system

Abstract

The system according to the embodiment aims to digitize business manuals and enable users to quickly obtain appropriate information. [Solution] A system according to an embodiment includes a digitizing unit, an analyzing unit, a receiving unit, a providing unit, an updating unit, and a learning unit. The digitizing unit digitizes a business manual. The analyzing unit analyzes the business manual digitized by the digitizing unit. The receiving unit receives questions from users based on the content analyzed by the analyzing unit. The providing unit provides answers to the questions received by the receiving unit. The updating unit automatically updates new rules and manuals based on the answers provided by the providing unit. The learning unit learns the content updated by the updating unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to quickly obtain appropriate information because there are a large number of business manuals and rules differ from company to company.

[0005] The system according to the embodiment aims to digitize business manuals and enable users to quickly obtain appropriate information. [Means for solving the problem]

[0006] The system according to the embodiment includes a digitizing unit, an analyzing unit, a receiving unit, a providing unit, an updating unit, and a learning unit. The digitizing unit digitizes the business manual. The analyzing unit analyzes the business manual digitized by the digitizing unit. The receiving unit receives questions from users based on the content analyzed by the analyzing unit. The providing unit provides answers to the questions received by the receiving unit. The updating unit automatically updates new rules and manuals based on the answers provided by the providing unit. The learning unit learns the content updated by the updating unit. [Effects of the Invention]

[0007] The system according to the embodiment can digitize business manuals and enable users to quickly obtain appropriate information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The business manual support system according to an embodiment of the present invention uses AI to provide user support in a situation where a large number of internal business manuals exist and the number of unique rules varies from company to company. This business manual support system first digitizes each business manual and inputs it into an AI. The AI ​​then analyzes these manuals and understands their contents. When a user inputs a question about a specific business task, the AI ​​searches the relevant manual content and provides an appropriate answer. For example, in response to a question such as "Please tell me the procedure for a specific business task," the AI ​​extracts the relevant section of the manual and provides it to the user. Furthermore, the AI ​​learns the user's question history and can automatically generate answers to frequently asked questions. This allows users to quickly and accurately obtain the information they need. Furthermore, the AI ​​is automatically updated whenever new rules or manuals are added, ensuring that the latest information is always available. This system streamlines business manual management and allows users to quickly obtain the information they need. For example, when new employees learn business procedures, the AI ​​can provide them with the appropriate manual, improving training efficiency. Furthermore, even when different rules exist, the AI ​​can select and provide the appropriate manual, preventing confusion. This enables the business manual support system to efficiently digitize, analyze, accept questions, provide answers, update, and learn from business manuals.

[0029] A business manual support system according to an embodiment includes a digitizing unit, an analyzing unit, a receiving unit, a providing unit, an updating unit, and a learning unit. The digitizing unit digitizes a business manual. For example, the digitizing unit digitizes the business manual using scanning technology and converts it into text information using OCR technology. The digitizing unit can also directly read business manuals submitted in digital format. The digitizing unit can also read printed business manuals using OCR technology. For example, the digitizing unit scans a handwritten business manual with a high-resolution scanner and converts it into text information using OCR technology. Digital business manuals submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analyzing unit analyzes the business manual digitized by the digitizing unit. For example, the analyzing unit analyzes the content of the business manual using text analysis technology. The analyzing unit can also extract important information from the business manual using data mining technology. The analyzing unit can also understand the content of the business manual using natural language processing technology. For example, the analysis unit analyzes the contents of a business manual using text analysis technology and extracts important information. Data mining technology is a technology for extracting useful information from large amounts of data, and can efficiently extract important information from a business manual. Natural language processing technology is a technology for analyzing text data and understanding its contents, and can accurately understand the contents of a business manual. The reception unit accepts questions from users based on the content analyzed by the analysis unit. The reception unit accepts, for example, text-based questions. The reception unit can also accept questions entered by voice. The reception unit can also accept questions entered by form. For example, the reception unit accepts text-based questions and provides answers based on the content analyzed by the analysis unit. Questions entered by voice are converted into text data using voice recognition technology and analyzed by the analysis unit. Questions entered by form input are in the form in which a user enters specific items and are analyzed by the analysis unit.The providing unit provides answers to questions received by the receiving unit. The providing unit provides, for example, text-based answers. The providing unit can also provide answers by voice output. Furthermore, the providing unit can also provide visual answers using graphs or charts. For example, the providing unit provides text-based answers to provide specific information to the user. Answers by voice output are provided to the user by converting text data into voice using voice synthesis technology. Visual answers using graphs or charts are provided to the user by using data visualization technology to provide information in an easy-to-understand format. The updating unit automatically updates new rules and manuals based on the answers provided by the providing unit. The updating unit, for example, updates a database. The updating unit can also revise business manuals. Furthermore, the updating unit can add new rules and manuals. For example, the updating unit updates the database based on the answers provided by the providing unit to maintain the latest information. Revising a business manual involves modifying the content of an existing manual to reflect the latest information. Adding new rules and manuals involves adding newly emerging business procedures and rules to the manual. The learning unit learns the content updated by the updating unit. The learning unit learns the updated content using, for example, a machine learning algorithm. The learning unit can also learn the updated content using a dataset. Furthermore, the learning unit can learn the updated content using past data. For example, the learning unit learns the updated content using a machine learning algorithm to improve the accuracy of answers to future questions. Learning using a dataset learns the updated content using a specific dataset to improve the accuracy of the model. Learning using past data learns the updated content using past question history and answer history to improve the accuracy of answers to future questions. As a result, the business manual support system according to the embodiment can efficiently digitize, analyze, accept questions, provide answers, update, and learn business manuals.

[0030] The digitizing unit can scan handwritten business manuals and convert them into digital data. Digital data formats include, but are not limited to, PDF, image, and text formats. For example, the digitizing unit scans the handwritten business manuals and saves them as image data. The digitizing unit then converts the image data into text data using OCR technology. The digitizing unit can also take a photo of the handwritten business manual using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The digitizing unit can also write the handwritten business manuals with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the pen's movements and save them as character data. Converting the handwritten business manuals into digital data facilitates analysis by a generating AI. Some or all of the above-described processing in the digitizing unit can be performed using AI, for example, or without AI. For example, the digitization department can scan a handwritten business manual, obtain image data, input it into the generation AI, and have the generation AI generate text data from the image data.

[0031] The digitizing unit can analyze writing pressure and stroke order to extract the writer's characteristics. The digitizing unit, for example, uses a writing pressure sensor to analyze the writing style of the business manual. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. The digitizing unit can also track the movement of a digital pen to collect data on the writer's stroke order to analyze the stroke order. For example, the digitizing unit can analyze stroke order patterns to identify the writer's characteristics. The digitizing unit can also combine data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, the digitizing unit extracts the writer's characteristics based on changes in writing pressure and the degree of agreement between the stroke order. This allows the writer's characteristics to be understood in detail by analyzing the writing pressure and stroke order. Some or all of the above-mentioned processing in the digitizing unit may be performed using, for example, AI, or may be performed without AI. For example, the digitizing unit can input the writer's stroke order data to a generation AI and cause the generation AI to extract the writer's characteristics.

[0032] During digitization, the digitization unit can adjust the level of detail of the digitization based on the importance of the manual. For example, the digitization unit digitizes even the detailed contents of a highly important business manual. The digitization unit can also digitize only the outline of a low-importance business manual. Furthermore, the digitization unit can digitize only the necessary parts of a medium-importance business manual. For example, the digitization unit digitizes the details of a highly important business manual and stores all information as digital data. For a low-importance business manual, the digitization unit digitizes only the main points and stores the outline as digital data. For a medium-importance business manual, the digitization unit digitizes only specific important parts and stores the necessary information as digital data. This enables efficient digitization by adjusting the level of detail of the digitization according to the importance of the manual. Some or all of the above-described processing in the digitization unit may be performed using, or without, AI. For example, the digitization unit can input the importance of the business manual into AI and have the AI ​​adjust the level of detail of the digitization.

[0033] The digitization unit can apply different digitization methods depending on the category of the manual during digitization. For example, the digitization unit digitizes technical manuals in detail, including figures and tables. The digitization unit can also digitize management manuals, focusing on text. The digitization unit can also digitize operation manuals, making extensive use of videos and images. For example, the digitization unit scans technical manuals to generate detailed digital data, including figures and tables. The digitization unit digitizes management manuals primarily as text data, saving important information as text data. The digitization unit digitizes operation manuals, focusing on videos and images, generating digital data in a format that is visually easy to understand. This enables appropriate digitization by applying a digitization method appropriate for the manual's category. Some or all of the above-described processing in the digitization unit may be performed using, for example, AI, or without AI. For example, the digitization unit can input the manual's category into AI and have the AI ​​select an appropriate digitization method.

[0034] During digitization, the digitization department can determine the priority of digitization based on the submission date of the manual. For example, the digitization department can prioritize digitization of recently submitted business manuals. The digitization department can also postpone the submission of older business manuals. Furthermore, the digitization department can adjust the digitization schedule according to the submission date. For example, the digitization department can determine the priority of digitization based on the submission date and time to perform efficient digitization. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient digitization by determining the priority of digitization based on the submission date and time of the manual. Some or all of the above-described processing in the digitization department can be performed using, or without, AI. For example, the digitization department can input submission date and time data into a generation AI and have the generation AI determine the priority of digitization.

[0035] During digitization, the digitization unit can adjust the order of digitization based on the relevance of the manuals. For example, the digitization unit prioritizes digitization of highly relevant business manuals. The digitization unit can also postpone less relevant business manuals. Furthermore, the digitization unit can adjust the order of digitization according to the relevance. For example, the digitization unit determines the order of digitization based on the similarity of the content of the business manuals or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, content similarity and related topics. This enables efficient digitization by adjusting the order of digitization based on the relevance of the manuals. Some or all of the above-described processing in the digitization unit may be performed using, or without, AI. For example, the digitization unit can input relevance data of the business manuals into a generation AI and have the generation AI adjust the order of digitization.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the manual. For example, the analysis unit analyzes a business manual with high importance in detail. The analysis unit can also analyze only an outline of a business manual with low importance. The analysis unit can also analyze only the necessary parts of a business manual with medium importance. For example, the analysis unit analyzes a business manual with high importance in detail and extracts all information. For a business manual with low importance, the analysis unit analyzes only the main points and extracts an outline. For a business manual with medium importance, the analysis unit analyzes only specific important parts and extracts the necessary information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the manual. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the business manual into AI and have the AI ​​adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the category of the manual during analysis. For example, the analysis unit uses a specialized analysis algorithm for technical manuals. The analysis unit can also use a general analysis algorithm for management manuals. The analysis unit can also use a visual analysis algorithm for operation manuals. For example, the analysis unit analyzes technical manuals using a specialized analysis algorithm to extract detailed information. The analysis unit analyzes management manuals using a general analysis algorithm to extract important information. The analysis unit analyzes operation manuals using a visual analysis algorithm to extract information in a format that is visually easy to understand. This enables appropriate analysis by applying an analysis algorithm depending on the category of the manual. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the manual into AI and have the AI ​​select an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can determine the analysis priority based on the submission date of the manual. For example, the analysis unit prioritizes analysis of recently submitted business manuals. The analysis unit can also postpone analysis of older submitted business manuals. Furthermore, the analysis unit can adjust the analysis schedule according to the submission date. For example, the analysis unit determines the analysis priority based on the submission date and time to perform efficient analysis. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient analysis by determining the analysis priority based on the submission date and time of the manual. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input submission date and time data to the generation AI and have the generation AI determine the analysis priority.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the manuals. For example, the analysis unit prioritizes analysis of highly relevant business manuals. The analysis unit can also postpone analysis of less relevant business manuals. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. For example, the analysis unit determines the order of analysis based on the similarity of the contents of the business manuals or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the contents and related topics. This enables efficient analysis by adjusting the order of analysis based on the relevance of the manuals. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the business manuals to the generation AI and have the generation AI adjust the order of analysis.

[0040] The reception unit can adjust the level of detail of the reception based on the importance of the question when receiving the question. For example, the reception unit receives questions of high importance in detail. The reception unit can also receive only an outline of questions of low importance. The reception unit can also receive only the necessary parts of questions of medium importance. For example, the reception unit receives questions of high importance in detail and collects all information. For questions of low importance, the reception unit receives only the main points and collects an outline. For questions of medium importance, the reception unit receives only specific important parts and collects the necessary information. This allows for efficient question reception by adjusting the level of detail of the reception according to the importance of the question. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the importance of the question into AI and have the AI ​​adjust the level of detail of the reception.

[0041] The reception unit can apply different reception methods depending on the category of the question when receiving the question. For example, the reception unit uses a specialized reception method for technical questions. The reception unit can also use a general reception method for administrative questions. The reception unit can also use a visual reception method for questions regarding operations. For example, the reception unit can use a specialized reception method to receive technical questions and collect detailed information. The reception unit can use a general reception method to receive administrative questions and collect important information. The reception unit can use a visual reception method to receive questions regarding operations and collect information in a visually easy-to-understand format. This allows appropriate question reception by applying a reception method depending on the question category. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the question category into AI and have the AI ​​select an appropriate reception method.

[0042] The reception unit can determine the reception priority based on the time of submission of the question at the time of reception. For example, the reception unit can prioritize recently submitted questions. The reception unit can also postpone questions submitted earlier. Furthermore, the reception unit can adjust the reception schedule according to the time of submission. For example, the reception unit can determine the reception priority based on the submission date and time to efficiently receive questions. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient question reception by determining the reception priority based on the time of submission of the question. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input submission date and time data to a generation AI and have the generation AI determine the reception priority.

[0043] The reception unit can adjust the reception order based on the relevance of questions when receiving them. For example, the reception unit prioritizes receiving highly relevant questions. The reception unit can also postpone less relevant questions. Furthermore, the reception unit can adjust the reception order according to the relevance. For example, the reception unit determines the reception order based on the similarity of the question content or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the content and related topics. This enables efficient question reception by adjusting the reception order based on the relevance of questions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input question relevance data to a generation AI and have the generation AI adjust the reception order.

[0044] The providing unit can adjust the level of detail of the answer based on the importance of the question when providing the answer. For example, the providing unit provides a detailed answer to a question of high importance. The providing unit can also provide only a summary of the answer to a question of low importance. Furthermore, the providing unit can provide only the necessary parts of the answer to a question of medium importance. For example, the providing unit provides a detailed answer to a question of high importance and provides all information. For a question of low importance, the providing unit provides only the main points and provides a summary. For a question of medium importance, the providing unit provides only specific important parts and provides the necessary information. This enables efficient answer provision by adjusting the level of detail of the answer depending on the importance of the question. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the question to AI and have the AI ​​adjust the level of detail of the answer.

[0045] The providing unit can apply different answering algorithms depending on the question category when providing the question. For example, the providing unit uses a specialized answering algorithm for technical questions. The providing unit can also use a general answering algorithm for administrative questions. Furthermore, the providing unit can also use a visual answering algorithm for questions regarding operations. For example, the providing unit can use a specialized answering algorithm to answer technical questions and provide detailed information. For administrative questions, the providing unit can use a general answering algorithm to answer and provide important information. For operational questions, the providing unit can use a visual answering algorithm to answer and provide information in a visually easy-to-understand format. This enables an appropriate answer to be provided by applying an answering algorithm depending on the question category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the question category into AI and have the AI ​​select an appropriate answering algorithm.

[0046] The providing unit can determine the priority of answers based on the time of submission of the question when providing the answers. For example, the providing unit prioritizes answers to recently submitted questions. The providing unit can also postpone questions submitted earlier. Furthermore, the providing unit can adjust the answer schedule according to the time of submission. For example, the providing unit determines the priority of answers based on the submission date and time, thereby providing answers efficiently. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient answer provision by determining the priority of answers based on the time of submission of the question. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input submission date and time data into a generation AI and have the generation AI determine the priority of answers.

[0047] The providing unit can adjust the order of answers based on the relevance of the questions when providing the answers. For example, the providing unit prioritizes answers to highly relevant questions. The providing unit can also postpone questions with low relevance. Furthermore, the providing unit can adjust the order of answers according to the relevance. For example, the providing unit determines the order of answers based on the similarity of the content of the questions or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the content and related topics. This enables efficient answer provision by adjusting the order of answers based on the relevance of the questions. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of the answers.

[0048] The update unit can adjust the level of detail of the update based on the importance of the manual when updating. For example, the update unit updates a business manual with high importance in detail. The update unit can also update only the outline of a business manual with low importance. The update unit can also update only the necessary parts of a business manual with medium importance. For example, the update unit updates a business manual with high importance in detail and updates all information. For a business manual with low importance, the update unit updates only the main points and updates the outline. For a business manual with medium importance, the update unit updates only specific important parts and updates the necessary information. This enables efficient updating by adjusting the level of detail of the update according to the importance of the manual. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the importance of the business manual into AI and have the AI ​​adjust the level of detail of the update.

[0049] The update unit can apply different update methods depending on the category of the manual when updating. For example, the update unit uses a specialized update method for a technical manual. The update unit can also use a general update method for an administrative manual. The update unit can also use a visual update method for an operation manual. For example, the update unit updates the technical manual using a specialized update method to update detailed information. The update unit updates the administrative manual using a general update method to update important information. The update unit updates the operation manual using a visual update method to update information in a format that is visually easy to understand. This enables appropriate updates by applying an update method depending on the category of the manual. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the category of the manual into AI and have the AI ​​select an appropriate update method.

[0050] The update unit can determine the priority of updates based on the submission date of the manuals when updating. For example, the update unit prioritizes updating the most recently submitted business manuals. The update unit can also postpone updating business manuals that were submitted earlier. Furthermore, the update unit can adjust the update schedule according to the submission date. For example, the update unit determines the priority of updates based on the submission date and time, and performs efficient updates. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient updates by determining the priority of updates based on the submission date and time of the manuals. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit can input submission date and time data into the generation AI and have the generation AI determine the priority of updates.

[0051] The update unit can adjust the update order based on the relevance of the manuals when updating. For example, the update unit prioritizes updating highly relevant business manuals. The update unit can also postpone updating less relevant business manuals. Furthermore, the update unit can adjust the update order according to the relevance. For example, the update unit determines the update order based on the similarity of the contents of the business manuals or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the contents and related topics. This enables efficient updating by adjusting the update order based on the relevance of the manuals. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit can input relevance data of the business manuals to a generation AI and have the generation AI adjust the update order.

[0052] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also extract effective learning patterns from past learning data. Furthermore, the learning unit can analyze past learning data and improve the learning algorithm. For example, the learning unit selects an optimal learning algorithm based on past learning data and performs efficient learning. Methods and criteria for referring to past learning data include, but are not limited to, the type of data, the quality of data, and the amount of data. This enables efficient learning by adjusting the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to adjust the learning algorithm.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The business manual support system may further include a feedback unit. The feedback unit may collect feedback from users and use the collected feedback to improve the system. For example, the feedback unit may have a function for users to evaluate their level of satisfaction with the answers they have provided. The feedback unit may also have a function for users to leave comments on the answers they have provided. Furthermore, the feedback unit may analyze the collected feedback and identify areas for improvement in the system. This allows the business manual support system to be continuously improved based on user feedback and provide higher levels of satisfaction.

[0055] The digitization department may be equipped with a function for evaluating the quality of digitized business manuals. For example, the digitization department may evaluate the accuracy of converted text data using OCR technology. The digitization department may also evaluate the resolution and clarity of scanned image data. Furthermore, the digitization department may evaluate the consistency of the digitized data and make corrections as necessary. This ensures the quality of digitized business manuals and improves the reliability of the information provided to users.

[0056] The digitization department can have a function for adding metadata to digitized business manuals. For example, the digitization department can add metadata such as the creation date, creator, and version information of the business manual. The digitization department can also add keywords and tags based on the content of the business manual. Furthermore, the digitization department can add metadata based on the category of the business manual and related topics. This improves the searchability of the digitized business manual, allowing users to quickly find the information they need.

[0057] The digitizing unit can be equipped with a function to enhance the security of the digitized business manual. For example, the digitizing unit can encrypt the digitized data. The digitizing unit can also perform access control on the digitized data. Furthermore, the digitizing unit can be equipped with a function to detect tampering with the digitized data. This enhances the security of the digitized business manual and prevents leaks of confidential information and unauthorized access.

[0058] The digitizing unit can be equipped with a function to improve the accessibility of the digitized business manual. For example, the digitizing unit can provide a voice reading function for the visually impaired. The digitizing unit can also provide sign language videos for the hearing impaired. Furthermore, the digitizing unit can apply a color filter for the color-blind. This makes the digitized business manual easier to use for a variety of users, improving accessibility.

[0059] The digitization department can be equipped with a version management function for digitized business manuals. For example, the digitization department can save each version of the business manual and track the change history. The digitization department can also provide a function for reverting to a specific version. Furthermore, the digitization department can be equipped with a function for displaying the differences between versions. This makes it easier to manage the versions of digitized business manuals, and makes it possible to track the change history and restore to a specific version.

[0060] The digitization unit may have a function for supporting multiple languages ​​for the digitized business manual. For example, the digitization unit may provide a function for translating the business manual into multiple languages. The digitization unit may also have a function for evaluating the quality of the translated business manual. Furthermore, the digitization unit may provide a function for displaying the business manual based on the language selected by the user. This makes the digitized business manual multilingual and suitable for international users.

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

[0062] Step 1: The digitization department digitizes the business manual. For example, the digitization department digitizes the business manual using scanning technology and converts it into text information using OCR technology. The digitization department can also directly read business manuals submitted in digital format. Furthermore, the digitization department can also read printed business manuals using OCR technology. For example, the digitization department scans handwritten business manuals with a high-resolution scanner and converts them into text information using OCR technology. Digital business manuals submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The analysis unit analyzes the business manual digitized by the digitization unit. The analysis unit analyzes the contents of the business manual using, for example, text analysis technology. The analysis unit can also extract important information from the business manual using data mining technology. Furthermore, the analysis unit can understand the contents of the business manual using natural language processing technology. For example, the analysis unit analyzes the contents of the business manual using text analysis technology and extracts important information. Data mining technology is a technology that extracts useful information from large amounts of data, and can efficiently extract important information from the business manual. Natural language processing technology is a technology that analyzes text data and understands its content, and can accurately understand the contents of the business manual. Step 3: The reception unit receives a user's question based on the content analyzed by the analysis unit. The reception unit receives, for example, a text-based question. The reception unit can also receive a question entered by voice. The reception unit can also receive a question entered by form. For example, the reception unit receives a text-based question and provides an answer based on the content analyzed by the analysis unit. A question entered by voice is converted into text data using voice recognition technology and analyzed by the analysis unit. A question entered by form is in the form in which the user enters specific items and is analyzed by the analysis unit. Step 4: The providing unit provides an answer to the question received by the receiving unit. The providing unit provides, for example, a text-based answer. The providing unit can also provide an answer by voice output. Furthermore, the providing unit can provide a visual answer using a graph or chart. For example, the providing unit provides a text-based answer to provide specific information to the user. An answer by voice output is provided to the user by converting text data into voice using voice synthesis technology. A visual answer using a graph or chart is provided to the user by using data visualization technology to provide information in an easy-to-understand format. Step 5: The update unit automatically updates new rules and manuals based on the answers provided by the provision unit. The update unit, for example, updates the database. The update unit can also revise the business manual. The update unit can also add new rules and manuals. For example, the update unit updates the database based on the answers provided by the provision unit to maintain the latest information. Revising the business manual involves modifying the contents of the existing manual to reflect the latest information. Adding new rules and manuals involves adding newly emerging business procedures and rules to the manual. Step 6: The learning unit learns the content updated by the update unit. The learning unit learns the updated content using, for example, a machine learning algorithm. The learning unit can also learn the updated content using a dataset. Furthermore, the learning unit can learn the updated content using past data. For example, the learning unit learns the updated content using a machine learning algorithm to improve the accuracy of answers to future questions. Learning using a dataset learns the updated content using a specific dataset to improve the accuracy of the model. Learning using past data learns the updated content using past question history and answer history to improve the accuracy of answers to future questions.

[0063] (Example 2) The business manual support system according to an embodiment of the present invention uses AI to provide user support in a situation where a large number of internal business manuals exist and the number of unique rules varies from company to company. This business manual support system first digitizes each business manual and inputs it into an AI. The AI ​​then analyzes these manuals and understands their contents. When a user inputs a question about a specific business task, the AI ​​searches the relevant manual content and provides an appropriate answer. For example, in response to a question such as "Please tell me the procedure for a specific business task," the AI ​​extracts the relevant section of the manual and provides it to the user. Furthermore, the AI ​​learns the user's question history and can automatically generate answers to frequently asked questions. This allows users to quickly and accurately obtain the information they need. Furthermore, the AI ​​is automatically updated whenever new rules or manuals are added, ensuring that the latest information is always available. This system streamlines business manual management and allows users to quickly obtain the information they need. For example, when new employees learn business procedures, the AI ​​can provide them with the appropriate manual, improving training efficiency. Furthermore, even when different rules exist, the AI ​​can select and provide the appropriate manual, preventing confusion. This enables the business manual support system to efficiently digitize, analyze, accept questions, provide answers, update, and learn from business manuals.

[0064] A business manual support system according to an embodiment includes a digitizing unit, an analyzing unit, a receiving unit, a providing unit, an updating unit, and a learning unit. The digitizing unit digitizes a business manual. For example, the digitizing unit digitizes the business manual using scanning technology and converts it into text information using OCR technology. The digitizing unit can also directly read business manuals submitted in digital format. The digitizing unit can also read printed business manuals using OCR technology. For example, the digitizing unit scans a handwritten business manual with a high-resolution scanner and converts it into text information using OCR technology. Digital business manuals submitted in a specific file format can also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analyzing unit analyzes the business manual digitized by the digitizing unit. For example, the analyzing unit analyzes the content of the business manual using text analysis technology. The analyzing unit can also extract important information from the business manual using data mining technology. The analyzing unit can also understand the content of the business manual using natural language processing technology. For example, the analysis unit analyzes the contents of a business manual using text analysis technology and extracts important information. Data mining technology is a technology for extracting useful information from large amounts of data, and can efficiently extract important information from a business manual. Natural language processing technology is a technology for analyzing text data and understanding its contents, and can accurately understand the contents of a business manual. The reception unit accepts questions from users based on the content analyzed by the analysis unit. The reception unit accepts, for example, text-based questions. The reception unit can also accept questions entered by voice. The reception unit can also accept questions entered by form. For example, the reception unit accepts text-based questions and provides answers based on the content analyzed by the analysis unit. Questions entered by voice are converted into text data using voice recognition technology and analyzed by the analysis unit. Questions entered by form input are in the form in which a user enters specific items and are analyzed by the analysis unit.The providing unit provides answers to questions received by the receiving unit. The providing unit provides, for example, text-based answers. The providing unit can also provide answers by voice output. Furthermore, the providing unit can also provide visual answers using graphs or charts. For example, the providing unit provides text-based answers to provide specific information to the user. Answers by voice output are provided to the user by converting text data into voice using voice synthesis technology. Visual answers using graphs or charts are provided to the user by using data visualization technology to provide information in an easy-to-understand format. The updating unit automatically updates new rules and manuals based on the answers provided by the providing unit. The updating unit, for example, updates a database. The updating unit can also revise business manuals. Furthermore, the updating unit can add new rules and manuals. For example, the updating unit updates the database based on the answers provided by the providing unit to maintain the latest information. Revising a business manual involves modifying the content of an existing manual to reflect the latest information. Adding new rules and manuals involves adding newly emerging business procedures and rules to the manual. The learning unit learns the content updated by the updating unit. The learning unit learns the updated content using, for example, a machine learning algorithm. The learning unit can also learn the updated content using a dataset. Furthermore, the learning unit can learn the updated content using past data. For example, the learning unit learns the updated content using a machine learning algorithm to improve the accuracy of answers to future questions. Learning using a dataset learns the updated content using a specific dataset to improve the accuracy of the model. Learning using past data learns the updated content using past question history and answer history to improve the accuracy of answers to future questions. As a result, the business manual support system according to the embodiment can efficiently digitize, analyze, accept questions, provide answers, update, and learn business manuals.

[0065] The digitizing unit can scan handwritten business manuals and convert them into digital data. Digital data formats include, but are not limited to, PDF, image, and text formats. For example, the digitizing unit scans the handwritten business manuals and saves them as image data. The digitizing unit then converts the image data into text data using OCR technology. The digitizing unit can also take a photo of the handwritten business manual using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The digitizing unit can also write the handwritten business manuals with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the pen's movements and save them as character data. Converting the handwritten business manuals into digital data facilitates analysis by a generating AI. Some or all of the above-described processing in the digitizing unit can be performed using AI, for example, or without AI. For example, the digitization department can scan a handwritten business manual, obtain image data, input it into the generation AI, and have the generation AI generate text data from the image data.

[0066] The digitizing unit can analyze writing pressure and stroke order to extract the writer's characteristics. The digitizing unit, for example, uses a writing pressure sensor to analyze the writing style of the business manual. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. The digitizing unit can also track the movement of a digital pen to collect data on the writer's stroke order to analyze the stroke order. For example, the digitizing unit can analyze stroke order patterns to identify the writer's characteristics. The digitizing unit can also combine data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, the digitizing unit extracts the writer's characteristics based on changes in writing pressure and the degree of agreement between the stroke order. This allows the writer's characteristics to be understood in detail by analyzing the writing pressure and stroke order. Some or all of the above-mentioned processing in the digitizing unit may be performed using, for example, AI, or may be performed without AI. For example, the digitizing unit can input the writer's stroke order data to a generation AI and cause the generation AI to extract the writer's characteristics.

[0067] The digitization unit can estimate a user's emotions and determine the priority of digitization based on the estimated user emotions. For example, if the user is feeling stressed, the digitization unit can prioritize digitizing important business manuals. Furthermore, if the user is relaxed, the digitization unit can also proceed with digitization while taking into account the overall balance. Furthermore, if the user is in a hurry, the digitization unit can prioritize digitizing the most frequently used business manuals. For example, the digitization unit can estimate a user's emotions and adjust the priority of digitization based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the digitization unit can be performed using, for example, AI, or without AI. For example, the digitization unit can input user emotion data into the generation AI and have the generation AI determine the priority of digitization.

[0068] During digitization, the digitization unit can adjust the level of detail of the digitization based on the importance of the manual. For example, the digitization unit digitizes even the detailed contents of a highly important business manual. The digitization unit can also digitize only the outline of a low-importance business manual. Furthermore, the digitization unit can digitize only the necessary parts of a medium-importance business manual. For example, the digitization unit digitizes the details of a highly important business manual and stores all information as digital data. For a low-importance business manual, the digitization unit digitizes only the main points and stores the outline as digital data. For a medium-importance business manual, the digitization unit digitizes only specific important parts and stores the necessary information as digital data. This enables efficient digitization by adjusting the level of detail of the digitization according to the importance of the manual. Some or all of the above-described processing in the digitization unit may be performed using, or without, AI. For example, the digitization unit can input the importance of the business manual into AI and have the AI ​​adjust the level of detail of the digitization.

[0069] The digitization unit can apply different digitization methods depending on the category of the manual during digitization. For example, the digitization unit digitizes technical manuals in detail, including figures and tables. The digitization unit can also digitize management manuals, focusing on text. The digitization unit can also digitize operation manuals, making extensive use of videos and images. For example, the digitization unit scans technical manuals to generate detailed digital data, including figures and tables. The digitization unit digitizes management manuals primarily as text data, saving important information as text data. The digitization unit digitizes operation manuals, focusing on videos and images, generating digital data in a format that is visually easy to understand. This enables appropriate digitization by applying a digitization method appropriate for the manual's category. Some or all of the above-described processing in the digitization unit may be performed using, for example, AI, or without AI. For example, the digitization unit can input the manual's category into AI and have the AI ​​select an appropriate digitization method.

[0070] The digitization unit can estimate the user's emotions and adjust the timing of digitization based on the estimated user emotions. For example, if the user is stressed, the digitization unit can quickly perform digitization. Furthermore, if the user is relaxed, the digitization unit can also flexibly adjust the timing of digitization. Furthermore, if the user is in a hurry, the digitization unit can immediately start digitization. For example, the digitization unit can estimate the user's emotions and adjust the timing of digitization based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the digitization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the digitization unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of digitization.

[0071] During digitization, the digitization department can determine the priority of digitization based on the submission date of the manual. For example, the digitization department can prioritize digitization of recently submitted business manuals. The digitization department can also postpone the submission of older business manuals. Furthermore, the digitization department can adjust the digitization schedule according to the submission date. For example, the digitization department can determine the priority of digitization based on the submission date and time to perform efficient digitization. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient digitization by determining the priority of digitization based on the submission date and time of the manual. Some or all of the above-described processing in the digitization department can be performed using, or without, AI. For example, the digitization department can input submission date and time data into a generation AI and have the generation AI determine the priority of digitization.

[0072] During digitization, the digitization unit can adjust the order of digitization based on the relevance of the manuals. For example, the digitization unit prioritizes digitization of highly relevant business manuals. The digitization unit can also postpone less relevant business manuals. Furthermore, the digitization unit can adjust the order of digitization according to the relevance. For example, the digitization unit determines the order of digitization based on the similarity of the content of the business manuals or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, content similarity and related topics. This enables efficient digitization by adjusting the order of digitization based on the relevance of the manuals. Some or all of the above-described processing in the digitization unit may be performed using, or without, AI. For example, the digitization unit can input relevance data of the business manuals into a generation AI and have the generation AI adjust the order of digitization.

[0073] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit uses a simple presentation when the user is stressed. The analysis unit can also use a detailed presentation when the user is relaxed. Furthermore, the analysis unit can use a more concise presentation when the user is in a hurry. For example, the analysis unit estimates the user's emotions and adjusts the way the analysis is presented based on the estimated emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the manual. For example, the analysis unit analyzes a business manual with high importance in detail. The analysis unit can also analyze only an outline of a business manual with low importance. The analysis unit can also analyze only the necessary parts of a business manual with medium importance. For example, the analysis unit analyzes a business manual with high importance in detail and extracts all information. For a business manual with low importance, the analysis unit analyzes only the main points and extracts an outline. For a business manual with medium importance, the analysis unit analyzes only specific important parts and extracts the necessary information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the manual. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the business manual into AI and have the AI ​​adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the manual during analysis. For example, the analysis unit uses a specialized analysis algorithm for technical manuals. The analysis unit can also use a general analysis algorithm for management manuals. The analysis unit can also use a visual analysis algorithm for operation manuals. For example, the analysis unit analyzes technical manuals using a specialized analysis algorithm to extract detailed information. The analysis unit analyzes management manuals using a general analysis algorithm to extract important information. The analysis unit analyzes operation manuals using a visual analysis algorithm to extract information in a format that is visually easy to understand. This enables appropriate analysis by applying an analysis algorithm depending on the category of the manual. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the manual into AI and have the AI ​​select an appropriate analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can perform a short analysis when the user is stressed. The analysis unit can also perform a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform a short analysis that focuses on the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the analysis priority based on the submission date of the manual. For example, the analysis unit prioritizes analysis of recently submitted business manuals. The analysis unit can also postpone analysis of older submitted business manuals. Furthermore, the analysis unit can adjust the analysis schedule according to the submission date. For example, the analysis unit determines the analysis priority based on the submission date and time to perform efficient analysis. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient analysis by determining the analysis priority based on the submission date and time of the manual. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input submission date and time data to the generation AI and have the generation AI determine the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the manuals. For example, the analysis unit prioritizes analysis of highly relevant business manuals. The analysis unit can also postpone analysis of less relevant business manuals. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. For example, the analysis unit determines the order of analysis based on the similarity of the contents of the business manuals or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the contents and related topics. This enables efficient analysis by adjusting the order of analysis based on the relevance of the manuals. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the business manuals to the generation AI and have the generation AI adjust the order of analysis.

[0079] The reception unit can estimate the user's emotions and adjust the method of accepting questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept questions. For example, the reception unit can estimate the user's emotions and adjust the method of accepting questions based on the estimated emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of accepting questions.

[0080] The reception unit can adjust the level of detail of the reception based on the importance of the question when receiving the question. For example, the reception unit receives questions of high importance in detail. The reception unit can also receive only an outline of questions of low importance. The reception unit can also receive only the necessary parts of questions of medium importance. For example, the reception unit receives questions of high importance in detail and collects all information. For questions of low importance, the reception unit receives only the main points and collects an outline. For questions of medium importance, the reception unit receives only specific important parts and collects the necessary information. This allows for efficient question reception by adjusting the level of detail of the reception according to the importance of the question. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the importance of the question into AI and have the AI ​​adjust the level of detail of the reception.

[0081] The reception unit can apply different reception methods depending on the category of the question when receiving the question. For example, the reception unit uses a specialized reception method for technical questions. The reception unit can also use a general reception method for administrative questions. The reception unit can also use a visual reception method for questions regarding operations. For example, the reception unit can use a specialized reception method to receive technical questions and collect detailed information. The reception unit can use a general reception method to receive administrative questions and collect important information. The reception unit can use a visual reception method to receive questions regarding operations and collect information in a visually easy-to-understand format. This allows appropriate question reception by applying a reception method depending on the question category. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the question category into AI and have the AI ​​select an appropriate reception method.

[0082] The reception unit can estimate the user's emotions and prioritize questions based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize questions with a high level of importance. Furthermore, when the user is relaxed, the reception unit can also prioritize questions based on the overall balance. Furthermore, when the user is in a hurry, the reception unit can prioritize questions that are most frequently used. For example, the reception unit can estimate the user's emotions and prioritize questions based on the estimated emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0083] The reception unit can determine the reception priority based on the time of submission of the question at the time of reception. For example, the reception unit can prioritize recently submitted questions. The reception unit can also postpone questions submitted earlier. Furthermore, the reception unit can adjust the reception schedule according to the time of submission. For example, the reception unit can determine the reception priority based on the submission date and time to efficiently receive questions. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient question reception by determining the reception priority based on the time of submission of the question. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input submission date and time data to a generation AI and have the generation AI determine the reception priority.

[0084] The reception unit can adjust the reception order based on the relevance of questions when receiving them. For example, the reception unit prioritizes receiving highly relevant questions. The reception unit can also postpone less relevant questions. Furthermore, the reception unit can adjust the reception order according to the relevance. For example, the reception unit determines the reception order based on the similarity of the question content or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the content and related topics. This enables efficient question reception by adjusting the reception order based on the relevance of questions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input question relevance data to a generation AI and have the generation AI adjust the reception order.

[0085] The providing unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, the providing unit uses a simple expression when the user is stressed. The providing unit can also use a detailed expression when the user is relaxed. Furthermore, the providing unit can also use a more concise expression when the user is in a hurry. For example, the providing unit estimates the user's emotions and adjusts the way the answer is expressed based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the answer is expressed.

[0086] The providing unit can adjust the level of detail of the answer based on the importance of the question when providing the answer. For example, the providing unit provides a detailed answer to a question of high importance. The providing unit can also provide only a summary of the answer to a question of low importance. Furthermore, the providing unit can provide only the necessary parts of the answer to a question of medium importance. For example, the providing unit provides a detailed answer to a question of high importance and provides all information. For a question of low importance, the providing unit provides only the main points and provides a summary. For a question of medium importance, the providing unit provides only specific important parts and provides the necessary information. This enables efficient answer provision by adjusting the level of detail of the answer depending on the importance of the question. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the question to AI and have the AI ​​adjust the level of detail of the answer.

[0087] The providing unit can apply different answering algorithms depending on the question category when providing the question. For example, the providing unit uses a specialized answering algorithm for technical questions. The providing unit can also use a general answering algorithm for administrative questions. Furthermore, the providing unit can also use a visual answering algorithm for questions regarding operations. For example, the providing unit can use a specialized answering algorithm to answer technical questions and provide detailed information. For administrative questions, the providing unit can use a general answering algorithm to answer and provide important information. For operational questions, the providing unit can use a visual answering algorithm to answer and provide information in a visually easy-to-understand format. This enables an appropriate answer to be provided by applying an answering algorithm depending on the question category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the question category into AI and have the AI ​​select an appropriate answering algorithm.

[0088] The providing unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, the providing unit can provide a short answer when the user is stressed. The providing unit can also provide a detailed answer when the user is relaxed. Furthermore, the providing unit can provide a short answer that focuses on the main points when the user is in a hurry. For example, the providing unit can estimate the user's emotions and adjust the length of the answer based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the answer.

[0089] The providing unit can determine the priority of answers based on the time of submission of the question when providing the answers. For example, the providing unit prioritizes answers to recently submitted questions. The providing unit can also postpone questions submitted earlier. Furthermore, the providing unit can adjust the answer schedule according to the time of submission. For example, the providing unit determines the priority of answers based on the submission date and time, thereby providing answers efficiently. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient answer provision by determining the priority of answers based on the time of submission of the question. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input submission date and time data into a generation AI and have the generation AI determine the priority of answers.

[0090] The providing unit can adjust the order of answers based on the relevance of the questions when providing the answers. For example, the providing unit prioritizes answers to highly relevant questions. The providing unit can also postpone questions with low relevance. Furthermore, the providing unit can adjust the order of answers according to the relevance. For example, the providing unit determines the order of answers based on the similarity of the content of the questions or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the content and related topics. This enables efficient answer provision by adjusting the order of answers based on the relevance of the questions. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of the answers.

[0091] The update unit can estimate the user's emotion and adjust the update method based on the estimated user's emotion. For example, the update unit can provide a simple update method when the user is stressed. The update unit can also provide a detailed update method when the user is relaxed. Furthermore, the update unit can provide a quick update method when the user is in a hurry. For example, the update unit can estimate the user's emotion and adjust the update method based on the estimated emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit can be performed using an AI, for example, or without an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the update method.

[0092] The update unit can adjust the level of detail of the update based on the importance of the manual when updating. For example, the update unit updates a business manual with high importance in detail. The update unit can also update only the outline of a business manual with low importance. The update unit can also update only the necessary parts of a business manual with medium importance. For example, the update unit updates a business manual with high importance in detail and updates all information. For a business manual with low importance, the update unit updates only the main points and updates the outline. For a business manual with medium importance, the update unit updates only specific important parts and updates the necessary information. This enables efficient updating by adjusting the level of detail of the update according to the importance of the manual. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the importance of the business manual into AI and have the AI ​​adjust the level of detail of the update.

[0093] The update unit can apply different update methods depending on the category of the manual when updating. For example, the update unit uses a specialized update method for a technical manual. The update unit can also use a general update method for an administrative manual. The update unit can also use a visual update method for an operation manual. For example, the update unit updates the technical manual using a specialized update method to update detailed information. The update unit updates the administrative manual using a general update method to update important information. The update unit updates the operation manual using a visual update method to update information in a format that is visually easy to understand. This enables appropriate updates by applying an update method depending on the category of the manual. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the category of the manual into AI and have the AI ​​select an appropriate update method.

[0094] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated user emotions. For example, when the user is stressed, the update unit prioritizes updates with a high level of importance. Furthermore, when the user is relaxed, the update unit can also perform updates while taking into consideration the overall balance. Furthermore, when the user is in a hurry, the update unit can prioritize updates that are most frequently used. For example, the update unit estimates the user's emotions and determines the priority of updates based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the update unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of updates.

[0095] The update unit can determine the priority of updates based on the submission date of the manuals when updating. For example, the update unit prioritizes updating the most recently submitted business manuals. The update unit can also postpone updating business manuals that were submitted earlier. Furthermore, the update unit can adjust the update schedule according to the submission date. For example, the update unit determines the priority of updates based on the submission date and time, and performs efficient updates. Methods and criteria for recording the submission date and time include, but are not limited to, the submission date, submission time, and submission order. This enables efficient updates by determining the priority of updates based on the submission date and time of the manuals. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit can input submission date and time data into the generation AI and have the generation AI determine the priority of updates.

[0096] The update unit can adjust the update order based on the relevance of the manuals when updating. For example, the update unit prioritizes updating highly relevant business manuals. The update unit can also postpone updating less relevant business manuals. Furthermore, the update unit can adjust the update order according to the relevance. For example, the update unit determines the update order based on the similarity of the contents of the business manuals or related topics. Methods and criteria for evaluating the relevance include, but are not limited to, similarity of the contents and related topics. This enables efficient updating by adjusting the update order based on the relevance of the manuals. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit can input relevance data of the business manuals to a generation AI and have the generation AI adjust the update order.

[0097] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, when the user is stressed, the learning unit prioritizes learning of data with high importance. Furthermore, when the user is relaxed, the learning unit can also select training data taking into account overall balance. Furthermore, when the user is in a hurry, the learning unit can prioritize learning of the most frequently used data. For example, the learning unit estimates the user's emotions and selects training data based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0098] During learning, the learning unit can adjust the learning algorithm by referring to past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. The learning unit can also extract effective learning patterns from past learning data. Furthermore, the learning unit can analyze past learning data and improve the learning algorithm. For example, the learning unit selects an optimal learning algorithm based on past learning data and performs efficient learning. Methods and criteria for referring to past learning data include, but are not limited to, the type of data, the quality of data, and the amount of data. This enables efficient learning by adjusting the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to adjust the learning algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the digitization unit, analysis unit, reception unit, provision unit, update unit, and learning unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the digitization unit scans an operation manual using the camera 42 of the smart device 14 and converts the scanned data into text information using OCR technology. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the digitized operation manual. The reception unit accepts user questions using the reception device 38 of the smart device 14. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and generates appropriate answers and provides them to the user via the output device 40 of the smart device 14. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically updates new rules and manuals. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the updated content. === Hard Collateral 1-2 === Each of the multiple elements, including the digitization unit, analysis unit, reception unit, provision unit, update unit, and learning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the digitization unit scans an operation manual using the camera 42 of the smart glasses 214 and converts it into text information using OCR technology. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digitized operation manual. The reception unit receives a user's question using the microphone 238 of the smart glasses 214. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer and provides it to the user through the speaker 240 of the smart glasses 214. The update unit is realized by the specific processing unit 290 of the data processing device 12 and automatically updates new rules and manuals. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the updated content. === Hard Collateral 1-3 === Each of the multiple elements, including the digitizing unit, analyzing unit, receiving unit, providing unit, updating unit, and learning unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the digitizing unit scans an operation manual using the camera 42 of the headset-type terminal 314 and converts the scanned data into text information using OCR technology. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digitized operation manual. The receiving unit receives a user's question using the microphone 238 of the headset-type terminal 314. The providing unit is realized by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer and provides it to the user through the speaker 240 of the headset-type terminal 314. The updating unit is realized by the specific processing unit 290 of the data processing device 12 and automatically updates new rules and manuals. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the updated content. === Hard Collateral 1-4 === Each of the multiple elements, including the digitization unit, analysis unit, reception unit, provision unit, update unit, and learning unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the digitization unit scans an operation manual using the camera 42 of the robot 414 and converts the scanned data into text information using OCR technology. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the digitized operation manual. The reception unit receives user questions using the microphone 238 of the robot 414. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate answers and provides them to the user through the speaker 240 of the robot 414. The update unit is realized by the specific processing unit 290 of the data processing device 12 and automatically updates new rules and manuals. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the updated content.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The business manual support system may further include a feedback unit. The feedback unit may collect feedback from users and use the collected feedback to improve the system. For example, the feedback unit may have a function for users to evaluate their level of satisfaction with the answers they have provided. The feedback unit may also have a function for users to leave comments on the answers they have provided. Furthermore, the feedback unit may analyze the collected feedback and identify areas for improvement in the system. This allows the business manual support system to be continuously improved based on user feedback and provide higher levels of satisfaction.

[0101] The digitization department may be equipped with a function for evaluating the quality of digitized business manuals. For example, the digitization department may evaluate the accuracy of converted text data using OCR technology. The digitization department may also evaluate the resolution and clarity of scanned image data. Furthermore, the digitization department may evaluate the consistency of the digitized data and make corrections as necessary. This ensures the quality of digitized business manuals and improves the reliability of the information provided to users.

[0102] The digitization department can have a function for adding metadata to digitized business manuals. For example, the digitization department can add metadata such as the creation date, creator, and version information of the business manual. The digitization department can also add keywords and tags based on the content of the business manual. Furthermore, the digitization department can add metadata based on the category of the business manual and related topics. This improves the searchability of the digitized business manual, allowing users to quickly find the information they need.

[0103] The digitization unit can estimate the user's emotion and adjust the digitization method based on the estimated user's emotion. For example, if the user is stressed, a simplified digitization method can be provided. If the user is relaxed, a detailed digitization method can be provided. Furthermore, if the user is in a hurry, a quick digitization method can be provided. This provides an optimal digitization method according to the user's emotion, thereby improving user satisfaction.

[0104] The digitizing unit can be equipped with a function to enhance the security of the digitized business manual. For example, the digitizing unit can encrypt the digitized data. The digitizing unit can also perform access control on the digitized data. Furthermore, the digitizing unit can be equipped with a function to detect tampering with the digitized data. This enhances the security of the digitized business manual and prevents leaks of confidential information and unauthorized access.

[0105] The digitizing unit can be equipped with a function to improve the accessibility of the digitized business manual. For example, the digitizing unit can provide a voice reading function for the visually impaired. The digitizing unit can also provide sign language videos for the hearing impaired. Furthermore, the digitizing unit can apply a color filter for the color-blind. This makes the digitized business manual easier to use for a variety of users, improving accessibility.

[0106] The digitization unit can estimate the user's emotions and notify the user of the progress of digitization based on the estimated user emotions. For example, if the user is feeling stressed, the digitization unit can notify the user of a brief progress status. If the user is relaxed, the digitization unit can notify the user of a detailed progress status. Furthermore, if the user is in a hurry, the digitization unit can notify the user of only important progress status. This allows the digitization unit to provide the user with the most appropriate progress notification according to the user's emotions, thereby improving user satisfaction.

[0107] The digitization department can be equipped with a version management function for digitized business manuals. For example, the digitization department can save each version of the business manual and track the change history. The digitization department can also provide a function for reverting to a specific version. Furthermore, the digitization department can be equipped with a function for displaying the differences between versions. This makes it easier to manage the versions of digitized business manuals, and makes it possible to track the change history and restore to a specific version.

[0108] The digitization unit may have a function for supporting multiple languages ​​for the digitized business manual. For example, the digitization unit may provide a function for translating the business manual into multiple languages. The digitization unit may also have a function for evaluating the quality of the translated business manual. Furthermore, the digitization unit may provide a function for displaying the business manual based on the language selected by the user. This makes the digitized business manual multilingual and suitable for international users.

[0109] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a concise analysis result can be presented. If the user is relaxed, a detailed analysis result can be presented. Furthermore, if the user is in a hurry, a summary analysis result can be presented. This provides an optimal presentation method of the analysis results according to the user's emotions, improving user satisfaction.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The digitization department digitizes the business manual. For example, the digitization department digitizes the business manual using scanning technology and converts it into text information using OCR technology. The digitization department can also directly read business manuals submitted in digital format. Furthermore, the digitization department can also read printed business manuals using OCR technology. For example, the digitization department scans handwritten business manuals with a high-resolution scanner and converts them into text information using OCR technology. Digital business manuals submitted in a specific file format can be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: The analysis unit analyzes the business manual digitized by the digitization unit. The analysis unit analyzes the contents of the business manual using, for example, text analysis technology. The analysis unit can also extract important information from the business manual using data mining technology. Furthermore, the analysis unit can understand the contents of the business manual using natural language processing technology. For example, the analysis unit analyzes the contents of the business manual using text analysis technology and extracts important information. Data mining technology is a technology that extracts useful information from large amounts of data, and can efficiently extract important information from the business manual. Natural language processing technology is a technology that analyzes text data and understands its content, and can accurately understand the contents of the business manual. Step 3: The reception unit receives a user's question based on the content analyzed by the analysis unit. The reception unit receives, for example, a text-based question. The reception unit can also receive a question entered by voice. The reception unit can also receive a question entered by form. For example, the reception unit receives a text-based question and provides an answer based on the content analyzed by the analysis unit. A question entered by voice is converted into text data using voice recognition technology and analyzed by the analysis unit. A question entered by form is in the form in which the user enters specific items and is analyzed by the analysis unit. Step 4: The providing unit provides an answer to the question received by the receiving unit. The providing unit provides, for example, a text-based answer. The providing unit can also provide an answer by voice output. Furthermore, the providing unit can provide a visual answer using a graph or chart. For example, the providing unit provides a text-based answer to provide specific information to the user. An answer by voice output is provided to the user by converting text data into voice using voice synthesis technology. A visual answer using a graph or chart is provided to the user by using data visualization technology to provide information in an easy-to-understand format. Step 5: The update unit automatically updates new rules and manuals based on the answers provided by the provision unit. The update unit, for example, updates the database. The update unit can also revise the business manual. The update unit can also add new rules and manuals. For example, the update unit updates the database based on the answers provided by the provision unit to maintain the latest information. Revising the business manual involves modifying the contents of the existing manual to reflect the latest information. Adding new rules and manuals involves adding newly emerging business procedures and rules to the manual. Step 6: The learning unit learns the content updated by the update unit. The learning unit learns the updated content using, for example, a machine learning algorithm. The learning unit can also learn the updated content using a dataset. Furthermore, the learning unit can learn the updated content using past data. For example, the learning unit learns the updated content using a machine learning algorithm to improve the accuracy of answers to future questions. Learning using a dataset learns the updated content using a specific dataset to improve the accuracy of the model. Learning using past data learns the updated content using past question history and answer history to improve the accuracy of answers to future questions.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] 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.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0177] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A digitization department that digitizes business manuals, an analysis unit that analyzes the business manual digitized by the digitization unit; a reception unit that receives a question from a user based on the content analyzed by the analysis unit; a providing unit that provides answers to the questions received by the receiving unit; an update unit that automatically updates new rules and manuals based on the answers provided by the providing unit; a learning unit that learns the content updated by the update unit. A system characterized by:

2. The digitizing unit Estimate user sentiment and determine priorities for digitalization based on the estimated user sentiment The system of claim 1 .

3. The digitizing unit During digitization, adjust the level of digitization detail based on manual priorities The system of claim 1 .

4. The digitizing unit When digitizing, different digitization methods are applied depending on the type of manual. The system of claim 1 .

5. The digitizing unit Estimate user emotions and adjust the timing of digitization based on the estimated user emotions The system of claim 1 .

6. The digitizing unit During digitization, prioritize digitization based on the submission date and time of the manual The system of claim 1 .

7. The digitizing unit During digitization, adjust the digitization order based on the relationships between the manuals The system of claim 1 .

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions The system of claim 1 .

Images