system

Abstract

An object of a system according to an embodiment is to quickly and easily provide information on an in-house knowledge base, an industry history, on-site knowledge, specific work skills, and FAQs.SOLUTION: A system includes an information providing part, an analysis part, and a data collection part. The information providing unit uses the generated AI to automatically provide a knowledgebase of the company, an industry history, a field knowledge, a specific work skill, and an answer to the FAQ. The analysis unit provides a numerical comparison analysis and a business insight based on the information provided by the information providing unit. The data collection part collects various data acquired from the inside of a company.SELECTED DRAWING: Figure 1

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 technologies have faced the challenge of making it difficult to quickly and easily obtain information on a company's knowledge base, industry history, on-site knowledge, specific work skills, and FAQs.

[0005] The system according to the embodiment aims to quickly and easily provide information on a company's knowledge base, industry history, on-site knowledge, specific business skills, and FAQs. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, an analysis unit, and a data collection unit. The information providing unit uses a generation AI to automatically provide a company's knowledge base, industry history, on-site knowledge, specific business skills, and answers to FAQs. The analysis unit provides comparative analysis of numerical data and business insights based on the information provided by the information providing unit. The data collection unit collects various data obtained from within the company. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and easily provide information on a company's knowledge base, industry history, on-site knowledge, specific business skills, and FAQs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision platform according to an embodiment of the present invention uses generative AI to provide quick and easy access to information needed to solve problems, thereby accelerating the growth of new employees and continuing learners and supporting quick decision-making by managers and leaders.

[0029] An information provision platform according to an embodiment includes an information provision unit, an analysis unit, and a data collection unit. The information provision unit uses a generation AI to automatically provide a company's knowledge base, industry history, on-site knowledge, specific business skills, and answers to FAQs. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to generate appropriate answers to user questions. The information provision unit generates answers based on prompts containing instructions from the user about what the generation AI wants the user to do. The analysis unit provides comparative analysis of numerical data and business insights based on the information provided by the information provision unit. For example, the analysis unit analyzes sales data and market data to provide the current state of the business and future outlook. The analysis unit can also analyze data using statistical methods and machine learning. The data collection unit collects various data from within the company. For example, the data collection unit collects necessary information from internal documents and databases, analyzes it, and provides it to the user. The data collection unit can also automatically evaluate the quality of the data, allowing only reliable data to be used. As a result, the information providing platform according to the embodiment allows quick and easy access to information and supports the decision-making of business leaders.

[0030] The information provision department can provide appropriate answers to questions from new employees and continuing learners. For example, the information provision department uses the generation AI to analyze the training and learning content that new employees have received in the past and provide individually customized learning plans based on the results. For example, if a specific skill is lacking, the generation AI can suggest learning materials and training to strengthen that skill. The information provision department also monitors individual progress in real time based on the new employee's learning history and adjusts the learning plan as needed. For example, if a new employee's understanding of a particular subject is low, the generation AI can provide additional learning resources related to that subject. The information provision department also analyzes the new employee's learning history and suggests learning materials and training methods tailored to their individual learning style. For example, video materials can be provided for new employees who are visual learners, and audio materials can be provided for new employees who are auditory learners. This can support the growth of new employees and continuing learners.

[0031] The analysis unit can analyze sales data and provide strategic proposals based on instructions from managers and leaders. In the analysis unit, for example, the generation AI analyzes managers' past decision-making data and proposes optimal decision-making patterns. For example, it proposes optimal decisions based on past successes and failures. The analysis unit also analyzes managers' past decision-making data and proposes optimal decision-making patterns for specific situations. For example, it proposes decisions that suit specific market conditions or competitive situations. The analysis unit also develops a system in which the generation AI proposes optimal decision-making patterns in real time based on managers' past decision-making data. For example, it proposes optimal decisions that suit the current situation in real time. This can support managers and leaders in making quick decisions.

[0032] The information provision unit can be used on mobile devices, PCs, and smart glasses. For example, the information provision unit uses a generation AI to analyze the situation on site in real time through smart glasses and provide appropriate instructions. For example, it provides troubleshooting methods for machines in real time. The information provision unit also uses a generation AI to analyze the situation on site in real time through smart glasses and provide appropriate instructions. For example, it displays work procedures and safety measures in real time. The information provision unit also uses a generation AI to analyze the situation on site in real time through smart glasses and provide appropriate instructions. For example, it suggests the optimal work procedures according to the situation on site. This allows for easy inquiries and quick feedback even during work.

[0033] The data collection unit can collect necessary information from internal documents and databases. For example, the data collection unit allows the generation AI to automatically classify internal documents to quickly search for necessary information. For example, it analyzes the content of documents and classifies them by category. The data collection unit also allows the generation AI to automatically classify internal documents to quickly search for necessary information. For example, it classifies documents based on specific keywords or phrases. The data collection unit also allows the generation AI to classify internal documents in real time to quickly search for necessary information. For example, it automatically classifies newly added documents and makes them searchable. This allows necessary information to be accessed quickly and easily.

[0034] The analysis unit analyzes sales data and market data and can provide the current state of the business and future outlook. In the analysis unit, for example, the generation AI analyzes past business data and predicts future trends. For example, future demand and market trends are predicted based on sales data and market data. In addition, the analysis unit uses the generation AI to analyze past business data and predict future trends in a specific industry or market. For example, sales forecasts are made for a specific product category. In addition, the analysis unit uses the generation AI to analyze past business data in real time and predict future trends. For example, trend forecasts are updated in real time in response to fluctuations in the data. This allows the current state of the business and future outlook to be quickly grasped.

[0035] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0036] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The information providing unit can also provide individually customized information based on the user's past search history and browsing history. For example, a user who frequently searches for information about a particular industry can be provided with the latest news and trend information related to that industry. The information providing unit can also automatically recommend related information based on the user's interests. For example, a user who is interested in a particular technology can be provided with the latest research papers and technical reports on that technology. The information providing unit can also analyze the user's behavioral patterns and predict and provide the information the user will need in advance. For example, a user working on a particular project can be provided with information related to that project in advance. This allows the user to access the information they need quickly and easily.

[0039] The information providing unit can also predict future behavior based on the user's past behavioral data and provide appropriate information in advance. For example, for a user working on a specific project, information related to that project can be provided in advance. The information providing unit can also analyze the user's behavioral patterns and predict and provide necessary information in advance. For example, for a user who frequently searches for information about a specific job, the latest information related to that job can be provided. The information providing unit can also provide individually customized information based on the user's behavioral data. For example, for a user who frequently searches for information about a specific skill, the latest training materials related to that skill can be provided. This allows users to access the information they need quickly and easily.

[0040] The analysis unit can also provide information to support future decision-making based on the user's past decision-making data. For example, it can suggest optimal decisions based on past successes and failures. The analysis unit can also analyze the user's past decision-making data and suggest optimal decision-making patterns for specific situations. For example, it can suggest decisions based on specific market conditions or competitive situations. The analysis unit can also develop a system that suggests optimal decision-making patterns in real time based on the user's past decision-making data. For example, it can suggest optimal decisions based on the current situation in real time. This can support the user in making quick decisions.

[0041] The information providing unit can also automatically recommend related information based on a user's interests. For example, a user who is interested in a particular technology can be provided with the latest research papers and technical reports on that technology. The information providing unit can also analyze a user's behavioral patterns and predict and provide information that the user will need in advance. For example, a user who frequently searches for information about a particular job can be provided with the latest information related to that job. The information providing unit can also provide individually customized information based on the user's past search history and browsing history. For example, a user who frequently searches for information about a particular industry can be provided with the latest news and trend information related to that industry. This allows users to access the information they need quickly and easily.

[0042] The analysis unit can also predict future behavior based on user behavior data and provide appropriate information in advance. For example, a user working on a specific project can be provided with information related to that project in advance. The analysis unit can also analyze a user's behavior patterns and predict and provide necessary information in advance. For example, a user who frequently searches for information about a specific job can be provided with the latest information related to that job. The analysis unit can also provide individually customized information based on the user behavior data. For example, a user who frequently searches for information about a specific skill can be provided with the latest training materials related to that skill. This allows users to access the information they need quickly and easily.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The information provider uses the generation AI to automatically provide answers to the company's knowledge base, industry history, on-site knowledge, specific work skills, and FAQs. For example, the generation AI uses text generation AI such as GPT-3 or BERT to generate appropriate answers to the user's questions. The information provider also allows the generation AI to generate answers based on prompts that include instructions on what the user wants the generation AI to do. Step 2: The analysis unit provides comparative analysis of figures and business insights based on the information provided by the information provider. For example, the analysis unit analyzes sales data and market data to provide the current state of the business and future outlook. The analysis unit can also analyze data using statistical methods and machine learning. Step 3: The data collection department collects various data obtained from within the company. For example, the data collection department collects necessary information from internal documents and databases, analyzes it, and provides it to the user. The data collection department also automatically evaluates the quality of the data, allowing only reliable data to be used.

[0045] (Example 2) An information provision platform according to an embodiment of the present invention uses generative AI to provide quick and easy access to information needed to solve problems, thereby accelerating the growth of new employees and continuing learners and supporting quick decision-making by managers and leaders.

[0046] An information provision platform according to an embodiment includes an information provision unit, an analysis unit, and a data collection unit. The information provision unit uses a generation AI to automatically provide a company's knowledge base, industry history, on-site knowledge, specific business skills, and answers to FAQs. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to generate appropriate answers to user questions. The information provision unit generates answers based on prompts containing instructions from the user about what the generation AI wants the user to do. The analysis unit provides comparative analysis of numerical data and business insights based on the information provided by the information provision unit. For example, the analysis unit analyzes sales data and market data to provide the current state of the business and future outlook. The analysis unit can also analyze data using statistical methods and machine learning. The data collection unit collects various data from within the company. For example, the data collection unit collects necessary information from internal documents and databases, analyzes it, and provides it to the user. The data collection unit can also automatically evaluate the quality of the data, allowing only reliable data to be used. As a result, the information providing platform according to the embodiment allows quick and easy access to information and supports the decision-making of business leaders.

[0047] The information provision department can provide appropriate answers to questions from new employees and continuing learners. For example, the information provision department uses the generation AI to analyze the training and learning content that new employees have received in the past and provide individually customized learning plans based on the results. For example, if a specific skill is lacking, the generation AI can suggest learning materials and training to strengthen that skill. The information provision department also monitors individual progress in real time based on the new employee's learning history and adjusts the learning plan as needed. For example, if a new employee's understanding of a particular subject is low, the generation AI can provide additional learning resources related to that subject. The information provision department also analyzes the new employee's learning history and suggests learning materials and training methods tailored to their individual learning style. For example, video materials can be provided for new employees who are visual learners, and audio materials can be provided for new employees who are auditory learners. This can support the growth of new employees and continuing learners.

[0048] The analysis unit can analyze sales data and provide strategic proposals based on instructions from managers and leaders. In the analysis unit, for example, the generation AI analyzes managers' past decision-making data and proposes optimal decision-making patterns. For example, it proposes optimal decisions based on past successes and failures. The analysis unit also analyzes managers' past decision-making data and proposes optimal decision-making patterns for specific situations. For example, it proposes decisions that suit specific market conditions or competitive situations. The analysis unit also develops a system in which the generation AI proposes optimal decision-making patterns in real time based on managers' past decision-making data. For example, it proposes optimal decisions that suit the current situation in real time. This can support managers and leaders in making quick decisions.

[0049] The information provision unit can be used on mobile devices, PCs, and smart glasses. For example, the information provision unit uses a generation AI to analyze the situation on site in real time through smart glasses and provide appropriate instructions. For example, it provides troubleshooting methods for machines in real time. The information provision unit also uses a generation AI to analyze the situation on site in real time through smart glasses and provide appropriate instructions. For example, it displays work procedures and safety measures in real time. The information provision unit also uses a generation AI to analyze the situation on site in real time through smart glasses and provide appropriate instructions. For example, it suggests the optimal work procedures according to the situation on site. This allows for easy inquiries and quick feedback even during work.

[0050] The data collection unit can collect necessary information from internal documents and databases. For example, the data collection unit allows the generation AI to automatically classify internal documents to quickly search for necessary information. For example, it analyzes the content of documents and classifies them by category. The data collection unit also allows the generation AI to automatically classify internal documents to quickly search for necessary information. For example, it classifies documents based on specific keywords or phrases. The data collection unit also allows the generation AI to classify internal documents in real time to quickly search for necessary information. For example, it automatically classifies newly added documents and makes them searchable. This allows necessary information to be accessed quickly and easily.

[0051] The analysis unit analyzes sales data and market data and can provide the current state of the business and future outlook. In the analysis unit, for example, the generation AI analyzes past business data and predicts future trends. For example, future demand and market trends are predicted based on sales data and market data. In addition, the analysis unit uses the generation AI to analyze past business data and predict future trends in a specific industry or market. For example, sales forecasts are made for a specific product category. In addition, the analysis unit uses the generation AI to analyze past business data in real time and predict future trends. For example, trend forecasts are updated in real time in response to fluctuations in the data. This allows the current state of the business and future outlook to be quickly grasped.

[0052] The information providing unit can monitor students' emotions in real time using an emotion estimation function and provide feedback according to the emotions. For example, the information providing unit captures the student's facial expression when reading an answer sheet with a camera and analyzes the student's emotions in real time using an emotion estimation algorithm. For example, the information providing unit calculates an emotion score based on changes in facial expression and provides feedback. The information providing unit also records the student's voice when reading an answer sheet and estimates the student's emotion in real time using voice analysis technology. For example, the information providing unit analyzes the tone and speed of the voice, calculates an emotion score, and provides feedback. The information providing unit also collects the student's biometric data (heart rate and electrodermal activity) with a sensor when reading an answer sheet and analyzes the student's emotion in real time using an emotion estimation algorithm. For example, the information providing unit calculates an emotion score based on fluctuations in heart rate and provides feedback. In this way, the student's emotions can be monitored in real time and appropriate feedback can be provided, thereby improving learning effectiveness.

[0053] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0054] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0055] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

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

[0057] The information providing unit can also provide individually customized information based on the user's past search history and browsing history. For example, a user who frequently searches for information about a particular industry can be provided with the latest news and trend information related to that industry. The information providing unit can also automatically recommend related information based on the user's interests. For example, a user who is interested in a particular technology can be provided with the latest research papers and technical reports on that technology. The information providing unit can also analyze the user's behavioral patterns and predict and provide the information the user will need in advance. For example, a user working on a particular project can be provided with information related to that project in advance. This allows the user to access the information they need quickly and easily.

[0058] The information providing unit can also estimate the user's emotions and provide appropriate information based on the estimated emotions. For example, if the user is feeling stressed, the information providing unit can provide information on relaxation methods and stress management. Also, if the user is excited, the information providing unit can provide information on improving motivation to make the most of that excitement. The information providing unit can also adjust the way information is presented depending on the user's emotions. For example, if the user is tired, the information providing unit can provide concise and easy-to-understand information. Also, if the user is concentrating, the information providing unit can provide detailed information. This makes it possible to provide appropriate information according to the user's emotions.

[0059] The analysis unit can also estimate the user's emotions and provide data analysis results based on the estimated emotions. For example, if the user is feeling anxious, it can emphasize positive data to give them a sense of security. If the user is excited, it can provide challenging data to capitalize on that excitement. The analysis unit can also adjust the way data is presented depending on the user's emotions. For example, if the user is tired, it can provide concise and easy-to-understand data. If the user is concentrating, it can provide detailed data. This makes it possible to provide appropriate data analysis results according to the user's emotions.

[0060] The information providing unit can also estimate the user's emotions and provide appropriate feedback based on the estimated emotions. For example, if the user is feeling down, it can provide an encouraging message or positive feedback. Also, if the user is feeling confident, it can provide feedback to further boost that confidence. The information providing unit can also adjust the content and method of feedback according to the user's emotions. For example, if the user is tired, it can provide simple and easy-to-understand feedback. Also, if the user is concentrating, it can provide detailed feedback. This makes it possible to provide appropriate feedback according to the user's emotions.

[0061] The data collection unit can also estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a data collection method to reduce stress. Also, if the user is excited, it can suggest a data collection method to make use of that excitement. The data collection unit can also adjust the range and frequency of data collection according to the user's emotions. For example, if the user is tired, it can reduce the frequency of data collection. Also, if the user is concentrating, it can expand the range of data collection. This makes it possible to collect data appropriately according to the user's emotions.

[0062] The information providing unit can also predict future behavior based on the user's past behavioral data and provide appropriate information in advance. For example, for a user working on a specific project, information related to that project can be provided in advance. The information providing unit can also analyze the user's behavioral patterns and predict and provide necessary information in advance. For example, for a user who frequently searches for information about a specific job, the latest information related to that job can be provided. The information providing unit can also provide individually customized information based on the user's behavioral data. For example, for a user who frequently searches for information about a specific skill, the latest training materials related to that skill can be provided. This allows users to access the information they need quickly and easily.

[0063] The analysis unit can also provide information to support future decision-making based on the user's past decision-making data. For example, it can suggest optimal decisions based on past successes and failures. The analysis unit can also analyze the user's past decision-making data and suggest optimal decision-making patterns for specific situations. For example, it can suggest decisions based on specific market conditions or competitive situations. The analysis unit can also develop a system that suggests optimal decision-making patterns in real time based on the user's past decision-making data. For example, it can suggest optimal decisions based on the current situation in real time. This can support the user in making quick decisions.

[0064] The information providing unit can also automatically recommend related information based on a user's interests. For example, a user who is interested in a particular technology can be provided with the latest research papers and technical reports on that technology. The information providing unit can also analyze a user's behavioral patterns and predict and provide information that the user will need in advance. For example, a user who frequently searches for information about a particular job can be provided with the latest information related to that job. The information providing unit can also provide individually customized information based on the user's past search history and browsing history. For example, a user who frequently searches for information about a particular industry can be provided with the latest news and trend information related to that industry. This allows users to access the information they need quickly and easily.

[0065] The analysis unit can also predict future behavior based on user behavior data and provide appropriate information in advance. For example, a user working on a specific project can be provided with information related to that project in advance. The analysis unit can also analyze a user's behavior patterns and predict and provide necessary information in advance. For example, a user who frequently searches for information about a specific job can be provided with the latest information related to that job. The analysis unit can also provide individually customized information based on the user behavior data. For example, a user who frequently searches for information about a specific skill can be provided with the latest training materials related to that skill. This allows users to access the information they need quickly and easily.

[0066] The information providing unit can also estimate the user's emotions and provide appropriate information based on the estimated emotions. For example, if the user is feeling stressed, the information providing unit can provide information on relaxation methods and stress management. Also, if the user is excited, the information providing unit can provide information on improving motivation to make the most of that excitement. The information providing unit can also adjust the way information is presented depending on the user's emotions. For example, if the user is tired, the information providing unit can provide concise and easy-to-understand information. Also, if the user is concentrating, the information providing unit can provide detailed information. This makes it possible to provide appropriate information according to the user's emotions.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The information provider uses the generation AI to automatically provide answers to the company's knowledge base, industry history, on-site knowledge, specific work skills, and FAQs. For example, the generation AI uses text generation AI such as GPT-3 or BERT to generate appropriate answers to the user's questions. The information provider also allows the generation AI to generate answers based on prompts that include instructions on what the user wants the generation AI to do. Step 2: The analysis unit provides comparative analysis of figures and business insights based on the information provided by the information provider. For example, the analysis unit analyzes sales data and market data to provide the current state of the business and future outlook. The analysis unit can also analyze data using statistical methods and machine learning. Step 3: The data collection department collects various data obtained from within the company. For example, the data collection department collects necessary information from internal documents and databases, analyzes it, and provides it to the user. The data collection department also automatically evaluates the quality of the data, allowing only reliable data to be used.

[0069] 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.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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. [Explanation of symbols]

[0136] 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. The information provision department uses generative AI to automatically provide corporate knowledge bases, industry history, on-site knowledge, specific work skills, and answers to FAQs. an analysis unit that provides comparative analysis of numerical values ​​and business insights based on the information provided by the information providing unit; A data collection unit that collects various data obtained from within the company. A system characterized by:

2. The information providing unit Provide relevant answers to questions from new employees and continuing learners 2. The system of claim 1.

3. The analysis unit Analyze sales data and provide strategic recommendations based on direction from management and leadership.

2. The system of claim 1.

4. The information providing unit Available on mobile, PC, and smart glasses 2. The system of claim 1.

5. The data collection unit Gather the necessary information from the company's internal documents and databases 2. The system of claim 1.

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