system

Abstract

The system according to this embodiment aims to efficiently create textbook-like documents summarizing the details of a case, thereby improving the consistency and clarity of the information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a storage unit. The reception unit receives instructions to send case information. The analysis unit analyzes the information received by the reception unit. The generation unit creates a textbook based on the information analyzed by the analysis unit. The storage unit stores the textbook created by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently create textbook-like texts summarizing the details of a case, and there is room for improvement in information consistency and readability.

[0005] The system according to the embodiment aims to efficiently create textbook-like texts summarizing the details of a case and improve information consistency and readability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a storage unit. The reception unit receives instructions to send case information. The analysis unit analyzes the information received by the reception unit. The generation unit creates a textbook based on the information analyzed by the analysis unit. The storage unit stores the textbook created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create textbook-like documents summarizing the details of a case, thereby improving the consistency and clarity of the information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The project textbook system according to an embodiment of the present invention is a system that uses a generative AI to create textbook-like text summarizing the details of a project. In this project textbook system, instructions to send project information are made via an API, and upon notification of the information, it is sent to the PJ file server. The generative AI then receives a file analysis instruction and performs the analysis. The analysis results are notified, and the textbook-generating AI creates or updates the textbook. The created textbook is stored on the textbook file server. This project textbook is designed to solve problems that prevent employees from keeping up with their work, such as having too much information, insufficient skills, or not having enough time to process it. For example, it addresses challenges such as being unable to scrutinize and acquire information, having information on various projects progressing simultaneously, having too many documents and meetings, not knowing what others are doing, and falling behind due to prolonged absences. The project textbook generated by the generative AI allows employees to acquire information and skills, freeing up time for activities. Furthermore, as an advanced form of this textbook creation, it is conceivable to sell a "project textbook solution" externally. For example, by offering it on a subscription model and introducing it to companies listed on the Tokyo Stock Exchange Prime and medium-sized or larger IT-related companies in Japan, revenue can be expected. This enables the project textbook system to efficiently receive, analyze, generate, and store project information.

[0029] The project textbook system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a storage unit. The reception unit receives instructions to send case information. The reception unit can receive instructions to send case information, for example, via an API. The analysis unit analyzes the information received by the reception unit. The analysis unit can receive information sending instructions, for example, via an API, and analyze the information. The generation unit creates a textbook based on the information analyzed by the analysis unit. The generation unit can create a textbook based on the analyzed information, for example. The generation unit creates a textbook using a generation AI. The generation AI can create a textbook using, for example, a text generation AI (e.g., LLM). The storage unit stores the textbooks created by the generation unit. The storage unit can store the created textbooks on a textbook file server, for example. This enables the project textbook system to efficiently receive, analyze, generate, and store case information.

[0030] The reception department receives instructions to send case information. For example, the reception department can receive instructions to send case information via API. Specifically, the reception department provides standard interfaces such as RESTful APIs and SOAP APIs to receive instructions to send case information from external systems and users. This allows the reception department to interact with various systems and applications and flexibly receive case information. Furthermore, the reception department has a function to temporarily store the received case information and verify the integrity and completeness of the data before passing it on to the analysis department. For example, the reception department verifies the format and content of the received data to check for any invalid or missing data. This allows the reception department to provide highly reliable data to the analysis department. The reception department also records logs of the received case information for later reference. This provides information useful for monitoring system operation and troubleshooting.

[0031] The analysis unit analyzes information received by the reception unit. For example, the analysis unit can receive information sending instructions via API and analyze the information. Specifically, the analysis unit analyzes the received case information using natural language processing and data mining technologies to extract important keywords and topics. For example, it performs morphological analysis on the text data contained in the case information and classifies it by part of speech such as nouns and verbs. The analysis unit can also search for related past cases and reference materials based on the content of the case information and extract highly relevant information. Furthermore, the analysis unit uses AI to understand the meaning and intent of the received information and organize the information necessary for textbook creation. For example, if the received case information concerns the progress or issues of a project, the analysis unit identifies the project phase and the type of issue, and determines the content of the textbook accordingly. In this way, the analysis unit can efficiently and accurately analyze the received information and provide it to the generation unit.

[0032] The generation unit creates textbooks based on information analyzed by the analysis unit. For example, the generation unit can create textbooks based on the analyzed information. The generation unit uses generative AI to create textbooks. For example, the generative AI can use text generation AI (e.g., LLM) to create textbooks. Specifically, the generation unit automatically generates the structure and content of the textbook based on the information provided by the analysis unit. The generative AI uses a large-scale language model to generate natural-sounding text and writes each chapter and section of the textbook. For example, it creates a chapter explaining the basics of project management and specific countermeasures based on information about the project's progress and challenges. Furthermore, the generation unit has a function to check the content of the generated textbook and make corrections or additions as needed. This allows the generation unit to efficiently create high-quality textbooks. In addition, the generation unit provides a function to customize the content of the textbook, allowing for the creation of textbooks tailored to the needs of specific projects or users. For example, creating textbooks specialized for specific industries or fields can provide more practical and useful information.

[0033] The storage unit stores the textbooks created by the generation unit. For example, the storage unit can store the created textbooks on a textbook file server. Specifically, the storage unit converts the generated textbooks into an appropriate format (e.g., PDF or HTML) and saves them to the file server. This ensures that the textbooks are always accessible. The storage unit also manages textbook metadata (e.g., creation date, author, version information) and has features to facilitate searching and referencing textbooks. Furthermore, the storage unit provides textbook backup and restore functions to ensure data security and availability. For example, it can create regular backups of textbooks to prepare for potential data loss. The storage unit also manages textbook access permissions, allowing only specific users or groups to view or edit textbooks. This enables the storage unit to manage generated textbooks securely and efficiently and provide them quickly when needed.

[0034] The reception desk can receive instructions to send case information via API. For example, the reception desk can receive instructions to send case information using a REST API. It can also receive instructions to send case information using a SOAP API. Furthermore, the reception desk can receive instructions to send case information using GraphQL. This allows the reception desk to receive instructions to send case information via API.

[0035] The analysis unit can receive information transmission instructions via APIs and analyze the information. For example, the analysis unit can receive information transmission instructions using a REST API and analyze the information. Furthermore, the analysis unit can also receive information transmission instructions using a SOAP API and analyze the information. In addition, the analysis unit can receive information transmission instructions using GraphQL and analyze the information. This allows the analysis unit to receive information transmission instructions via APIs and analyze the information.

[0036] The generation unit can create textbooks based on the analyzed information. For example, the generation unit creates textbooks based on the analyzed information. The generation unit creates textbooks using a generation AI. For example, the generation AI can create textbooks using a text generation AI (e.g., LLM). The generation unit can create textbooks using the generation AI based on the analyzed information. This allows the generation unit to create textbooks based on the analyzed information.

[0037] The storage unit can store the created textbooks on a textbook file server. For example, the storage unit can store the created textbooks on cloud storage. Furthermore, the storage unit can also store the created textbooks on an on-premises server. In addition, the storage unit can store the created textbooks on hybrid storage. This allows the created textbooks to be stored on a textbook file server.

[0038] The generation unit can update textbooks. For example, the generation unit can periodically update the content of textbooks. Furthermore, the generation unit can update the content of textbooks in real time. In addition, the generation unit can update the content of textbooks based on user feedback. This enables the textbooks to be updated.

[0039] The generation unit can sell the "Project Textbook Solution" externally as an advanced form of textbook creation. For example, the generation unit can offer the "Project Textbook Solution" on a subscription model. Alternatively, the generation unit can offer the "Project Textbook Solution" on a licensing model. Furthermore, the generation unit can offer the "Project Textbook Solution" on a customization model. This allows the "Project Textbook Solution" to be sold externally as an advanced form of textbook creation.

[0040] The reception department can analyze the user's past case information submission history and select the optimal reception method. For example, the reception department may prioritize suggesting reception methods that the user has frequently used in the past. The reception department can also suggest the optimal reception method for a specific time period based on the user's past submission history. Furthermore, the reception department can analyze the user's past submission history and select the most efficient reception method. This allows the reception department to analyze the user's past case information submission history and select the optimal reception method. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI.

[0041] The reception unit can filter case information upon receiving instructions to send case information based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving only information related to the user's current projects. The reception unit can also filter and receive case information that is highly relevant based on the user's areas of interest. Furthermore, the reception unit can prioritize receiving information of high importance depending on the user's project status. This allows for filtering of case information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0042] The reception unit, upon receiving instructions to send case information, can prioritize receiving highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving case information related to that region. The reception unit can also prioritize receiving nearby case information based on the user's current location. Furthermore, the reception unit can prioritize receiving highly relevant information by considering the user's travel history. This allows the reception unit to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0043] The reception department can analyze the user's social media activity and receive relevant information upon receiving instructions to send case information. For example, the reception department can analyze the content of the user's social media posts and prioritize receiving relevant case information. The reception department can also receive information of high interest based on the user's social media activity history. Furthermore, the reception department can receive highly relevant information by referring to the activities of the user's followers and friends. This allows the reception department to analyze the user's social media activity and receive relevant information. Some or all of the above processing in the reception department may be performed using AI, for example, or without using AI.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the case information during information analysis. For example, the analysis unit performs a detailed analysis on case information with high importance. It can also perform a simplified analysis on case information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the case information. This allows the level of detail of the analysis to be adjusted based on the importance of the case information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the case information during information analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical case information. It can also apply a business-oriented analysis algorithm to business-related case information. Furthermore, the analysis unit can select the most suitable analysis algorithm depending on the category of the case information. This allows for the application of different analysis algorithms depending on the category of the case information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0046] The analysis unit can determine the priority of analysis based on the submission timing of case information during information analysis. For example, the analysis unit will prioritize the analysis of case information with an approaching submission deadline. The analysis unit can also postpone the analysis of case information whose submission deadline has passed. Furthermore, the analysis unit can adjust the analysis schedule based on the submission timing. This allows the analysis priority to be determined based on the submission timing of case information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The analysis unit can adjust the order of analysis based on the relevance of case information during information analysis. For example, the analysis unit prioritizes the analysis of highly relevant case information. It can also postpone the analysis of less relevant case information. Furthermore, the analysis unit can determine the order of analysis based on the relevance of case information. This allows the order of analysis to be adjusted based on the relevance of case information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0048] The generation unit can adjust the level of detail in the textbook based on the importance of the analyzed information during textbook generation. For example, the generation unit can generate a textbook that includes detailed explanations for information of high importance. It can also generate a simplified textbook for information of low importance. Furthermore, the generation unit can adjust the level of detail in the textbook according to the importance of the information. This allows the level of detail in the textbook to be adjusted based on the importance of the analyzed information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0049] The generation unit can apply different generation algorithms depending on the category of the case information when generating textbooks. For example, the generation unit can apply a specialized generation algorithm to technical case information. It can also apply a business-oriented generation algorithm to business-related case information. Furthermore, the generation unit can select the most suitable generation algorithm depending on the category of the case information. This allows for the application of different generation algorithms depending on the category of the case information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0050] The generation unit can determine the priority of textbooks based on the submission timing of the analyzed information during textbook generation. For example, the generation unit prioritizes incorporating information with approaching submission deadlines into the textbooks. It can also postpone information whose submission deadline has passed. Furthermore, the generation unit can adjust the textbook generation schedule based on the submission timing. This allows for the determination of textbook priorities based on the submission timing of the analyzed information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0051] The generation unit can adjust the order of the textbook based on the relevance of the analyzed information during textbook generation. For example, the generation unit prioritizes reflecting highly relevant information in the textbook. The generation unit can also postpone the inclusion of less relevant information. Furthermore, the generation unit can determine the order of the textbook based on the relevance of the information. This allows the order of the textbook to be adjusted based on the relevance of the analyzed information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0052] The storage unit can select the optimal storage method when storing textbooks by referring to the user's past textbook usage history. For example, the storage unit may prioritize suggesting storage methods that the user has frequently used in the past. The storage unit can also suggest the optimal storage method for a specific time period based on the user's past usage history. Furthermore, the storage unit can analyze the user's past usage history and select the most efficient storage method. This allows the storage unit to select the optimal storage method by referring to the user's past textbook usage history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0053] The storage unit can customize the storage method based on the user's current project status when storing textbooks. For example, the storage unit can prioritize storing textbooks related to the user's current project. It can also prioritize storing textbooks of high importance depending on the user's project status. Furthermore, the storage unit can suggest the optimal storage method based on the user's project progress. This allows the storage method to be customized based on the user's current project status. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0054] The storage unit can select the optimal storage method when storing textbooks, taking into account the user's geographical location information. For example, if the user is in a specific region, the storage unit will prioritize storing textbooks related to that region. The storage unit can also prioritize storing nearby textbooks based on the user's current location. Furthermore, the storage unit can prioritize storing highly relevant textbooks by considering the user's travel history. This allows the storage unit to select the optimal storage method while taking into account the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0055] The storage unit can analyze the user's social media activity and suggest storage methods when storing textbooks. For example, the storage unit can analyze the content of the user's social media posts and prioritize storing relevant textbooks. The storage unit can also store textbooks of high interest based on the user's social media activity history. Furthermore, the storage unit can store highly relevant textbooks by referring to the activities of the user's followers and friends. This allows the storage unit to analyze the user's social media activity and suggest storage methods. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can optimize its analysis algorithm by referring to the user's past analysis results when analyzing case information. For example, it can prioritize the application of analysis methods that have been successful in the past. It can also try different analysis methods to avoid past failures. Furthermore, it can adjust the analysis algorithm based on the user's past feedback. This allows the analysis algorithm to be optimized by referring to the user's past analysis results.

[0058] The generation unit can customize the content of textbooks based on the user's learning style during the textbook creation process. For example, visual learners can be provided with textbooks that make extensive use of diagrams and graphs. Auditory learners can be provided with textbooks that include audio explanations. Furthermore, practical learners can be provided with textbooks that include many real-world examples and practice problems. In this way, the content of textbooks can be customized according to the user's learning style.

[0059] The storage unit can select the optimal storage format based on the user's device environment when storing textbooks. For example, it can provide a lightweight and easy-to-read format for users using mobile devices, a format containing detailed information for users using desktop devices, and a format that is easily accessible online for users using cloud storage. This allows the system to select the optimal storage format based on the user's device environment.

[0060] The analysis unit can provide real-time data feedback when analyzing case information. For example, if new information is added during the analysis, it can be immediately reflected in the analysis. Furthermore, because the analysis results are updated sequentially, users can always stay informed of the latest information. In addition, real-time feedback can improve the accuracy of the analysis. This enables real-time data feedback.

[0061] The storage unit can select the optimal storage method for textbooks based on the user's network environment. For example, textbooks can be provided in a lightweight file format to users with slow network environments. Conversely, textbooks can be provided in a large file format containing detailed information to users with high-speed network environments. Furthermore, textbooks can be provided in a downloadable format to users in offline environments. This allows the system to select the optimal storage method based on the user's network environment.

[0062] The analysis unit can incorporate user feedback in real time when analyzing case information. For example, if a user adds a comment to the analysis results, that comment can be immediately reflected in the analysis. Furthermore, if a user requests a correction to the analysis results, that correction can be made in real time. In addition, the analysis algorithm can be adjusted based on user feedback. This allows for real-time incorporation of user feedback.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception department receives instructions to send case information. For example, it can receive instructions to send case information via API. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it can receive information sending instructions via API and analyze the information. Step 3: The generation unit creates a textbook based on the information analyzed by the analysis unit. For example, a generation AI can be used to create the textbook. The generation AI can create the textbook using a text generation AI (e.g., LLM). Step 4: The storage unit stores the textbooks created by the generation unit. For example, the created textbooks can be stored in a textbook file server.

[0065] (Example of form 2) The project textbook system according to an embodiment of the present invention is a system that uses a generative AI to create textbook-like text summarizing the details of a project. In this project textbook system, instructions to send project information are made via an API, and upon notification of the information, it is sent to the PJ file server. The generative AI then receives a file analysis instruction and performs the analysis. The analysis results are notified, and the textbook-generating AI creates or updates the textbook. The created textbook is stored on the textbook file server. This project textbook is designed to solve problems that prevent employees from keeping up with their work, such as having too much information, insufficient skills, or not having enough time to process it. For example, it addresses challenges such as being unable to scrutinize and acquire information, having information on various projects progressing simultaneously, having too many documents and meetings, not knowing what others are doing, and falling behind due to prolonged absences. The project textbook generated by the generative AI allows employees to acquire information and skills, freeing up time for activities. Furthermore, as an advanced form of this textbook creation, it is conceivable to sell a "project textbook solution" externally. For example, by offering it on a subscription model and introducing it to companies listed on the Tokyo Stock Exchange Prime and medium-sized or larger IT-related companies in Japan, revenue can be expected. This enables the project textbook system to efficiently receive, analyze, generate, and store project information.

[0066] The project textbook system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a storage unit. The reception unit receives instructions to send case information. The reception unit can receive instructions to send case information, for example, via an API. The analysis unit analyzes the information received by the reception unit. The analysis unit can receive information sending instructions, for example, via an API, and analyze the information. The generation unit creates a textbook based on the information analyzed by the analysis unit. The generation unit can create a textbook based on the analyzed information, for example. The generation unit creates a textbook using a generation AI. The generation AI can create a textbook using, for example, a text generation AI (e.g., LLM). The storage unit stores the textbooks created by the generation unit. The storage unit can store the created textbooks on a textbook file server, for example. This enables the project textbook system to efficiently receive, analyze, generate, and store case information.

[0067] The reception department receives instructions to send case information. For example, the reception department can receive instructions to send case information via API. Specifically, the reception department provides standard interfaces such as RESTful APIs and SOAP APIs to receive instructions to send case information from external systems and users. This allows the reception department to interact with various systems and applications and flexibly receive case information. Furthermore, the reception department has a function to temporarily store the received case information and verify the integrity and completeness of the data before passing it on to the analysis department. For example, the reception department verifies the format and content of the received data to check for any invalid or missing data. This allows the reception department to provide highly reliable data to the analysis department. The reception department also records logs of the received case information for later reference. This provides information useful for monitoring system operation and troubleshooting.

[0068] The analysis unit analyzes information received by the reception unit. For example, the analysis unit can receive information sending instructions via API and analyze the information. Specifically, the analysis unit analyzes the received case information using natural language processing and data mining technologies to extract important keywords and topics. For example, it performs morphological analysis on the text data contained in the case information and classifies it by part of speech such as nouns and verbs. The analysis unit can also search for related past cases and reference materials based on the content of the case information and extract highly relevant information. Furthermore, the analysis unit uses AI to understand the meaning and intent of the received information and organize the information necessary for textbook creation. For example, if the received case information concerns the progress or issues of a project, the analysis unit identifies the project phase and the type of issue, and determines the content of the textbook accordingly. In this way, the analysis unit can efficiently and accurately analyze the received information and provide it to the generation unit.

[0069] The generation unit creates textbooks based on information analyzed by the analysis unit. For example, the generation unit can create textbooks based on the analyzed information. The generation unit uses generative AI to create textbooks. For example, the generative AI can use text generation AI (e.g., LLM) to create textbooks. Specifically, the generation unit automatically generates the structure and content of the textbook based on the information provided by the analysis unit. The generative AI uses a large-scale language model to generate natural-sounding text and writes each chapter and section of the textbook. For example, it creates a chapter explaining the basics of project management and specific countermeasures based on information about the project's progress and challenges. Furthermore, the generation unit has a function to check the content of the generated textbook and make corrections or additions as needed. This allows the generation unit to efficiently create high-quality textbooks. In addition, the generation unit provides a function to customize the content of the textbook, allowing for the creation of textbooks tailored to the needs of specific projects or users. For example, creating textbooks specialized for specific industries or fields can provide more practical and useful information.

[0070] The storage unit stores the textbooks created by the generation unit. For example, the storage unit can store the created textbooks on a textbook file server. Specifically, the storage unit converts the generated textbooks into an appropriate format (e.g., PDF or HTML) and saves them to the file server. This ensures that the textbooks are always accessible. The storage unit also manages textbook metadata (e.g., creation date, author, version information) and has features to facilitate searching and referencing textbooks. Furthermore, the storage unit provides textbook backup and restore functions to ensure data security and availability. For example, it can create regular backups of textbooks to prepare for potential data loss. The storage unit also manages textbook access permissions, allowing only specific users or groups to view or edit textbooks. This enables the storage unit to manage generated textbooks securely and efficiently and provide them quickly when needed.

[0071] The reception desk can receive instructions to send case information via API. For example, the reception desk can receive instructions to send case information using a REST API. It can also receive instructions to send case information using a SOAP API. Furthermore, the reception desk can receive instructions to send case information using GraphQL. This allows the reception desk to receive instructions to send case information via API.

[0072] The analysis unit can receive information transmission instructions via APIs and analyze the information. For example, the analysis unit can receive information transmission instructions using a REST API and analyze the information. Furthermore, the analysis unit can also receive information transmission instructions using a SOAP API and analyze the information. In addition, the analysis unit can receive information transmission instructions using GraphQL and analyze the information. This allows the analysis unit to receive information transmission instructions via APIs and analyze the information.

[0073] The generation unit can create textbooks based on the analyzed information. For example, the generation unit creates textbooks based on the analyzed information. The generation unit creates textbooks using a generation AI. For example, the generation AI can create textbooks using a text generation AI (e.g., LLM). The generation unit can create textbooks using the generation AI based on the analyzed information. This allows the generation unit to create textbooks based on the analyzed information.

[0074] The storage unit can store the created textbooks on a textbook file server. For example, the storage unit can store the created textbooks on cloud storage. Furthermore, the storage unit can also store the created textbooks on an on-premises server. In addition, the storage unit can store the created textbooks on hybrid storage. This allows the created textbooks to be stored on a textbook file server.

[0075] The generation unit can update textbooks. For example, the generation unit can periodically update the content of textbooks. Furthermore, the generation unit can update the content of textbooks in real time. In addition, the generation unit can update the content of textbooks based on user feedback. This enables the textbooks to be updated.

[0076] The generation unit can sell the "Project Textbook Solution" externally as an advanced form of textbook creation. For example, the generation unit can offer the "Project Textbook Solution" on a subscription model. Alternatively, the generation unit can offer the "Project Textbook Solution" on a licensing model. Furthermore, the generation unit can offer the "Project Textbook Solution" on a customization model. This allows the "Project Textbook Solution" to be sold externally as an advanced form of textbook creation.

[0077] The reception desk can estimate the user's emotions and adjust the timing of receiving instructions to send case information based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the reception and wait until the user is relaxed. Conversely, if the user is in a hurry, the reception desk can immediately receive instructions to send case information. Furthermore, if the user is concentrating, the reception desk can accept the request at an appropriate time so as not to interrupt their concentration. In this way, the timing of receiving instructions to send case information can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception department can analyze the user's past case information submission history and select the optimal reception method. For example, the reception department may prioritize suggesting reception methods that the user has frequently used in the past. The reception department can also suggest the optimal reception method for a specific time period based on the user's past submission history. Furthermore, the reception department can analyze the user's past submission history and select the most efficient reception method. This allows the reception department to analyze the user's past case information submission history and select the optimal reception method. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI.

[0079] The reception unit can filter case information upon receiving instructions to send case information based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving only information related to the user's current projects. The reception unit can also filter and receive case information that is highly relevant based on the user's areas of interest. Furthermore, the reception unit can prioritize receiving information of high importance depending on the user's project status. This allows for filtering of case information based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0080] The reception desk can estimate the user's emotions and determine the priority of the case information to be received based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone receiving less important case information. Conversely, if the user is relaxed, the reception desk may prioritize receiving more important case information. Furthermore, if the user is in a hurry, the reception desk may prioritize receiving more urgent case information. This allows for the prioritization of case information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception unit, upon receiving instructions to send case information, can prioritize receiving highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving case information related to that region. The reception unit can also prioritize receiving nearby case information based on the user's current location. Furthermore, the reception unit can prioritize receiving highly relevant information by considering the user's travel history. This allows the reception unit to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0082] The reception department can analyze the user's social media activity and receive relevant information upon receiving instructions to send case information. For example, the reception department can analyze the content of the user's social media posts and prioritize receiving relevant case information. The reception department can also receive information of high interest based on the user's social media activity history. Furthermore, the reception department can receive highly relevant information by referring to the activities of the user's followers and friends. This allows the reception department to analyze the user's social media activity and receive relevant information. Some or all of the above processing in the reception department may be performed using AI, for example, or without using AI.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the information analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation that gets straight to the point. This allows the presentation of the information analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the case information during information analysis. For example, the analysis unit performs a detailed analysis on case information with high importance. It can also perform a simplified analysis on case information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the case information. This allows the level of detail of the analysis to be adjusted based on the importance of the case information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the case information during information analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical case information. It can also apply a business-oriented analysis algorithm to business-related case information. Furthermore, the analysis unit can select the most suitable analysis algorithm depending on the category of the case information. This allows for the application of different analysis algorithms depending on the category of the case information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can determine the priority of analysis based on the submission timing of case information during information analysis. For example, the analysis unit will prioritize the analysis of case information with an approaching submission deadline. The analysis unit can also postpone the analysis of case information whose submission deadline has passed. Furthermore, the analysis unit can adjust the analysis schedule based on the submission timing. This allows the analysis priority to be determined based on the submission timing of case information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0088] The analysis unit can adjust the order of analysis based on the relevance of case information during information analysis. For example, the analysis unit prioritizes the analysis of highly relevant case information. It can also postpone the analysis of less relevant case information. Furthermore, the analysis unit can determine the order of analysis based on the relevance of case information. This allows the order of analysis to be adjusted based on the relevance of case information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0089] The generation unit can estimate the user's emotions and adjust the textbook's presentation based on those emotions. For example, if the user is stressed, the generation unit can provide a simple and highly visual presentation. If the user is relaxed, it can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise presentation that gets straight to the point. This allows the textbook's presentation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The generation unit can adjust the level of detail in the textbook based on the importance of the analyzed information during textbook generation. For example, the generation unit can generate a textbook that includes detailed explanations for information of high importance. It can also generate a simplified textbook for information of low importance. Furthermore, the generation unit can adjust the level of detail in the textbook according to the importance of the information. This allows the level of detail in the textbook to be adjusted based on the importance of the analyzed information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0091] The generation unit can apply different generation algorithms depending on the category of the case information when generating textbooks. For example, the generation unit can apply a specialized generation algorithm to technical case information. It can also apply a business-oriented generation algorithm to business-related case information. Furthermore, the generation unit can select the most suitable generation algorithm depending on the category of the case information. This allows for the application of different generation algorithms depending on the category of the case information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0092] The generation unit can estimate the user's emotions and adjust the length of the textbook based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise textbook. If the user is relaxed, the generation unit can also generate a longer textbook with more detailed explanations. Furthermore, if the user is excited, the generation unit can generate a textbook with visually stimulating effects. This allows the length of the textbook to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The generation unit can determine the priority of textbooks based on the submission timing of the analyzed information during textbook generation. For example, the generation unit prioritizes incorporating information with approaching submission deadlines into the textbooks. It can also postpone information whose submission deadline has passed. Furthermore, the generation unit can adjust the textbook generation schedule based on the submission timing. This allows for the determination of textbook priorities based on the submission timing of the analyzed information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0094] The generation unit can adjust the order of the textbook based on the relevance of the analyzed information during textbook generation. For example, the generation unit prioritizes reflecting highly relevant information in the textbook. The generation unit can also postpone the inclusion of less relevant information. Furthermore, the generation unit can determine the order of the textbook based on the relevance of the information. This allows the order of the textbook to be adjusted based on the relevance of the analyzed information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0095] The storage unit can estimate the user's emotions and adjust the way textbooks are stored based on those emotions. For example, if the user is stressed, the storage unit can provide a simple and highly visible storage method. If the user is relaxed, the storage unit can also provide a storage method that includes detailed information. Furthermore, if the user is in a hurry, the storage unit can provide a concise storage method that gets straight to the point. This allows the storage method of textbooks to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The storage unit can select the optimal storage method when storing textbooks by referring to the user's past textbook usage history. For example, the storage unit may prioritize suggesting storage methods that the user has frequently used in the past. The storage unit can also suggest the optimal storage method for a specific time period based on the user's past usage history. Furthermore, the storage unit can analyze the user's past usage history and select the most efficient storage method. This allows the storage unit to select the optimal storage method by referring to the user's past textbook usage history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0097] The storage unit can customize the storage method based on the user's current project status when storing textbooks. For example, the storage unit can prioritize storing textbooks related to the user's current project. It can also prioritize storing textbooks of high importance depending on the user's project status. Furthermore, the storage unit can suggest the optimal storage method based on the user's project progress. This allows the storage method to be customized based on the user's current project status. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0098] The storage unit can estimate the user's emotions and determine the storage priority of textbooks based on the estimated emotions. For example, if the user is stressed, the storage unit will postpone storing less important textbooks. Conversely, if the user is relaxed, the storage unit can prioritize storing more important textbooks. Furthermore, if the user is in a hurry, the storage unit can prioritize storing more urgent textbooks. This allows the storage priority of textbooks to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The storage unit can select the optimal storage method when storing textbooks, taking into account the user's geographical location information. For example, if the user is in a specific region, the storage unit will prioritize storing textbooks related to that region. The storage unit can also prioritize storing nearby textbooks based on the user's current location. Furthermore, the storage unit can prioritize storing highly relevant textbooks by considering the user's travel history. This allows the storage unit to select the optimal storage method while taking into account the user's geographical location information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0100] The storage unit can analyze the user's social media activity and suggest storage methods when storing textbooks. For example, the storage unit can analyze the content of the user's social media posts and prioritize storing relevant textbooks. The storage unit can also store textbooks of high interest based on the user's social media activity history. Furthermore, the storage unit can store highly relevant textbooks by referring to the activities of the user's followers and friends. This allows the storage unit to analyze the user's social media activity and suggest storage methods. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can estimate the user's emotions and customize how they receive instructions to send case information based on those emotions. For example, if the user is stressed, the reception desk can provide a simpler and more intuitive interface. If the user is relaxed, it can provide an interface with more detailed options. Furthermore, if the user is in a hurry, it can provide an interface that allows for the quickest possible operation. This allows the reception desk to customize how they receive instructions to send case information based on the user's emotions.

[0103] The analysis unit can optimize its analysis algorithm by referring to the user's past analysis results when analyzing case information. For example, it can prioritize the application of analysis methods that have been successful in the past. It can also try different analysis methods to avoid past failures. Furthermore, it can adjust the analysis algorithm based on the user's past feedback. This allows the analysis algorithm to be optimized by referring to the user's past analysis results.

[0104] The generation unit can customize the content of textbooks based on the user's learning style during the textbook creation process. For example, visual learners can be provided with textbooks that make extensive use of diagrams and graphs. Auditory learners can be provided with textbooks that include audio explanations. Furthermore, practical learners can be provided with textbooks that include many real-world examples and practice problems. In this way, the content of textbooks can be customized according to the user's learning style.

[0105] The storage unit can select the optimal storage format based on the user's device environment when storing textbooks. For example, it can provide a lightweight and easy-to-read format for users using mobile devices, a format containing detailed information for users using desktop devices, and a format that is easily accessible online for users using cloud storage. This allows the system to select the optimal storage format based on the user's device environment.

[0106] The reception desk can estimate the user's emotions and adjust the frequency of receiving instructions to send case information based on those emotions. For example, if the user is stressed, the frequency of requests can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of requests can be increased to efficiently collect information. Furthermore, if the user is in a hurry, the request can be processed immediately. In this way, the frequency of receiving instructions to send case information can be adjusted based on the user's emotions.

[0107] The analysis unit can provide real-time data feedback when analyzing case information. For example, if new information is added during the analysis, it can be immediately reflected in the analysis. Furthermore, because the analysis results are updated sequentially, users can always stay informed of the latest information. In addition, real-time feedback can improve the accuracy of the analysis. This enables real-time data feedback.

[0108] The generation unit can estimate the user's emotions when creating a textbook and adjust the tone and style of the textbook based on those emotions. For example, if the user is stressed, the textbook can be created in a gentle, relaxing tone. If the user is relaxed, the textbook can be created in a detailed and professional tone. Furthermore, if the user is in a hurry, the textbook can be created in a concise and to-the-point tone. In this way, the tone and style of the textbook can be adjusted based on the user's emotions.

[0109] The storage unit can select the optimal storage method for textbooks based on the user's network environment. For example, textbooks can be provided in a lightweight file format to users with slow network environments. Conversely, textbooks can be provided in a large file format containing detailed information to users with high-speed network environments. Furthermore, textbooks can be provided in a downloadable format to users in offline environments. This allows the system to select the optimal storage method based on the user's network environment.

[0110] The reception desk can estimate the user's emotions and customize the content of the case information submission instructions based on those emotions. For example, if the user is stressed, less important information can be omitted. Conversely, if the user is relaxed, detailed information can be included in the submission. Furthermore, if the user is in a hurry, only the most important information can be submitted. This allows the content of the case information submission instructions to be customized based on the user's emotions.

[0111] The analysis unit can incorporate user feedback in real time when analyzing case information. For example, if a user adds a comment to the analysis results, that comment can be immediately reflected in the analysis. Furthermore, if a user requests a correction to the analysis results, that correction can be made in real time. In addition, the analysis algorithm can be adjusted based on user feedback. This allows for real-time incorporation of user feedback.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception department receives instructions to send case information. For example, it can receive instructions to send case information via API. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it can receive information sending instructions via API and analyze the information. Step 3: The generation unit creates a textbook based on the information analyzed by the analysis unit. For example, a generation AI can be used to create the textbook. The generation AI can create the textbook using a text generation AI (e.g., LLM). Step 4: The storage unit stores the textbooks created by the generation unit. For example, the created textbooks can be stored in a textbook file server.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives instructions to send case information via an API. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a textbook using a generation AI based on the analyzed information. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the created textbook in a textbook file server. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and storage unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives instructions to send case information via an API. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a textbook using a generation AI based on the analyzed information. The storage unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and stores the created textbook in a textbook file server. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and storage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives instructions to send case information via API. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a textbook using a generation AI based on the analyzed information. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the created textbook in a textbook file server. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and storage unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives instructions to send case information via an API. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a textbook using a generation AI based on the analyzed information. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the created textbook in a textbook file server. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The reception department accepts instructions to send case information, An analysis unit that analyzes the information received by the reception unit, A generation unit that creates a textbook based on the information analyzed by the analysis unit, The system includes a storage unit for storing the textbooks created by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept instructions to send project information via API. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system receives instructions to send information via an API and analyzes that information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Create textbooks based on the analyzed information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned storage unit is The created textbooks are stored on the textbook file server. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Textbooks will be updated. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is As an advanced form of textbook creation, we will sell "Project Textbook Solution" to external customers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving instructions to send case information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past case information submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a request to send project information, the system filters it based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the case information to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions to send case information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving instructions to send case information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way information analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During information analysis, the level of detail of the analysis is adjusted based on the importance of the case information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing information, different analysis algorithms are applied depending on the category of the case information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing information, the priority of the analysis is determined based on when the case information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During information analysis, the order of analysis is adjusted based on the relevance of the case information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system estimates the user's emotions and adjusts the textbook's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating textbooks, adjust the level of detail in the textbooks based on the importance of the analyzed information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating textbooks, different generation algorithms are applied depending on the category of the case information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is The system estimates the user's emotions and adjusts the length of the textbook based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating textbooks, the priority of the textbooks is determined based on the timing of submission of the analyzed information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating textbooks, the order of the textbooks is adjusted based on the relevance of the analyzed information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned storage unit is The system estimates the user's emotions and adjusts how textbooks are stored based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned storage unit is When storing textbooks, the system selects the optimal storage method by referring to the user's past textbook usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned storage unit is When storing textbooks, customize the storage method based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage unit is The system estimates the user's emotions and determines the storage priority of textbooks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage unit is When storing textbooks, the system selects the optimal storage method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage unit is When storing textbooks, we analyze users' social media activity and propose storage methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception department accepts instructions to send case information, An analysis unit that analyzes the information received by the reception unit, A generation unit that creates a textbook based on the information analyzed by the analysis unit, The system includes a storage unit for storing the textbooks created by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept instructions to send project information via API. The system according to feature 1.

3. The aforementioned analysis unit, The system receives instructions to send information via an API and analyzes that information. The system according to feature 1.

4. The generating unit is Create textbooks based on the analyzed information. The system according to feature 1.

5. The aforementioned storage unit is The created textbooks are stored on the textbook file server. The system according to feature 1.

6. The generating unit is Textbooks will be updated. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving instructions to send case information based on those estimated emotions. The system according to feature 1.

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