System

Abstract

An object of the system according to the embodiment is to collectively manage and operate various types of data.SOLUTION: A system according to an embodiment includes a data collection part, a data storage part, a data retrieval part, a data analysis part, a data sharing part, and an access management part. The data collection unit collects data. The data storage unit stores the data collected by the data collection unit. The data retrieval part retrieves the data stored by the data storage part. The data analysis unit analyzes the data retrieved by the data retrieval unit. The data sharing unit shares the data analyzed by the data analysis unit. The access management unit manages access to the data shared by the data sharing 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 technologies do not adequately manage and operate various types of data in a unified manner, and there is room for improvement.

[0005] The system according to the embodiment aims to collectively manage and operate various types of data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data storage unit, a data search unit, a data analysis unit, a data sharing unit, and an access management unit. The data collection unit collects data. The data storage unit stores the data collected by the data collection unit. The data search unit searches for the data stored by the data storage unit. The data analysis unit analyzes the data searched by the data search unit. The data sharing unit shares the data analyzed by the data analysis unit. The access management unit manages access to the data shared by the data sharing unit. [Effects of the Invention]

[0007] The system according to the embodiment can collectively manage and operate various types of data. [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 data management system according to an embodiment of the present invention is a system for collectively managing and operating a wide variety of data (text, audio, video, etc.) used both inside and outside of business. This data management system is an integrated platform for efficiently collecting, storing, searching, analyzing, and sharing data. As a result, the data management system can collectively manage and operate a wide variety of data used both inside and outside of business.

[0029] A data management system according to an embodiment includes a data collection unit, a data storage unit, a data search unit, a data analysis unit, a data sharing unit, and an access management unit. The data collection unit collects data. For example, the data collection unit collects meeting minutes, presentation slides, call records with customers, and surveillance camera footage. The data collection unit also uses a generation AI to assign appropriate metadata to the collected data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of the data and generate metadata. The data storage unit stores the data collected by the data collection unit. For example, the data storage unit stores the data in a database. The data storage unit also periodically creates backups of the data. The data search unit searches the data stored by the data storage unit. For example, the data search unit searches for text data containing specific keywords. The data search unit can also search for voice data of a specific person. The data analysis unit analyzes the data searched by the data search unit. For example, the data analysis unit uses the generation AI to analyze the content of the data and extract important information and trends. The data sharing unit shares the data analyzed by the data analysis unit. For example, the data sharing unit shares data within a project team. The data sharing unit can also set access permissions for specific users. The access management unit manages access to the data shared by the data sharing unit. For example, the access management unit sets appropriate access permissions depending on the importance and confidentiality of the data. This allows the data management system according to the embodiment to collectively manage and operate a wide variety of data. For example, the data management system can efficiently collect, store, search, analyze, and share data. The data management system can also achieve efficient information sharing while ensuring data security.

[0030] In the data collection section, the generation AI evaluates the reliability of the data when collecting it, allowing it to prioritize storing highly reliable data. For example, to evaluate the reliability of collected data, the data collection section has the generation AI analyze information about the data's origin and creator and calculate a reliability score. For example, data from official sources is stored as highly rated. In addition, to evaluate the reliability of data, the data collection section has the generation AI cross-check the content of the data to confirm consistency and accuracy. For example, if the same information is obtained from multiple sources, the data is stored as highly rated. In addition, in order for the generation AI to evaluate the reliability of data, the data collection section compares it with past data and detects contradictions and inconsistencies. For example, information that matches past data is stored as highly rated. This allows highly reliable data to be stored preferentially.

[0031] The data collection unit enables the generation AI to automatically detect duplicate data and eliminate the duplicate data before storing it. For example, the data collection unit analyzes the content of the data and calculates a similarity score so that the generation AI can detect duplicate data. For example, it compares the content of text data and eliminates data with high similarity as duplicates. In addition, to detect duplicate data, the data collection unit analyzes the metadata of the data (creation date and time, creator, file size, etc.) and identifies data that is likely to be duplicates. For example, it eliminates data with the same creation date and file size as duplicates. In addition, in order for the generation AI to detect duplicate data, the data collection unit compares previously saved data with newly collected data and eliminates matching data as duplicates. For example, it compares new data with a past database. This allows duplicate data to be eliminated before storing.

[0032] The data collection unit can translate data collected using the generation AI in real time and store it in a multilingual database. For example, the data collection unit translates collected text data in real time using the generation AI and stores it in a database that supports multiple languages. For example, the data collection unit translates and stores the data into English, Japanese, French, etc. When collecting audio data, the generation AI performs speech recognition and translation in real time and stores the data in a multilingual database as text data. For example, the data collection unit translates and stores the audio of a meeting in real time. When collecting video data, the generation AI translates the text and audio in the video in real time and stores the data in a multilingual database as subtitles or captions. For example, the data collection unit translates and stores the video of a presentation. This allows the data to be stored in a multilingual database.

[0033] In the data collection unit, the generation AI understands the context of the data as it collects it, and automatically generates and stores associated metadata. For example, in the data collection unit, the generation AI analyzes the context of collected text data and automatically generates and stores associated metadata (keywords, topics, categories, etc.). For example, important keywords are extracted from meeting minutes and stored. In addition, when collecting audio data, the generation AI analyzes the content of the audio and automatically generates and stores associated metadata (speaker, topic, time, etc.). For example, speaker information is extracted from customer call records and stored. In addition, when collecting video data, the generation AI analyzes the content of the video and automatically generates and stores associated metadata (scenes, characters, locations, etc.). For example, scene information is extracted from surveillance camera footage and stored. This allows the data context to be understood, and associated metadata to be automatically generated and stored.

[0034] The data search unit performs time series analysis of data and can extract trends and patterns over time. In the data search unit, for example, the generation AI performs time series analysis of data and extracts trends and patterns over a specific period. For example, it analyzes trends from customer feedback over the past year. The data search unit also visualizes data fluctuations and patterns based on time series analysis. For example, it performs time series analysis of sales data and extracts seasonal trends. In the data search unit, the generation AI also performs time series analysis of data to detect abnormal patterns and fluctuations. For example, it detects abnormal increases or decreases in sales and analyzes the causes. This makes it possible to extract trends and patterns over time.

[0035] The data search unit can evaluate the relevance of data and automatically link and display related data. For example, the data search unit builds a system in which the generation AI evaluates the relevance of data and automatically links and displays related data. For example, customer feedback and product specifications are linked and displayed. In addition, to evaluate the relevance of data, the generation AI analyzes the content of the data and extracts related keywords and topics. For example, data related to the same topic is linked and displayed. In addition, the data search unit evaluates the relevance of data and visualizes related data. For example, related data is displayed in graphs and charts to allow users to understand intuitively. This allows related data to be automatically linked and displayed.

[0036] The data search unit can visualize search results, allowing users to intuitively understand them. The data search unit, for example, builds a system in which the generation AI visualizes search results, allowing users to intuitively understand them. For example, it displays search results in graphs or charts. To visualize the search results, the data search unit also analyzes the content of the data and selects an appropriate visual format. For example, it displays a bar graph or pie chart depending on the type of data. The data search unit also visualizes search results, allowing users to operate them interactively. For example, it allows users to access detailed information by clicking or zooming on the search results. In this way, the search results are visualized, allowing users to intuitively understand them.

[0037] The data search unit integrates different data sources and can search and analyze information from multiple data sources in one go. For example, the generation AI integrates different data sources to build a system that searches and analyzes information from multiple data sources in one go. For example, it integrates an internal database with an external database. To integrate different data sources, the generation AI analyzes the format and structure of the data and converts it into a unified format. For example, it integrates and searches data in different formats. The generation AI also integrates different data sources to extract correlations and patterns. For example, it integrates and analyzes internal data and market data. This allows information from multiple data sources to be searched and analyzed in one go.

[0038] The access management unit can evaluate the confidentiality of data and automatically set access restrictions for highly sensitive data. For example, the access management unit analyzes the content and origin of data and calculates a confidentiality score so that the generation AI can evaluate the confidentiality of the data. For example, it sets strict access restrictions for data that contains highly confidential information. To evaluate the confidentiality of data, the access management unit analyzes the metadata of the data (creator, creation date and time, file size, etc.) and identifies highly confidential data. For example, it sets access restrictions for data related to a specific project. The access management unit also builds a system in which the generation AI evaluates the confidentiality of data and automatically sets access restrictions for highly sensitive data. For example, it dynamically adjusts access permissions based on the confidentiality score. This makes it possible to automatically set access restrictions for highly sensitive data.

[0039] The access management unit tracks data usage history and can detect unauthorized access and abnormal usage patterns. For example, the access management unit builds a system in which the generation AI tracks data usage history and detects abnormal usage patterns. For example, it detects access from unusual times or locations. To detect unauthorized access, the access management unit also analyzes data usage history and identifies abnormal access patterns. For example, it issues an alert if a large amount of data is downloaded in a short period of time. The access management unit also tracks data usage history and monitors abnormal usage patterns in real time. For example, it immediately sets access restrictions if abnormal access is detected. This makes it possible to detect unauthorized access and abnormal usage patterns.

[0040] The data sharing unit can automatically generate a summary of data when data is shared and provide it to the user with whom it is shared. The data sharing unit, for example, builds a system in which a generation AI automatically generates a summary of data and provides it to the user with whom it is shared. For example, it summarizes and shares long reports or meeting minutes. In addition, when data is shared, the generation AI analyzes the content of the data, extracts important points, and generates a summary. For example, it summarizes and shares presentation slides. In addition, in the data sharing unit, the generation AI generates a summary of data in real time and provides it to the user with whom it is shared. For example, it summarizes and shares call records with customers. This allows a summary of data to be automatically generated when data is shared and provided to the user with whom it is shared.

[0041] The data sharing unit can analyze data sharing history and propose optimal sharing patterns. For example, the data sharing unit builds a system in which a generation AI analyzes data sharing history and proposes optimal sharing patterns. For example, it proposes effective sharing methods based on past sharing history. The data sharing unit also analyzes data sharing history and identifies optimal sharing patterns based on the reactions and feedback of users with whom the data is shared. For example, it proposes sharing methods that receive the most positive reactions. The data sharing unit also analyzes data sharing history using a generation AI to optimize the timing and method of sharing. For example, it suggests that sharing at a specific time period or in a specific format is effective. This makes it possible to analyze data sharing history and propose optimal sharing patterns.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data management system may further include a data visualization unit. The data visualization unit visually represents collected data, allowing users to intuitively understand it. For example, the data visualization unit may display collected text data as a word cloud. The data visualization unit may also display audio data as a waveform or spectrogram. The data visualization unit may also display video data as thumbnails or a timeline. This allows users to grasp the contents of the data at a glance.

[0044] The data collection unit can also be equipped with a data anonymization function. This function can remove or mask personal information from collected data. For example, personal information such as names and addresses can be removed from text data. It can also anonymize specific speakers' voices by converting them from audio data. It can also obscure personal information such as faces and license plates from video data. This allows data to be collected and stored while protecting the privacy of the data.

[0045] The data collection unit can further include a real-time data monitoring function. The real-time monitoring function can monitor collected data in real time and detect abnormalities. For example, it can analyze surveillance camera footage in real time to detect suspicious movements. It can also analyze audio data in real time to detect abnormal sounds. It can also analyze text data in real time to detect inappropriate content. This allows for early detection of abnormalities and rapid response.

[0046] The data collection unit may further include a data compression function. The data compression function efficiently compresses collected data and can save storage space. For example, text data can be compressed and saved. Audio data can also be compressed and saved. Furthermore, video data can be compressed and saved. This saves data storage space and allows for efficient data management.

[0047] The data collection unit can further include a data version management function. The data version management function manages different versions of collected data and can revert to a previous version as needed. For example, it can save the editing history of text data and revert to a previous version. It can also save the editing history of audio data and revert to a previous version. It can also save the editing history of video data and revert to a previous version. This makes it possible to manage the data change history and revert to a previous version as needed.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The data collection unit collects data. For example, it collects meeting minutes, presentation slides, call records with customers, and surveillance camera footage. The data collection unit also uses a generation AI to assign appropriate metadata to the collected data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of the data and generate metadata. Step 2: The data storage unit stores the data collected by the data collection unit. For example, the data storage unit stores the data in a database. The data storage unit also periodically creates backups of the data. Step 3: The data search unit searches the data stored by the data storage unit. For example, the data search unit searches for text data containing a specific keyword. The data search unit can also search for voice data of a specific person. Step 4: The data analysis unit analyzes the data retrieved by the data search unit. For example, the data analysis unit uses a generation AI to analyze the content of the data and extract important information and trends. Step 5: The data sharing unit shares the data analyzed by the data analysis unit. For example, the data sharing unit shares the data within the project team. The data sharing unit can also set access permissions for specific users. Step 6: The access management unit manages access to the data shared by the data sharing unit. For example, the access management unit sets appropriate access rights depending on the importance and confidentiality of the data.

[0050] (Example 2) A data management system according to an embodiment of the present invention is a system for collectively managing and operating a wide variety of data (text, audio, video, etc.) used both inside and outside of business. This data management system is an integrated platform for efficiently collecting, storing, searching, analyzing, and sharing data. As a result, the data management system can collectively manage and operate a wide variety of data used both inside and outside of business.

[0051] A data management system according to an embodiment includes a data collection unit, a data storage unit, a data search unit, a data analysis unit, a data sharing unit, and an access management unit. The data collection unit collects data. For example, the data collection unit collects meeting minutes, presentation slides, call records with customers, and surveillance camera footage. The data collection unit also uses a generation AI to assign appropriate metadata to the collected data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of the data and generate metadata. The data storage unit stores the data collected by the data collection unit. For example, the data storage unit stores the data in a database. The data storage unit also periodically creates backups of the data. The data search unit searches the data stored by the data storage unit. For example, the data search unit searches for text data containing specific keywords. The data search unit can also search for voice data of a specific person. The data analysis unit analyzes the data searched by the data search unit. For example, the data analysis unit uses the generation AI to analyze the content of the data and extract important information and trends. The data sharing unit shares the data analyzed by the data analysis unit. For example, the data sharing unit shares data within a project team. The data sharing unit can also set access permissions for specific users. The access management unit manages access to the data shared by the data sharing unit. For example, the access management unit sets appropriate access permissions depending on the importance and confidentiality of the data. This allows the data management system according to the embodiment to collectively manage and operate a wide variety of data. For example, the data management system can efficiently collect, store, search, analyze, and share data. The data management system can also achieve efficient information sharing while ensuring data security.

[0052] The data collection unit performs an emotion analysis on the collected data and can classify and store the data based on the intensity and type of emotion. For example, the data collection unit performs an emotion analysis on each piece of data and quantifies the intensity and type of emotion based on the emotion score. For example, emotions such as joy, sadness, and surprise are expressed numerically, and data with high specific emotion scores is preferentially stored. The data collection unit also analyzes the user's emotional state in real time when collecting data and automatically filters and stores data with strong positive emotions. Specifically, the data collection unit analyzes the user's facial expressions and tone of voice when entering data and calculates an emotion score. The data collection unit also uses emotion analysis to build a filtering system that eliminates data containing negative emotions. For example, data with high scores for anger and sadness are not stored in the database, thereby collecting only positive data. This allows data to be classified and stored based on the intensity and type of emotion.

[0053] In the data collection section, the generation AI evaluates the reliability of the data when collecting it, allowing it to prioritize storing highly reliable data. For example, to evaluate the reliability of collected data, the data collection section has the generation AI analyze information about the data's origin and creator and calculate a reliability score. For example, data from official sources is stored as highly rated. In addition, to evaluate the reliability of data, the data collection section has the generation AI cross-check the content of the data to confirm consistency and accuracy. For example, if the same information is obtained from multiple sources, the data is stored as highly rated. In addition, in order for the generation AI to evaluate the reliability of data, the data collection section compares it with past data and detects contradictions and inconsistencies. For example, information that matches past data is stored as highly rated. This allows highly reliable data to be stored preferentially.

[0054] The data collection unit enables the generation AI to automatically detect duplicate data and eliminate the duplicate data before storing it. For example, the data collection unit analyzes the content of the data and calculates a similarity score so that the generation AI can detect duplicate data. For example, it compares the content of text data and eliminates data with high similarity as duplicates. In addition, to detect duplicate data, the data collection unit analyzes the metadata of the data (creation date and time, creator, file size, etc.) and identifies data that is likely to be duplicates. For example, it eliminates data with the same creation date and file size as duplicates. In addition, in order for the generation AI to detect duplicate data, the data collection unit compares previously saved data with newly collected data and eliminates matching data as duplicates. For example, it compares new data with a past database. This allows duplicate data to be eliminated before storing.

[0055] The data collection unit can translate data collected using the generation AI in real time and store it in a multilingual database. For example, the data collection unit translates collected text data in real time using the generation AI and stores it in a database that supports multiple languages. For example, the data collection unit translates and stores the data into English, Japanese, French, etc. When collecting audio data, the generation AI performs speech recognition and translation in real time and stores the data in a multilingual database as text data. For example, the data collection unit translates and stores the audio of a meeting in real time. When collecting video data, the generation AI translates the text and audio in the video in real time and stores the data in a multilingual database as subtitles or captions. For example, the data collection unit translates and stores the video of a presentation. This allows the data to be stored in a multilingual database.

[0056] In the data collection unit, the generation AI understands the context of the data as it collects it, and automatically generates and stores associated metadata. For example, in the data collection unit, the generation AI analyzes the context of collected text data and automatically generates and stores associated metadata (keywords, topics, categories, etc.). For example, important keywords are extracted from meeting minutes and stored. In addition, when collecting audio data, the generation AI analyzes the content of the audio and automatically generates and stores associated metadata (speaker, topic, time, etc.). For example, speaker information is extracted from customer call records and stored. In addition, when collecting video data, the generation AI analyzes the content of the video and automatically generates and stores associated metadata (scenes, characters, locations, etc.). For example, scene information is extracted from surveillance camera footage and stored. This allows the data context to be understood, and associated metadata to be automatically generated and stored.

[0057] The data collection unit can use an emotion estimation function to evaluate the emotional value of collected data and prioritize saving data with positive emotions. For example, the data collection unit performs emotion estimation on collected text data and prioritizes saving data with a high positive emotion score. For example, it extracts and saves positive comments from customer feedback. When collecting audio data, the data collection unit uses a generation AI to analyze the emotion in the audio and prioritize saving data with strong positive emotions. For example, it extracts and saves positive conversations from call records with customers. When collecting video data, the data collection unit uses a generation AI to analyze the emotion in the video and prioritize saving scenes with strong positive emotions. For example, it extracts and saves positive reactions from video of a presentation. This allows data with positive emotions to be prioritized for saving.

[0058] The data search unit performs sentiment analysis on the search query and can optimize search results based on the user's sentiment. For example, the data search unit performs sentiment analysis on the user's search query and prioritizes displaying data with positive sentiment as search results. For example, if a user searches for "success stories," positive stories are prioritized. The data search unit also provides search results that match the user's sentiment based on the sentiment analysis of the search query. For example, if a user searches for "problem solving," positive solutions are prioritized. The data search unit also performs sentiment analysis on the user's search query and filters out data with negative sentiment. For example, if a user searches for "failure stories," negative stories are excluded from the display. This allows search results to be optimized based on the user's sentiment.

[0059] The data search unit performs time series analysis of data and can extract trends and patterns over time. In the data search unit, for example, the generation AI performs time series analysis of data and extracts trends and patterns over a specific period. For example, it analyzes trends from customer feedback over the past year. The data search unit also visualizes data fluctuations and patterns based on time series analysis. For example, it performs time series analysis of sales data and extracts seasonal trends. In the data search unit, the generation AI also performs time series analysis of data to detect abnormal patterns and fluctuations. For example, it detects abnormal increases or decreases in sales and analyzes the causes. This makes it possible to extract trends and patterns over time.

[0060] The data search unit can evaluate the relevance of data and automatically link and display related data. For example, the data search unit builds a system in which the generation AI evaluates the relevance of data and automatically links and displays related data. For example, customer feedback and product specifications are linked and displayed. In addition, to evaluate the relevance of data, the generation AI analyzes the content of the data and extracts related keywords and topics. For example, data related to the same topic is linked and displayed. In addition, the data search unit evaluates the relevance of data and visualizes related data. For example, related data is displayed in graphs and charts to allow users to understand intuitively. This allows related data to be automatically linked and displayed.

[0061] The data search unit can visualize search results, allowing users to intuitively understand them. The data search unit, for example, builds a system in which the generation AI visualizes search results, allowing users to intuitively understand them. For example, it displays search results in graphs or charts. To visualize the search results, the data search unit also analyzes the content of the data and selects an appropriate visual format. For example, it displays a bar graph or pie chart depending on the type of data. The data search unit also visualizes search results, allowing users to operate them interactively. For example, it allows users to access detailed information by clicking or zooming on the search results. In this way, the search results are visualized, allowing users to intuitively understand them.

[0062] The data search unit integrates different data sources and can search and analyze information from multiple data sources in one go. For example, the generation AI integrates different data sources to build a system that searches and analyzes information from multiple data sources in one go. For example, it integrates an internal database with an external database. To integrate different data sources, the generation AI analyzes the format and structure of the data and converts it into a unified format. For example, it integrates and searches data in different formats. The generation AI also integrates different data sources to extract correlations and patterns. For example, it integrates and analyzes internal data and market data. This allows information from multiple data sources to be searched and analyzed in one go.

[0063] The data search unit uses the emotion estimation function to collect users' emotional responses to search results, allowing the search algorithm to be continuously improved. For example, the data search unit collects users' emotional responses to search results in real time and builds a system to improve the search algorithm based on that data. For example, the data search unit calculates an emotion score by analyzing the user's facial expressions and voice. The data search unit also uses the emotion estimation function to collect users' emotional responses to search results and preferentially display search results with a high number of positive responses. For example, the search results are ranked based on the user's emotion score. The data search unit also identifies areas for improvement in the search algorithm based on the user's emotional response data. For example, the data search unit filters search results with a high number of negative responses. This allows the search algorithm to be continuously improved by collecting users' emotional responses to search results.

[0064] The data sharing unit performs sentiment analysis when sharing data and can optimize the sharing method based on the emotions of the recipient user. For example, when sharing data, the data sharing unit uses a generation AI to analyze the emotions of the recipient user and propose a sharing method that elicits positive emotions. For example, the data sharing unit adjusts the content and format of the data to be shared to share it in a way that is easy for the user to accept. The data sharing unit also uses a generation AI to analyze the emotions of the recipient user in real time and dynamically adjust the sharing method. For example, it changes the timing and method of sharing depending on the user's emotional state. The data sharing unit also uses sentiment analysis to prioritize sharing data that the recipient user has positive emotions about. For example, it selects and shares positive data based on the user's past emotional reactions. This makes it possible to optimize the sharing method based on the emotions of the recipient user.

[0065] The access management unit can evaluate the confidentiality of data and automatically set access restrictions for highly sensitive data. For example, the access management unit analyzes the content and origin of data and calculates a confidentiality score so that the generation AI can evaluate the confidentiality of the data. For example, it sets strict access restrictions for data that contains highly confidential information. To evaluate the confidentiality of data, the access management unit analyzes the metadata of the data (creator, creation date and time, file size, etc.) and identifies highly confidential data. For example, it sets access restrictions for data related to a specific project. The access management unit also builds a system in which the generation AI evaluates the confidentiality of data and automatically sets access restrictions for highly sensitive data. For example, it dynamically adjusts access permissions based on the confidentiality score. This makes it possible to automatically set access restrictions for highly sensitive data.

[0066] The access management unit tracks data usage history and can detect unauthorized access and abnormal usage patterns. For example, the access management unit builds a system in which the generation AI tracks data usage history and detects abnormal usage patterns. For example, it detects access from unusual times or locations. To detect unauthorized access, the access management unit also analyzes data usage history and identifies abnormal access patterns. For example, it issues an alert if a large amount of data is downloaded in a short period of time. The access management unit also tracks data usage history and monitors abnormal usage patterns in real time. For example, it immediately sets access restrictions if abnormal access is detected. This makes it possible to detect unauthorized access and abnormal usage patterns.

[0067] The data sharing unit can automatically generate a summary of data when data is shared and provide it to the user with whom it is shared. The data sharing unit, for example, builds a system in which a generation AI automatically generates a summary of data and provides it to the user with whom it is shared. For example, it summarizes and shares long reports or meeting minutes. In addition, when data is shared, the generation AI analyzes the content of the data, extracts important points, and generates a summary. For example, it summarizes and shares presentation slides. In addition, in the data sharing unit, the generation AI generates a summary of data in real time and provides it to the user with whom it is shared. For example, it summarizes and shares call records with customers. This allows a summary of data to be automatically generated when data is shared and provided to the user with whom it is shared.

[0068] The data sharing unit can analyze data sharing history and propose optimal sharing patterns. For example, the data sharing unit builds a system in which a generation AI analyzes data sharing history and proposes optimal sharing patterns. For example, it proposes effective sharing methods based on past sharing history. The data sharing unit also analyzes data sharing history and identifies optimal sharing patterns based on the reactions and feedback of users with whom the data is shared. For example, it proposes sharing methods that receive the most positive reactions. The data sharing unit also analyzes data sharing history using a generation AI to optimize the timing and method of sharing. For example, it suggests that sharing at a specific time period or in a specific format is effective. This makes it possible to analyze data sharing history and propose optimal sharing patterns.

[0069] The data sharing unit can use the emotion estimation function to evaluate the user's emotions when sharing data and suggest a sharing method that elicits positive emotions. For example, when sharing data, the data sharing unit uses a generation AI to analyze the user's emotions in real time and suggest a sharing method that elicits positive emotions. For example, the data sharing unit adjusts the content and format of the data to be shared so that it is shared in a way that is easy for the user to accept. The data sharing unit also uses the emotion estimation function to evaluate the emotions of the user receiving the data and provide an interface for eliciting positive emotions. For example, it presents encouraging messages or success stories. The data sharing unit also optimizes the timing and method of data sharing based on the user's emotional response. For example, it changes the timing and method of sharing depending on the user's emotional state. This makes it possible to evaluate the user's emotions when sharing data and suggest a sharing method that elicits positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The data management system may further include a data visualization unit. The data visualization unit visually represents collected data, allowing users to intuitively understand it. For example, the data visualization unit may display collected text data as a word cloud. The data visualization unit may also display audio data as a waveform or spectrogram. The data visualization unit may also display video data as thumbnails or a timeline. This allows users to grasp the contents of the data at a glance.

[0072] The data collection unit can also be equipped with a data anonymization function. This function can remove or mask personal information from collected data. For example, personal information such as names and addresses can be removed from text data. It can also anonymize specific speakers' voices by converting them from audio data. It can also obscure personal information such as faces and license plates from video data. This allows data to be collected and stored while protecting the privacy of the data.

[0073] The data collection unit can further include a real-time data monitoring function. The real-time monitoring function can monitor collected data in real time and detect abnormalities. For example, it can analyze surveillance camera footage in real time to detect suspicious movements. It can also analyze audio data in real time to detect abnormal sounds. It can also analyze text data in real time to detect inappropriate content. This allows for early detection of abnormalities and rapid response.

[0074] The data collection unit may further include a data compression function. The data compression function efficiently compresses collected data and can save storage space. For example, text data can be compressed and saved. Audio data can also be compressed and saved. Furthermore, video data can be compressed and saved. This saves data storage space and allows for efficient data management.

[0075] The data collection unit can further include a data version management function. The data version management function manages different versions of collected data and can revert to a previous version as needed. For example, it can save the editing history of text data and revert to a previous version. It can also save the editing history of audio data and revert to a previous version. It can also save the editing history of video data and revert to a previous version. This makes it possible to manage the data change history and revert to a previous version as needed.

[0076] The data collection unit uses an emotion estimation function to evaluate the emotional value of collected data and prioritize saving data with positive emotions. For example, emotion estimation is performed on collected text data, and data with a high positive emotion score is prioritized for saving. For example, positive comments are extracted and saved from customer feedback. When collecting audio data, the data collection unit uses a generation AI to analyze the emotion in the audio and prioritize saving data with strong positive emotions. For example, positive conversations are extracted and saved from customer call records. When collecting video data, the data collection unit uses a generation AI to analyze the emotion in the video and prioritize saving scenes with strong positive emotions. For example, positive reactions are extracted and saved from presentation footage. This allows data with positive emotions to be prioritized for saving.

[0077] The data search unit performs sentiment analysis on the search query and can optimize search results based on the user's sentiment. For example, sentiment analysis is performed on the user's search query, and data with positive sentiment is preferentially displayed as search results. For example, if a user searches for "success stories," positive stories are preferentially displayed. The data search unit also provides search results that match the user's sentiment based on the sentiment analysis of the search query. For example, if a user searches for "problem solving," positive solutions are preferentially displayed. The data search unit also performs sentiment analysis on the user's search query and filters out data with negative sentiment. For example, if a user searches for "failure stories," negative stories are excluded and displayed. This allows search results to be optimized based on the user's sentiment.

[0078] The data search unit can use the emotion estimation function to collect users' emotional responses to search results and continuously improve the search algorithm. For example, a system can be built that collects users' emotional responses to search results in real time and improves the search algorithm based on that data. For example, an emotion score can be calculated by analyzing the user's facial expressions and voice. The data search unit can also use the emotion estimation function to collect users' emotional responses to search results and prioritize displaying search results with a high number of positive responses. For example, it can rank search results based on the user's emotion score. The data search unit can also identify areas for improvement in the search algorithm based on the user's emotional response data. For example, it can filter search results with a high number of negative responses. This allows the system to collect users' emotional responses to search results and continuously improve the search algorithm.

[0079] The data sharing unit performs sentiment analysis when sharing data and can optimize the sharing method based on the recipient user's emotions. For example, when sharing data, the generation AI analyzes the recipient user's emotions and suggests a sharing method that elicits positive emotions. For example, it adjusts the content and format of the data to be shared to share it in a way that is easy for the user to accept. The data sharing unit also analyzes the recipient user's emotions in real time and dynamically adjusts the sharing method. For example, it changes the timing and method of sharing depending on the user's emotional state. The data sharing unit also uses sentiment analysis to prioritize sharing data that the recipient user has positive emotions about. For example, it selects and shares positive data based on the user's past emotional reactions. This makes it possible to optimize the sharing method based on the recipient user's emotions.

[0080] The data sharing unit can use the emotion estimation function to evaluate the user's emotions when sharing data and suggest a sharing method that elicits positive emotions. For example, when sharing data, the generative AI can analyze the user's emotions in real time and suggest a sharing method that elicits positive emotions. For example, it can adjust the content and format of the data to be shared and share it in a way that is easy for the user to accept. The data sharing unit also uses the emotion estimation function to evaluate the emotions of the user receiving the data and provide an interface to elicit positive emotions. For example, it can present encouraging messages or success stories. The data sharing unit also optimizes the timing and method of data sharing based on the user's emotional response. For example, it can change the timing and method of sharing depending on the user's emotional state. This makes it possible to evaluate the user's emotions when sharing data and suggest a sharing method that elicits positive emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The data collection unit collects data. For example, it collects meeting minutes, presentation slides, call records with customers, and surveillance camera footage. The data collection unit also uses a generation AI to assign appropriate metadata to the collected data. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of the data and generate metadata. Step 2: The data storage unit stores the data collected by the data collection unit. For example, the data storage unit stores the data in a database. The data storage unit also periodically creates backups of the data. Step 3: The data search unit searches the data stored by the data storage unit. For example, the data search unit searches for text data containing a specific keyword. The data search unit can also search for voice data of a specific person. Step 4: The data analysis unit analyzes the data retrieved by the data search unit. For example, the data analysis unit uses a generation AI to analyze the content of the data and extract important information and trends. Step 5: The data sharing unit shares the data analyzed by the data analysis unit. For example, the data sharing unit shares the data within the project team. The data sharing unit can also set access permissions for specific users. Step 6: The access management unit manages access to the data shared by the data sharing unit. For example, the access management unit sets appropriate access rights depending on the importance and confidentiality of the data.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0119] The 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.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 data; a data storage unit that stores the data collected by the data collection unit; a data search unit that searches for the data stored by the data storage unit; a data analysis unit that analyzes the data searched by the data search unit; a data sharing unit that shares the data analyzed by the data analysis unit; an access management unit that manages access to the data shared by the data sharing unit; A system characterized by:

2. The data collection unit Performing a sentiment analysis of the collected data and categorizing and storing the data based on the intensity and type of sentiment.

2. The system of claim 1.

3. The data collection unit When collecting data, the AI ​​evaluates the reliability of the data and prioritizes storing the highly reliable data.

2. The system of claim 1.

4. The data collection unit Generative AI automatically detects duplicates of the data, eliminates them, and saves them.

2. The system of claim 1.

5. The data collection unit The collected data is translated in real time using generative AI and stored in a multilingual database.

2. The system of claim 1.

6. The data collection unit Generative AI understands the context of data as it is collected and automatically generates and stores relevant metadata 2. The system of claim 1.

7. The data collection unit Evaluate the emotional value of the collected data and prioritize saving data with positive emotions 2. The system of claim 1.

8. The data search unit Perform sentiment analysis on search queries and optimize search results based on user sentiment 2. The system of claim 1.

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