System

Abstract

An object of a system according to an embodiment is to analyze communication data and realize optimal communication.SOLUTION: A system includes a data collection part, a data analysis part, a summary extraction part, an information sharing part, and a problem specification part. The data collection unit collects communication data. The data analysis unit analyzes the communication data collected by the data collection unit. The summary extraction unit extracts a summary and an important item from the communication data analyzed by the data analysis unit. An information sharing part quickly and effectively shares the summary and the important item extracted by the summary extraction part. The problem identification unit identifies a lack of sharing and a problem from the communication data analyzed by the data analysis unit.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 technology has many issues and disadvantages due to lack of communication, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze communication data and realize optimal communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a summary extraction unit, an information sharing unit, and an issue identification unit. The data collection unit collects communication data. The data analysis unit analyzes the communication data collected by the data collection unit. The summary extraction unit extracts summaries and important points from the communication data analyzed by the data analysis unit. The information sharing unit quickly and effectively shares the summaries and important points extracted by the summary extraction unit. The issue identification unit identifies insufficient sharing and issues from the communication data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze communication data and realize optimal communication. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication efficiency improvement system according to an embodiment of the present invention is a system in which a generative AI collects and analyzes communication data, extracts summaries and important points, shares information quickly and effectively, and identifies insufficient sharing and issues. As a result, the communication efficiency system can realize innovation and efficiency in communication by utilizing the generative AI.

[0029] A communication efficiency improvement system according to an embodiment includes a data collection unit, a data analysis unit, a summary extraction unit, an information sharing unit, and an issue identification unit. The data collection unit collects communication data. For example, the data collection unit collects data such as emails, chats, and meeting minutes. The data collection unit can also collect audio data and video data. The data analysis unit analyzes the communication data collected by the data collection unit. For example, the data analysis unit analyzes text data using natural language processing technology. The data analysis unit can also analyze audio data using speech recognition technology. The summary extraction unit extracts summaries and key points from the communication data analyzed by the data analysis unit. For example, the summary extraction unit summarizes key points and decisions from long meeting minutes using generative AI. The summary extraction unit can also extract frequently occurring keywords and important statements. The information sharing unit quickly and effectively shares the summaries and key points extracted by the summary extraction unit. For example, the information sharing unit automatically distributes information via email or chat. The information sharing unit can also provide real-time notifications and prioritize information. The problem identification unit identifies insufficient sharing or issues from the communication data analyzed by the data analysis unit. For example, the problem identification unit automatically detects issues when there is a lack of information sharing in a specific project or when a specific issue remains unresolved. The problem identification unit can also identify issues based on the number of unread messages or lack of feedback. This allows the communication efficiency improvement system according to the embodiment to utilize generative AI to achieve innovation and efficiency in communication. For example, this can reduce workload, speed up decision-making, improve overall organizational performance, and improve work-life balance.

[0030] The data analysis unit can analyze non-verbal communication elements, including the content of audio and video conferences. The data analysis unit, for example, analyzes audio data and estimates emotions based on the speaker's tone and pitch. For example, it quantifies the degree of tension or excitement and evaluates the atmosphere of the conference. The data analysis unit also analyzes video data from video conferences and estimates the speaker's emotions using facial expression recognition technology. For example, it reads emotions from facial expressions such as smiles and furrowed brows. The data analysis unit also integrates audio and video data and comprehensively evaluates the quality of communication by combining the content of remarks and non-verbal elements. For example, it analyzes the degree of agreement between the content of remarks and facial expressions. This allows for analysis of non-verbal communication elements.

[0031] The data analysis department can classify communication data by organizational hierarchy and department, and compare and analyze the quality of communication between departments. For example, the data analysis department analyzes the frequency and content of communication for each department based on the data collected by the generation AI. For example, it compares the number of email exchanges and meetings between departments. The data analysis department also evaluates the quality of communication between superiors and subordinates based on the organizational hierarchy. For example, it analyzes the frequency and content of feedback from superiors. The data analysis department also analyzes communication data for each department over time to evaluate fluctuations in communication as the project progresses. For example, it compares the quality of communication in the early and later stages of a project. This makes it possible to compare and analyze the quality of communication between departments.

[0032] The data collection unit can analyze external feedback, including social media posts or comments. For example, the data collection unit collects social media post data, and the generation AI analyzes the content. For example, it extracts customer feedback and opinions about the company. The data collection unit also analyzes social media comment data and classifies positive and negative opinions. For example, it extracts evaluations of a product and areas for improvement. The data collection unit also evaluates the company's brand image and customer satisfaction based on social media data. For example, it analyzes responses to a specific campaign. This makes it possible to analyze external feedback.

[0033] The data analysis unit can compare communication data with data from different industries and cultural spheres to analyze global communication trends. For example, the data analysis unit collects communication data from different industries, and the generation AI analyzes the trends. For example, it compares the differences in communication styles between the IT industry and the manufacturing industry. The data analysis unit also analyzes communication data from different cultural spheres to evaluate cultural differences. For example, it analyzes the differences in business communication between Asia and Europe. The data analysis unit also identifies common trends and patterns based on global communication data. For example, it analyzes changes in communication due to the spread of remote work. This makes it possible to analyze global communication trends.

[0034] The summary extraction unit can improve the accuracy of the summary by referring to similar past communication data. For example, when the generation AI summarizes meeting minutes, the summary extraction unit refers to the minutes of similar past meetings to improve the accuracy of the summary. For example, it refers to the decisions made in past meetings. Furthermore, when summarizing emails, the summary extraction unit refers to the content of similar past emails to improve the accuracy of the summary. For example, it extracts important points based on past email exchanges. Furthermore, when summarizing chat logs, the summary extraction unit refers to the content of similar past chats to improve the accuracy of the summary. For example, it refers to important exchanges in past chats. This can improve the accuracy of the summary.

[0035] The summary extraction unit can automatically explain technical terms and phrases to promote understanding. For example, when the generation AI summarizes meeting minutes, the summary extraction unit automatically explains technical terms and phrases. For example, it includes definitions of technical terms and background information in the summary. The summary extraction unit also automatically explains technical terms and phrases when summarizing emails. For example, it generates summaries that explain technical content in an easy-to-understand manner. The summary extraction unit also automatically explains technical terms and phrases when summarizing chat logs. For example, it summarizes the content of technical discussions in general terms. This makes it possible to automatically explain technical terms and phrases to promote understanding.

[0036] The summary extraction unit automatically translates summaries into different languages, allowing for feedback from an international perspective. For example, the summary extraction unit uses a generative AI to summarize meeting minutes and automatically translate them into different languages. For example, the summary extraction unit translates the summaries into multiple languages, such as English, French, and Chinese, and collects feedback from an international perspective. The summary extraction unit also summarizes emails and automatically translates them into different languages. For example, the summary extraction unit provides summaries to international team members and obtains feedback. The summary extraction unit also summarizes chat logs and automatically translates them into different languages. For example, in a global project, the summaries are shared with members of different languages. This allows for automatic translation into different languages ​​and feedback from an international perspective.

[0037] When summarizing, the summary extraction unit converts the results into visual notes or mind maps, making them easier to understand visually. For example, the summary extraction unit uses a generation AI to summarize meeting minutes and convert them into visual notes. For example, it can indicate important points with diagrams and icons to make them easier to understand visually. The summary extraction unit also summarizes emails and converts them into mind maps. For example, it can visually organize related keywords and concepts and display summaries. The summary extraction unit also summarizes chat logs and converts them into visual notes or mind maps. For example, it can visually display important interactions to make it easier to grasp the overall flow. This allows the summary to be converted into visual notes or mind maps, making it easier to understand visually.

[0038] The information sharing unit can select the most effective information sharing method based on the recipient's past behavioral history. The information sharing unit, for example, analyzes the recipient's past behavioral history and selects the most effective information sharing method. For example, if information sharing by email has been effective in the past, email will be used again. The information sharing unit also builds a system that customizes the information distribution method based on the recipient's behavioral history. For example, for recipients who prefer chat or social media, those platforms will be used. The information sharing unit also analyzes the recipient's past behavioral data and selects a method to maximize the effectiveness of information sharing. For example, the optimal distribution channel will be selected based on past data. This makes it possible to select the most effective information sharing method.

[0039] The information sharing unit can automatically set the priority of notifications according to the importance of the information. For example, the generation AI in the information sharing unit analyzes the importance of information and automatically sets the priority of notifications. For example, it prioritizes the delivery of information with a high level of urgency. The information sharing unit also builds a system that sets the priority of notifications based on the importance of information. For example, it delivers notifications of important meetings with the highest priority. The information sharing unit also has the generation AI evaluate the importance of information and dynamically adjust the priority of notifications. For example, it delivers progress information on important projects with priority. This makes it possible to automatically set the priority of notifications.

[0040] The information sharing unit can integrate different communication channels and manage them centrally. For example, the information sharing unit builds a system in which the generation AI integrates different communication channels and manages information centrally. For example, email, chat, and social media messages are managed on a single platform. The information sharing unit also integrates information from different channels and allows recipients to access it centrally. For example, all messages are displayed on a single interface. The information sharing unit also integrates information from different channels in real time using the generation AI and delivers the information in the most optimal way to the recipient. For example, important information is delivered simultaneously through multiple channels. This allows different communication channels to be integrated and managed centrally.

[0041] The information sharing unit can provide customized information based on the recipient's profile. For example, the information sharing unit builds a system that provides customized information based on the recipient's profile information. For example, it delivers information according to the recipient's job title or job responsibilities. The information sharing unit also uses a generation AI to analyze the recipient's profile and select and deliver the most appropriate information. For example, it provides information based on the recipient's interests and concerns. The information sharing unit also customizes the content and format of the information based on the recipient's profile data. For example, it explains technical information in detail for engineers. This makes it possible to provide customized information based on the recipient's profile.

[0042] The problem identification unit can compare with past data to identify trends and patterns. For example, the generation AI compares past communication data with current data to identify trends and patterns of lack of sharing and issues. For example, it analyzes current issues based on past data. The problem identification unit also analyzes past data to identify the frequency and trends of lack of sharing and issues. For example, it analyzes the increase or decrease in issues over a specific period. The problem identification unit also builds a system in which the generation AI compares past data with current data to identify patterns of lack of sharing and issues. For example, it refers to past success stories and failure stories. This makes it possible to compare with past data to identify trends and patterns.

[0043] The problem identification unit can automatically identify the root cause of a problem and propose solutions. For example, the generation AI in the problem identification unit analyzes data on lack of sharing and issues and automatically identifies the root cause of the problem. For example, it analyzes the causes of lack of information sharing in a specific project. The problem identification unit also builds a system that identifies the root cause of a problem and automatically proposes solutions. For example, it proposes measures to improve information sharing and strengthen communication. The problem identification unit also builds a system that identifies the root cause of a problem and proposes specific solutions. For example, it proposes ways to resolve lack of communication in a specific team. This makes it possible to automatically identify the root cause of a problem and propose solutions.

[0044] The problem identification unit can compare data between different projects or departments and identify common issues. In the problem identification unit, for example, the generation AI compares data between different projects or departments and identifies common issues. For example, it analyzes a lack of information sharing that occurs across multiple projects. The problem identification unit can also build a system that analyzes data between different departments and identifies common issues. For example, it can identify problems with company-wide communication. In addition, the problem identification unit can compare data between different projects or departments and identify common issues. For example, it can analyze a lack of information sharing across multiple departments. This makes it possible to compare data between different projects or departments and identify common issues.

[0045] The problem identification department can compare with external benchmark data and identify gaps with industry standards. For example, the generation AI collects external benchmark data and compares it with internal data to identify gaps with industry standards. For example, it compares it with the communication styles of other companies. The problem identification department also builds a system based on external benchmark data to identify gaps in sharing and issues. For example, it evaluates the company's performance against industry standards. The problem identification department also uses the generation AI to analyze external benchmark data and identify gaps with industry standards. For example, it refers to success stories from other companies. This makes it possible to compare with external benchmark data and identify gaps with industry standards.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can also collect data from IoT devices. For example, it can collect temperature and lighting data in a conference room to evaluate the comfort of the meeting. The data collection unit can also collect data from wearable devices to monitor participants' health and stress levels. Furthermore, the data collection unit can collect data from sensors in the office to analyze the frequency and location of communication. This makes it possible to utilize data from IoT devices to improve the quality of communication.

[0048] The data analysis unit can analyze non-verbal communication elements, including the content of audio and video conferences. For example, it can analyze audio data and estimate emotions based on the speaker's tone and pitch. For example, it can quantify the degree of tension or excitement to evaluate the atmosphere of the conference. The data analysis unit can also analyze video data from video conferences and estimate the speaker's emotions using facial expression recognition technology. For example, it can read emotions from facial expressions such as smiles and furrowed brows. The data analysis unit can also integrate audio and video data and comprehensively evaluate the quality of communication by combining the content of remarks with non-verbal elements. For example, it can analyze the degree of agreement between the content of remarks and facial expressions. This allows it to analyze non-verbal communication elements.

[0049] The data analysis department can classify communication data by organizational hierarchy and department, and compare and analyze the quality of communication between departments. For example, based on the data collected by the generation AI, it can analyze the frequency and content of communication for each department. For example, it can compare the number of email exchanges and meetings between departments. The data analysis department can also evaluate the quality of communication between superiors and subordinates based on the organizational hierarchy. For example, it can analyze the frequency and content of feedback from superiors. The data analysis department can also analyze communication data for each department over time to evaluate fluctuations in communication as the project progresses. For example, it can compare the quality of communication in the early and later stages of a project. This makes it possible to compare and analyze the quality of communication between departments.

[0050] The data collection unit can analyze external feedback, including social media posts and comments. For example, social media post data is collected and the generation AI analyzes the content. For example, customer feedback and opinions about a company can be extracted. The data collection unit can also analyze social media comment data and classify positive and negative opinions. For example, product evaluations and areas for improvement can be extracted. The data collection unit can also evaluate a company's brand image and customer satisfaction based on social media data. For example, it can analyze responses to a specific campaign. This allows for external feedback to be analyzed.

[0051] The summary extraction unit can improve the accuracy of the summary by referring to similar past communication data. For example, when the generation AI summarizes meeting minutes, it can refer to the minutes of similar past meetings to improve the accuracy of the summary. For example, it can refer to the decisions made in past meetings. In addition, when summarizing emails, the summary extraction unit can improve the accuracy of the summary by referring to the content of similar past emails. For example, it can extract important points based on past email exchanges. In addition, when summarizing chat logs, the summary extraction unit can improve the accuracy of the summary by referring to the content of similar past chats. For example, it can refer to important exchanges in past chats. This can improve the accuracy of the summary.

[0052] The summary extraction unit can automatically explain technical terms and terminology to promote understanding. For example, when the generation AI summarizes meeting minutes, it automatically explains technical terms and terminology. For example, it includes definitions of technical terms and background information in the summary. The summary extraction unit also automatically explains technical terms and terminology when summarizing emails. For example, it generates summaries that explain technical content in an easy-to-understand manner. The summary extraction unit also automatically explains technical terms and terminology when summarizing chat logs. For example, it summarizes the content of technical discussions in general terms. This makes it possible to automatically explain technical terms and terminology to promote understanding.

[0053] The summary extraction unit automatically translates summaries into different languages, allowing for feedback from an international perspective. For example, a generative AI summarizes meeting minutes and automatically translates them into different languages. For example, this can be translated into multiple languages, such as English, French, and Chinese, to collect feedback from an international perspective. The summary extraction unit also summarizes emails and automatically translates them into different languages. For example, it can provide summaries to international team members and obtain feedback. The summary extraction unit also summarizes chat logs and automatically translates them into different languages. For example, in a global project, summaries can be shared with members of different languages. This allows for automatic translation into different languages ​​and feedback from an international perspective.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects communication data. For example, the data collection unit collects data such as emails, chats, and meeting minutes. The data collection unit can also collect audio data and video data. Step 2: The data analysis unit analyzes the communication data collected by the data collection unit. For example, the data analysis unit analyzes text data using natural language processing technology. The data analysis unit can also analyze voice data using voice recognition technology. Step 3: The summary extraction unit extracts summaries and key points from the communication data analyzed by the data analysis unit. For example, the summary extraction unit uses generative AI to summarize the main points and decisions from long meeting minutes. The summary extraction unit can also extract frequently occurring keywords and important statements. Step 4: The information sharing unit quickly and effectively shares the summaries and key points extracted by the summary extraction unit. For example, the information sharing unit can automatically distribute information via email or chat. The information sharing unit can also provide real-time notifications and prioritize information. Step 5: The problem identification unit identifies issues and lack of sharing from the communication data analyzed by the data analysis unit. For example, the problem identification unit automatically detects issues such as a lack of information sharing in a specific project or an unresolved issue. The problem identification unit can also identify issues based on the number of unread messages or a lack of feedback.

[0056] (Example 2) A communication efficiency improvement system according to an embodiment of the present invention is a system in which a generative AI collects and analyzes communication data, extracts summaries and important points, shares information quickly and effectively, and identifies insufficient sharing and issues. As a result, the communication efficiency system can realize innovation and efficiency in communication by utilizing the generative AI.

[0057] A communication efficiency improvement system according to an embodiment includes a data collection unit, a data analysis unit, a summary extraction unit, an information sharing unit, and an issue identification unit. The data collection unit collects communication data. For example, the data collection unit collects data such as emails, chats, and meeting minutes. The data collection unit can also collect audio data and video data. The data analysis unit analyzes the communication data collected by the data collection unit. For example, the data analysis unit analyzes text data using natural language processing technology. The data analysis unit can also analyze audio data using speech recognition technology. The summary extraction unit extracts summaries and key points from the communication data analyzed by the data analysis unit. For example, the summary extraction unit summarizes key points and decisions from long meeting minutes using generative AI. The summary extraction unit can also extract frequently occurring keywords and important statements. The information sharing unit quickly and effectively shares the summaries and key points extracted by the summary extraction unit. For example, the information sharing unit automatically distributes information via email or chat. The information sharing unit can also provide real-time notifications and prioritize information. The problem identification unit identifies insufficient sharing or issues from the communication data analyzed by the data analysis unit. For example, the problem identification unit automatically detects issues when there is a lack of information sharing in a specific project or when a specific issue remains unresolved. The problem identification unit can also identify issues based on the number of unread messages or lack of feedback. This allows the communication efficiency improvement system according to the embodiment to utilize generative AI to achieve innovation and efficiency in communication. For example, this can reduce workload, speed up decision-making, improve overall organizational performance, and improve work-life balance.

[0058] The data analysis unit uses the emotion estimation function to analyze the user's emotions and can evaluate the quality of communication based on emotional fluctuations. For example, the data analysis unit performs emotion estimation on email and chat data collected by the generation AI and analyzes the user's emotional fluctuations. For example, if there are a lot of positive emotions, it may evaluate that communication is smooth. The data analysis unit also analyzes meeting minutes data to estimate the speaker's emotions. For example, it quantifies the intensity of emotions from the content of comments and evaluates the atmosphere of the meeting and the progress of the discussion. The data analysis unit also visualizes emotional fluctuations over time based on the data collected by the generation AI and evaluates the quality of communication over a specific period. For example, it analyzes emotional fluctuations as a project progresses. This makes it possible to evaluate the quality of communication based on emotional fluctuations.

[0059] The data analysis unit can analyze non-verbal communication elements, including the content of audio and video conferences. The data analysis unit, for example, analyzes audio data and estimates emotions based on the speaker's tone and pitch. For example, it quantifies the degree of tension or excitement and evaluates the atmosphere of the conference. The data analysis unit also analyzes video data from video conferences and estimates the speaker's emotions using facial expression recognition technology. For example, it reads emotions from facial expressions such as smiles and furrowed brows. The data analysis unit also integrates audio and video data and comprehensively evaluates the quality of communication by combining the content of remarks and non-verbal elements. For example, it analyzes the degree of agreement between the content of remarks and facial expressions. This allows for analysis of non-verbal communication elements.

[0060] The data analysis department can classify communication data by organizational hierarchy and department, and compare and analyze the quality of communication between departments. For example, the data analysis department analyzes the frequency and content of communication for each department based on the data collected by the generation AI. For example, it compares the number of email exchanges and meetings between departments. The data analysis department also evaluates the quality of communication between superiors and subordinates based on the organizational hierarchy. For example, it analyzes the frequency and content of feedback from superiors. The data analysis department also analyzes communication data for each department over time to evaluate fluctuations in communication as the project progresses. For example, it compares the quality of communication in the early and later stages of a project. This makes it possible to compare and analyze the quality of communication between departments.

[0061] The data collection unit can analyze external feedback, including social media posts or comments. For example, the data collection unit collects social media post data, and the generation AI analyzes the content. For example, it extracts customer feedback and opinions about the company. The data collection unit also analyzes social media comment data and classifies positive and negative opinions. For example, it extracts evaluations of a product and areas for improvement. The data collection unit also evaluates the company's brand image and customer satisfaction based on social media data. For example, it analyzes responses to a specific campaign. This makes it possible to analyze external feedback.

[0062] The data analysis unit can compare communication data with data from different industries and cultural spheres to analyze global communication trends. For example, the data analysis unit collects communication data from different industries, and the generation AI analyzes the trends. For example, it compares the differences in communication styles between the IT industry and the manufacturing industry. The data analysis unit also analyzes communication data from different cultural spheres to evaluate cultural differences. For example, it analyzes the differences in business communication between Asia and Europe. The data analysis unit also identifies common trends and patterns based on global communication data. For example, it analyzes changes in communication due to the spread of remote work. This makes it possible to analyze global communication trends.

[0063] The data analysis unit can use the emotion estimation function to monitor the user's emotional response to communication data in real time and provide feedback to elicit positive emotions. For example, the data analysis unit can monitor the user's emotional response to data collected by the generative AI in real time and provide feedback to elicit positive emotions. For example, it can analyze the user's facial expressions and voice and provide positive feedback. The data analysis unit can also use the emotion estimation function to analyze the user's emotional fluctuations and make positive suggestions when negative emotions are detected. For example, it can present encouraging messages or success stories. The data analysis unit can also provide an interface to elicit positive emotions based on the user's emotional response data. For example, it can display designs or content that evoke positive emotions in the user. This makes it possible to monitor the user's emotional response in real time and provide feedback to elicit positive emotions.

[0064] The summary extraction unit can use the emotion estimation function to emphasize emotionally important points from communication data. For example, when the generation AI summarizes meeting minutes, the summary extraction unit uses the emotion estimation function to emphasize emotionally important points. For example, parts that strongly express the speaker's emotions are reflected in the summary. In addition, when summarizing the contents of emails, the summary extraction unit uses the emotion estimation function to emphasize emotionally important messages. For example, messages of gratitude or apology are included in the summary. In addition, when summarizing chat logs, the summary extraction unit uses the emotion estimation function to emphasize emotionally important exchanges. For example, expressions of user dissatisfaction or joy are reflected in the summary. This makes it possible to emphasize emotionally important points.

[0065] The summary extraction unit can improve the accuracy of the summary by referring to similar past communication data. For example, when the generation AI summarizes meeting minutes, the summary extraction unit refers to the minutes of similar past meetings to improve the accuracy of the summary. For example, it refers to the decisions made in past meetings. Furthermore, when summarizing emails, the summary extraction unit refers to the content of similar past emails to improve the accuracy of the summary. For example, it extracts important points based on past email exchanges. Furthermore, when summarizing chat logs, the summary extraction unit refers to the content of similar past chats to improve the accuracy of the summary. For example, it refers to important exchanges in past chats. This can improve the accuracy of the summary.

[0066] The summary extraction unit can automatically explain technical terms and phrases to promote understanding. For example, when the generation AI summarizes meeting minutes, the summary extraction unit automatically explains technical terms and phrases. For example, it includes definitions of technical terms and background information in the summary. The summary extraction unit also automatically explains technical terms and phrases when summarizing emails. For example, it generates summaries that explain technical content in an easy-to-understand manner. The summary extraction unit also automatically explains technical terms and phrases when summarizing chat logs. For example, it summarizes the content of technical discussions in general terms. This makes it possible to automatically explain technical terms and phrases to promote understanding.

[0067] The summary extraction unit automatically translates summaries into different languages, allowing for feedback from an international perspective. For example, the summary extraction unit uses a generative AI to summarize meeting minutes and automatically translate them into different languages. For example, the summary extraction unit translates the summaries into multiple languages, such as English, French, and Chinese, and collects feedback from an international perspective. The summary extraction unit also summarizes emails and automatically translates them into different languages. For example, the summary extraction unit provides summaries to international team members and obtains feedback. The summary extraction unit also summarizes chat logs and automatically translates them into different languages. For example, in a global project, the summaries are shared with members of different languages. This allows for automatic translation into different languages ​​and feedback from an international perspective.

[0068] When summarizing, the summary extraction unit converts the results into visual notes or mind maps, making them easier to understand visually. For example, the summary extraction unit uses a generation AI to summarize meeting minutes and convert them into visual notes. For example, it can indicate important points with diagrams and icons to make them easier to understand visually. The summary extraction unit also summarizes emails and converts them into mind maps. For example, it can visually organize related keywords and concepts and display summaries. The summary extraction unit also summarizes chat logs and converts them into visual notes or mind maps. For example, it can visually display important interactions to make it easier to grasp the overall flow. This allows the summary to be converted into visual notes or mind maps, making it easier to understand visually.

[0069] The summary extraction unit uses the emotion estimation function to collect users' emotional reactions to summarized information and can improve the accuracy of the summary based on that. The summary extraction unit collects users' emotional reactions to summarized meeting minutes, for example, and improves the accuracy of the summary based on that data. For example, it prioritizes the adoption of summary sentences with a high number of positive reactions. The summary extraction unit also collects users' emotional reactions to email summaries and regenerates summaries if there are a high number of negative reactions. For example, it modifies the summary sentences based on user feedback. The summary extraction unit also summarizes chat logs and identifies areas for improvement in the summary sentences based on user emotional reaction data. For example, it makes suggestions to modify parts with low emotion scores. This can improve the accuracy of the summary.

[0070] The information sharing unit can use the emotion estimation function to deliver information at the optimal timing according to the recipient's emotional state. For example, the information sharing unit uses a generation AI to analyze the recipient's emotional state in real time and deliver information at the optimal timing. For example, important information is delivered when the recipient is relaxed. The information sharing unit also uses the emotion estimation function to analyze the recipient's emotional fluctuations and deliver information when positive emotions are strong. For example, positive feedback is sent when the recipient is happy. The information sharing unit also builds a system that adjusts the timing of information delivery based on the recipient's emotional state. For example, the delivery of information is delayed when the recipient is feeling stressed. This makes it possible to deliver information at the optimal timing according to the recipient's emotional state.

[0071] The information sharing unit can select the most effective information sharing method based on the recipient's past behavioral history. The information sharing unit, for example, analyzes the recipient's past behavioral history and selects the most effective information sharing method. For example, if information sharing by email has been effective in the past, email will be used again. The information sharing unit also builds a system that customizes the information distribution method based on the recipient's behavioral history. For example, for recipients who prefer chat or social media, those platforms will be used. The information sharing unit also analyzes the recipient's past behavioral data and selects a method to maximize the effectiveness of information sharing. For example, the optimal distribution channel will be selected based on past data. This makes it possible to select the most effective information sharing method.

[0072] The information sharing unit can automatically set the priority of notifications according to the importance of the information. For example, the generation AI in the information sharing unit analyzes the importance of information and automatically sets the priority of notifications. For example, it prioritizes the delivery of information with a high level of urgency. The information sharing unit also builds a system that sets the priority of notifications based on the importance of information. For example, it delivers notifications of important meetings with the highest priority. The information sharing unit also has the generation AI evaluate the importance of information and dynamically adjust the priority of notifications. For example, it delivers progress information on important projects with priority. This makes it possible to automatically set the priority of notifications.

[0073] The information sharing unit can integrate different communication channels and manage them centrally. For example, the information sharing unit builds a system in which the generation AI integrates different communication channels and manages information centrally. For example, email, chat, and social media messages are managed on a single platform. The information sharing unit also integrates information from different channels and allows recipients to access it centrally. For example, all messages are displayed on a single interface. The information sharing unit also integrates information from different channels in real time using the generation AI and delivers the information in the most optimal way to the recipient. For example, important information is delivered simultaneously through multiple channels. This allows different communication channels to be integrated and managed centrally.

[0074] The information sharing unit can provide customized information based on the recipient's profile. For example, the information sharing unit builds a system that provides customized information based on the recipient's profile information. For example, it delivers information according to the recipient's job title or job responsibilities. The information sharing unit also uses a generation AI to analyze the recipient's profile and select and deliver the most appropriate information. For example, it provides information based on the recipient's interests and concerns. The information sharing unit also customizes the content and format of the information based on the recipient's profile data. For example, it explains technical information in detail for engineers. This makes it possible to provide customized information based on the recipient's profile.

[0075] The information sharing unit can use the emotion estimation function to monitor the emotional reactions of recipients after sharing information and reflect them in the next information sharing. The information sharing unit, for example, monitors the emotional reactions of recipients after sharing information in real time and reflects that data in the next information sharing. For example, it continues the information sharing method that results in a high number of positive reactions. The information sharing unit also uses the emotion estimation function to analyze the emotional fluctuations of recipients and select the optimal method for the next information sharing. For example, if there are a lot of negative reactions, it adjusts the content of the information. The information sharing unit also builds a system that improves the method and timing of information sharing based on the recipient's emotional reaction data. For example, it distributes information during times when the emotion score is high. This makes it possible to monitor the emotional reactions of recipients after sharing information and reflect them in the next information sharing.

[0076] The problem identification unit can use the emotion estimation function to prioritize analysis of areas with a high number of negative emotional reactions. For example, the generation AI uses the emotion estimation function to identify areas with a high number of negative emotional reactions and prioritize analysis of those areas. For example, it detects dissatisfaction and stress in a specific project. The problem identification unit can also analyze communication data with a high number of negative emotional reactions and identify insufficient sharing and issues. For example, it can analyze areas where complaints frequently occur. The problem identification unit can also use the emotion estimation function to prioritize analysis of areas with strong negative emotions and identify the root cause of the problem. For example, it can detect insufficient communication within a specific team. This makes it possible to prioritize analysis of areas with a high number of negative emotional reactions.

[0077] The problem identification unit can compare with past data to identify trends and patterns. For example, the generation AI compares past communication data with current data to identify trends and patterns of lack of sharing and issues. For example, it analyzes current issues based on past data. The problem identification unit also analyzes past data to identify the frequency and trends of lack of sharing and issues. For example, it analyzes the increase or decrease in issues over a specific period. The problem identification unit also builds a system in which the generation AI compares past data with current data to identify patterns of lack of sharing and issues. For example, it refers to past success stories and failure stories. This makes it possible to compare with past data to identify trends and patterns.

[0078] The problem identification unit can automatically identify the root cause of a problem and propose solutions. For example, the generation AI in the problem identification unit analyzes data on lack of sharing and issues and automatically identifies the root cause of the problem. For example, it analyzes the causes of lack of information sharing in a specific project. The problem identification unit also builds a system that identifies the root cause of a problem and automatically proposes solutions. For example, it proposes measures to improve information sharing and strengthen communication. The problem identification unit also builds a system that identifies the root cause of a problem and proposes specific solutions. For example, it proposes ways to resolve lack of communication in a specific team. This makes it possible to automatically identify the root cause of a problem and propose solutions.

[0079] The problem identification unit can compare data between different projects or departments and identify common issues. In the problem identification unit, for example, the generation AI compares data between different projects or departments and identifies common issues. For example, it analyzes a lack of information sharing that occurs across multiple projects. The problem identification unit can also build a system that analyzes data between different departments and identifies common issues. For example, it can identify problems with company-wide communication. In addition, the problem identification unit can compare data between different projects or departments and identify common issues. For example, it can analyze a lack of information sharing across multiple departments. This makes it possible to compare data between different projects or departments and identify common issues.

[0080] The problem identification department can compare with external benchmark data and identify gaps with industry standards. For example, the generation AI collects external benchmark data and compares it with internal data to identify gaps with industry standards. For example, it compares it with the communication styles of other companies. The problem identification department also builds a system based on external benchmark data to identify gaps in sharing and issues. For example, it evaluates the company's performance against industry standards. The problem identification department also uses the generation AI to analyze external benchmark data and identify gaps with industry standards. For example, it refers to success stories from other companies. This makes it possible to compare with external benchmark data and identify gaps with industry standards.

[0081] The problem identification unit can use the emotion estimation function to collect users' emotional reactions to lack of sharing or issues and propose solutions that are easy to empathize with emotionally. The problem identification unit, for example, uses the emotion estimation function to collect users' emotional reactions to lack of sharing or issues and propose solutions that are easy to empathize with emotionally based on that data. For example, it proposes specific methods for resolving users' dissatisfaction. The problem identification unit also analyzes users' emotional reaction data and builds a system that identifies solutions that are easy to empathize with emotionally. For example, it proposes communication methods that elicit positive emotions. The problem identification unit also uses the emotion estimation function to collect users' emotional reactions to lack of sharing or issues and propose solutions based on that data. For example, it provides solutions that take into account the user's emotions. This makes it possible to propose solutions that are easy to empathize with emotionally.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The data collection unit can also collect data from IoT devices. For example, it can collect temperature and lighting data in a conference room to evaluate the comfort of the meeting. The data collection unit can also collect data from wearable devices to monitor participants' health and stress levels. Furthermore, the data collection unit can collect data from sensors in the office to analyze the frequency and location of communication. This makes it possible to utilize data from IoT devices to improve the quality of communication.

[0084] The data analysis unit uses the emotion estimation function to analyze the user's emotions and evaluate the quality of communication based on emotional fluctuations. For example, it performs emotion estimation on email and chat data collected by the generation AI and analyzes the user's emotional fluctuations. For example, if there are a lot of positive emotions, it evaluates that communication is smooth. The data analysis unit also analyzes meeting minutes data to estimate the speaker's emotions. For example, it quantifies the intensity of emotions from the content of comments and evaluates the atmosphere of the meeting and the progress of the discussion. The data analysis unit also visualizes emotional fluctuations over time based on the data collected by the generation AI and evaluates the quality of communication over a specific period. For example, it analyzes emotional fluctuations as a project progresses. This makes it possible to evaluate the quality of communication based on emotional fluctuations.

[0085] The data analysis unit can analyze non-verbal communication elements, including the content of audio and video conferences. For example, it can analyze audio data and estimate emotions based on the speaker's tone and pitch. For example, it can quantify the degree of tension or excitement to evaluate the atmosphere of the conference. The data analysis unit can also analyze video data from video conferences and estimate the speaker's emotions using facial expression recognition technology. For example, it can read emotions from facial expressions such as smiles and furrowed brows. The data analysis unit can also integrate audio and video data and comprehensively evaluate the quality of communication by combining the content of remarks with non-verbal elements. For example, it can analyze the degree of agreement between the content of remarks and facial expressions. This allows it to analyze non-verbal communication elements.

[0086] The data analysis department can classify communication data by organizational hierarchy and department, and compare and analyze the quality of communication between departments. For example, based on the data collected by the generation AI, it can analyze the frequency and content of communication for each department. For example, it can compare the number of email exchanges and meetings between departments. The data analysis department can also evaluate the quality of communication between superiors and subordinates based on the organizational hierarchy. For example, it can analyze the frequency and content of feedback from superiors. The data analysis department can also analyze communication data for each department over time to evaluate fluctuations in communication as the project progresses. For example, it can compare the quality of communication in the early and later stages of a project. This makes it possible to compare and analyze the quality of communication between departments.

[0087] The data collection unit can analyze external feedback, including social media posts and comments. For example, social media post data is collected and the generation AI analyzes the content. For example, customer feedback and opinions about a company can be extracted. The data collection unit can also analyze social media comment data and classify positive and negative opinions. For example, product evaluations and areas for improvement can be extracted. The data collection unit can also evaluate a company's brand image and customer satisfaction based on social media data. For example, it can analyze responses to a specific campaign. This allows for external feedback to be analyzed.

[0088] The data analysis unit can use the emotion estimation function to monitor the user's emotional response to communication data in real time and provide feedback to elicit positive emotions. For example, the data analysis unit can monitor the user's emotional response to data collected by the generative AI in real time and provide feedback to elicit positive emotions. For example, it can analyze the user's facial expressions and voice and provide positive feedback. The data analysis unit also uses the emotion estimation function to analyze the user's emotional fluctuations and make positive suggestions when negative emotions are detected. For example, it can present encouraging messages or success stories. The data analysis unit also provides an interface to elicit positive emotions based on the user's emotional response data. For example, it can display designs and content that evoke positive emotions in the user. This makes it possible to monitor the user's emotional response in real time and provide feedback to elicit positive emotions.

[0089] The summary extraction unit can use the emotion estimation function to emphasize emotionally important points from communication data. For example, when the generation AI summarizes meeting minutes, it uses the emotion estimation function to emphasize emotionally important points. For example, it reflects parts that strongly express the speaker's emotions in the summary. In addition, when summarizing the contents of emails, the summary extraction unit uses the emotion estimation function to emphasize emotionally important messages. For example, it includes messages of gratitude or apology in the summary. In addition, when summarizing chat logs, the summary extraction unit uses the emotion estimation function to emphasize emotionally important exchanges. For example, it reflects the user's expressions of dissatisfaction or joy in the summary. This makes it possible to emphasize emotionally important points.

[0090] The summary extraction unit can improve the accuracy of the summary by referring to similar past communication data. For example, when the generation AI summarizes meeting minutes, it can refer to the minutes of similar past meetings to improve the accuracy of the summary. For example, it can refer to the decisions made in past meetings. In addition, when summarizing emails, the summary extraction unit can improve the accuracy of the summary by referring to the content of similar past emails. For example, it can extract important points based on past email exchanges. In addition, when summarizing chat logs, the summary extraction unit can improve the accuracy of the summary by referring to the content of similar past chats. For example, it can refer to important exchanges in past chats. This can improve the accuracy of the summary.

[0091] The summary extraction unit can automatically explain technical terms and terminology to promote understanding. For example, when the generation AI summarizes meeting minutes, it automatically explains technical terms and terminology. For example, it includes definitions of technical terms and background information in the summary. The summary extraction unit also automatically explains technical terms and terminology when summarizing emails. For example, it generates summaries that explain technical content in an easy-to-understand manner. The summary extraction unit also automatically explains technical terms and terminology when summarizing chat logs. For example, it summarizes the content of technical discussions in general terms. This makes it possible to automatically explain technical terms and terminology to promote understanding.

[0092] The summary extraction unit automatically translates summaries into different languages, allowing for feedback from an international perspective. For example, a generative AI summarizes meeting minutes and automatically translates them into different languages. For example, this can be translated into multiple languages, such as English, French, and Chinese, to collect feedback from an international perspective. The summary extraction unit also summarizes emails and automatically translates them into different languages. For example, it can provide summaries to international team members and obtain feedback. The summary extraction unit also summarizes chat logs and automatically translates them into different languages. For example, in a global project, summaries can be shared with members of different languages. This allows for automatic translation into different languages ​​and feedback from an international perspective.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection unit collects communication data. For example, the data collection unit collects data such as emails, chats, and meeting minutes. The data collection unit can also collect audio data and video data. Step 2: The data analysis unit analyzes the communication data collected by the data collection unit. For example, the data analysis unit analyzes text data using natural language processing technology. The data analysis unit can also analyze voice data using voice recognition technology. Step 3: The summary extraction unit extracts summaries and key points from the communication data analyzed by the data analysis unit. For example, the summary extraction unit uses generative AI to summarize the main points and decisions from long meeting minutes. The summary extraction unit can also extract frequently occurring keywords and important statements. Step 4: The information sharing unit quickly and effectively shares the summaries and key points extracted by the summary extraction unit. For example, the information sharing unit can automatically distribute information via email or chat. The information sharing unit can also provide real-time notifications and prioritize information. Step 5: The problem identification unit identifies issues and lack of sharing from the communication data analyzed by the data analysis unit. For example, the problem identification unit automatically detects issues such as a lack of information sharing in a specific project or an unresolved issue. The problem identification unit can also identify issues based on the number of unread messages or a lack of feedback.

[0095] 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.

[0096] 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 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 AI 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 AI 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 data collection unit that collects communication data; a data analysis unit that analyzes the communication data collected by the data collection unit; a summary extraction unit that extracts summaries and important points from the communication data analyzed by the data analysis unit; an information sharing unit that quickly and effectively shares the summaries and important matters extracted by the summary extraction unit; a problem identification unit that identifies a lack of sharing or a problem from the communication data analyzed by the data analysis unit. A system characterized by:

2. The data analysis unit Analyzing user emotions with respect to the communication data collected by the data collection unit, and evaluating the quality of communication based on fluctuations in emotions 2. The system of claim 1.

3. The data analysis unit The communication data collected by the data collection unit includes the contents of voice and video conferences, and non-verbal communication elements are analyzed.

2. The system of claim 1.

4. The data analysis unit The communication data collected by the data collection unit is classified by organizational hierarchy and department, and the quality of communication between departments is compared and analyzed.

2. The system of claim 1.

5. The data collection unit Analyze external feedback, including social media posts or comments 2. The system of claim 1.

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