage unit to optimize the storage algorithm by referring to past stored data when saving.

[0080] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit adjusts the level of detail of storage based on the importance of the data when storing the data. For example, data with high importance is stored in detail. Data with low importance can also be stored concisely. Furthermore, the level of detail of storage can be dynamically adjusted based on the importance of the data. This makes it possible to dynamically adjust the level of detail of storage according to the importance of the data. The adjustment of the level of detail of storage is performed using, for example, the granularity of the data or the storage format. This makes it possible for the storage unit to adjust the level of detail of storage based on the importance of the data when storing the data.

[0081] The storage unit can estimate the user's emotion and adjust the display method of the stored data based on the estimated user's emotion. The storage unit, for example, estimates the user's emotion and adjusts the display method of the stored data based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method of the stored data can be adjusted according to the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. In this way, the storage unit can estimate the user's emotion and adjust the display method of the stored data based on the estimated user's emotion.

[0082] The storage unit can determine the priority of the stored data based on the time of submission of the data when the data is saved. The storage unit, for example, determines the priority of the stored data based on the time of submission of the data when the data is saved. For example, the most recent data is saved preferentially. Data that was submitted earlier can also be saved later. Furthermore, the priority of the stored data can be dynamically adjusted based on the time of submission. This makes it possible to dynamically adjust the priority of the stored data based on the time of submission. The priority of the stored data is determined using, for example, the importance of the data or the date and time of submission. This allows the storage unit to determine the priority of the stored data based on the time of submission of the data when the data is saved.

[0083] The storage unit can improve storage accuracy by referencing a related external database when saving. The storage unit can improve storage accuracy by, for example, referencing a related external database when saving. For example, the storage unit can refer to an external technical database and save related data. The storage unit can also improve storage accuracy by referencing an external industry standard database. Furthermore, the storage unit can also refer to an external academic database and perform storage based on the latest information. In this way, storage accuracy is improved by referencing an external database. The external database is referenced using, for example, an API usage method or a database type. In this way, the storage unit can improve storage accuracy by referencing a related external database when saving.

[0084] The pasting unit can estimate the user's emotion and adjust the pasting method based on the estimated user's emotion. The pasting unit, for example, estimates the user's emotion and adjusts the pasting method based on the estimated user's emotion. For example, if the user is relaxed, a detailed pasting method can be provided. Also, if the user is in a hurry, a quick pasting method can be provided. Furthermore, if the user is stressed, a simple pasting method can be provided. In this way, it is possible to provide an optimal pasting method depending on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. In this way, the pasting unit can estimate the user's emotion and adjust the pasting method based on the estimated user's emotion.

[0085] The pasting unit can select the optimal pasting method by referring to the user's past pasting history when pasting. The pasting unit, for example, selects the optimal pasting method by referring to the user's past pasting history when pasting. For example, the pasting unit can suggest the optimal method based on past pasting methods used by the user. It can also preferentially suggest frequently used methods based on the user's past pasting history. Furthermore, it can analyze the user's past pasting history and provide an efficient pasting method. This makes it possible to provide the optimal pasting method based on the past pasting history. The pasting history is referenced using, for example, a method for saving history data or a method for analyzing history data. This makes it possible for the pasting unit to select the optimal pasting method by referring to the user's past pasting history when pasting.

[0086] The pasting unit can adjust the priority of pasting based on the user's current work situation when pasting. The pasting unit, for example, adjusts the priority of pasting based on the user's current work situation when pasting. For example, when the user is busy working, pasting can be performed by voice instruction. Also, when the user is doing desk work, pasting can be performed by keyword input. Furthermore, when the user is on the move, pasting can be performed by a simple voice command. This makes it possible to provide an optimal pasting method depending on the user's work situation. The work situation is identified, for example, by using work log analysis or real-time monitoring. This allows the pasting unit to adjust the priority of pasting based on the user's current work situation when pasting.

[0087] The pasting unit can estimate the user's emotion and adjust the display method of the pasted result based on the estimated user's emotion. The pasting unit, for example, estimates the user's emotion and adjusts the display method of the pasted result based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide an optimal display method depending on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. This allows the pasting unit to estimate the user's emotion and adjust the display method of the pasted result based on the estimated user's emotion.

[0088] The pasting unit can prioritize presenting highly relevant pasting methods in consideration of the user's geographical location information when pasting. For example, the pasting unit prioritizes presenting highly relevant pasting methods in consideration of the user's geographical location information when pasting. For example, when the user is in the office, keyword input can be prioritized. Also, when the user is out, voice input can be prioritized. Furthermore, when the user is at home, detailed input options can be provided. This makes it possible to provide an optimal pasting method based on the user's geographical location information. The geographical location information is identified using, for example, GPS data or a location information service. This makes it possible for the pasting unit to prioritize presenting highly relevant pasting methods in consideration of the user's geographical location information when pasting.

[0089] The pasting unit can analyze the user's social media activity at the time of pasting and suggest a related pasting method. The pasting unit, for example, analyzes the user's social media activity at the time of pasting and suggests a related pasting method. For example, keywords frequently used by the user on social media can be automatically displayed as candidates. The pasting unit can also suggest the optimal pasting method (audio, text, etc.) based on the user's social media activity. Furthermore, the user's social media activity can be analyzed and the name of a related manual can be suggested. This makes it possible to provide the optimal pasting method based on the user's social media activity. The analysis of social media activity is performed, for example, using an analysis of the content of posts and the trends of followers. This makes it possible for the pasting unit to analyze the user's social media activity at the time of pasting and suggest a related pasting method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and pasting unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives keyword and voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using natural language processing technology or voice recognition technology. The storage unit temporarily stores the input information in, for example, the storage 32 of the data processing device 12. The pasting unit is realized, for example, by the control unit 46A of the smart device 14 and pastes the saved manual title onto a dedicated sheet. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, storage unit, and attachment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives keyword or voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using natural language processing technology or voice recognition technology. The storage unit temporarily stores the input information in, for example, the storage 32 of the data processing device 12. The attachment unit is realized, for example, by the control unit 46A of the smart glasses 214 and attaches the saved manual title to a dedicated sheet. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and attachment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives keyword and voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using natural language processing technology or voice recognition technology. The storage unit temporarily stores the input information in the storage 32 of the data processing device 12, for example. The attachment unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and attaches the saved manual title to a dedicated sheet. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and pasting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives keyword or voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using natural language processing technology or voice recognition technology. The storage unit temporarily stores the input information in, for example, the storage 32 of the data processing device 12. The pasting unit is realized, for example, by the control unit 46A of the robot 414 and pastes the saved manual title onto a dedicated sheet.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can also automatically search for and provide related images and videos based on the user's input. For example, if the user inputs "operation manual," it will search for videos and images of related operating procedures and paste them onto a dedicated sheet. Also, if the user inputs "tell me how to operate it," it can automatically play related videos. This allows the user to obtain visual information, making it easier for them to understand.

[0092] The analysis unit can also automatically search for and provide related FAQs and support information based on the user's input. For example, if the user types "troubleshooting," it will search for related FAQs and support information and paste it onto a dedicated sheet. It can also automatically display related support information when the user uses voice input to say, "Tell me how to solve the problem." This allows the user to quickly obtain information to solve the problem.

[0093] The storage unit can also automatically display and reuse related past search history based on the user's input. For example, if the user inputs "operation manual," related operation manuals searched in the past are automatically displayed. Also, if the user gives a voice command such as "Display the previous operation manual," related manuals from the past search history can be automatically displayed. This allows the user to easily reuse past search history.

[0094] The pasting unit can also automatically select and provide a related template based on the user's input. For example, if the user types "report template," it will search for a related report template and paste it onto a dedicated sheet. Also, if the user gives the voice command "Display report templates," it can automatically display related templates. This allows the user to easily find the appropriate template.

[0095] The analysis unit can also automatically search for and provide related external resources based on user input. For example, if a user types "technical documentation," related external technical documentation will be searched for and pasted onto a dedicated sheet. Also, if the user gives the command "search for technical documentation" via voice input, related external resources can be automatically displayed. This allows users to easily use external resources.

[0096] The reception unit can also estimate the user's emotions and provide appropriate feedback based on the estimated user's emotions. For example, if the user is feeling stressed, an encouraging message can be displayed. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, brief feedback can be provided. In this way, feedback can be provided according to the user's emotions.

[0097] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method of the analysis results can be adjusted according to the user's emotions.

[0098] The storage unit can also estimate the user's emotions and select data to be stored based on the estimated user emotions. For example, if the user is relaxed, detailed data can be stored. If the user is in a hurry, data that focuses on the main points can be stored. Furthermore, if the user is stressed, concise data can be stored. This allows the optimal data to be selected according to the user's emotions.

[0099] The pasting unit can also estimate the user's emotion and adjust the pasting method based on the estimated user's emotion. For example, if the user is relaxed, a detailed pasting method can be provided. If the user is in a hurry, a quick pasting method can be provided. Furthermore, if the user is stressed, a simple pasting method can be provided. In this way, the optimal pasting method can be provided according to the user's emotion.

[0100] The reception unit can also estimate the user's emotions and automatically switch the input method based on the estimated user emotions. For example, if the user is feeling stressed, voice input can be prioritized to reduce the effort required. Also, if the user is relaxed, detailed keyword input options can be provided. Furthermore, if the user is in a hurry, simple keyword input can be prioritized to speed up processing. This makes it possible to provide the optimal input method according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The accepting unit accepts input of a keyword or voice. For example, when a user inputs a keyword, the input is in text format, and when a user inputs a voice, the input is in voice format. Step 2: The analysis unit analyzes the information received by the reception unit and identifies the name of the associated manual. The analysis unit performs the analysis using, for example, natural language processing technology or voice recognition technology. Step 3: The storage unit temporarily stores the manual name identified by the analysis unit. The storage unit can store the manual name in a memory, for example, and delete it after a certain period of time. Step 4: The pasting unit pastes the name of the manual stored in the storage unit onto a dedicated sheet, such as an Excel spreadsheet or a specific application.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0137] 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.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a keyword or voice; an analysis unit that analyzes the information received by the reception unit and identifies the name of a related manual; a storage unit that temporarily stores the manual name identified by the analysis unit; A pasting section is provided for pasting the name of the manual stored in the storage section onto a dedicated sheet. A system characterized by:

2. The reception unit Supports both keyword input and voice input 2. The system of claim 1.

3. The analysis unit Perform analysis based on a pre-trained database 2. The system of claim 1.

4. The attachment portion is Automatically paste the manual name into an Excel spreadsheet or dedicated application 2. The system of claim 1.

5. The analysis unit AI is used to analyze input keywords and voice commands and search for related manual titles.

2. The system of claim 1.

6. The storage unit Temporarily store and efficiently manage analysis results 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and automatically switches input methods based on the estimated emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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