System

Abstract

An object of a system according to an embodiment is to reduce the time taken to search for a file and maintain the concentration of a business person.SOLUTION: A system according to an embodiment includes an interaction unit, a metadata storage unit, and a file presentation unit. The interaction unit performs interaction with a user. The metadata storage unit stores the information collected by the dialogue unit as metadata. The file presentation unit presents an optimum file based on the metadata stored by the metadata storage 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] With conventional technology, business people had to spend a lot of time searching for files, which disrupted their concentration.

[0005] The system according to the embodiment aims to reduce the time spent searching for files and maintain the concentration of business people. [Means for solving the problem]

[0006] A system according to an embodiment includes a dialogue unit, a metadata storage unit, and a file presentation unit. The dialogue unit dialogues with a user. The metadata storage unit stores information collected by the dialogue unit as metadata. The file presentation unit presents optimal files based on the metadata stored by the metadata storage unit. [Effects of the Invention]

[0007] The system according to the embodiment reduces the time spent searching for files, allowing business people to maintain their concentration. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​file management system according to an embodiment of the present invention is a system that reduces the time business people spend searching for files, helps them maintain their concentration, and realizes a stress-free life. As a result, the AI ​​file management system reduces the time business people spend searching for files, helps them maintain their concentration, and realizes a stress-free life.

[0029] An AI file management system according to an embodiment includes a dialogue unit, a metadata storage unit, and a file presentation unit. The dialogue unit dialogues with a user. For example, when a user creates a report for a new project, the generation AI asks questions such as, "What is the project name of this report?" and "Please tell me the date this report was created." When a user searches for a file, the dialogue unit analyzes the metadata and instantly displays the latest sales report when the user performs a search such as, "Show me the latest sales report." The metadata storage unit stores information collected by the dialogue unit as metadata. For example, the generation AI stores the project name and creation date collected when the user creates a report for a new project as metadata. The metadata storage unit also stores the search keywords and search date and time collected when the user searches for a file as metadata. The file presentation unit presents optimal files based on the metadata stored by the metadata storage unit. For example, when a user performs a search such as, "I'm looking for a report for Project X from last year," the generation AI instantly presents relevant files based on the metadata. In addition, when a user searches for something like "Show me the latest sales report," the file presentation unit analyzes the metadata and immediately displays the latest sales report. This allows the AI ​​file management system according to the embodiment to store the content of the user's dialogue as metadata and quickly present the most suitable file.

[0030] The dialogue unit can optimize the dialogue by referencing the user's past dialogue history and learning the user's preferences and patterns. For example, the generation AI analyzes the user's past dialogue history and learns the user's preferred question formats and response patterns. For example, if the user prefers detailed explanations, the generation AI asks detailed questions. The dialogue unit also learns the user's preferences and patterns based on the user's past dialogue history and optimizes the dialogue. For example, if the user frequently uses a specific keyword, the generation AI asks a question containing that keyword. The dialogue unit also refers to the user's past dialogue history and advances the dialogue based on information previously provided by the user. For example, the generation AI automatically suggests project names previously entered by the user. This makes it possible to improve user satisfaction by optimizing the dialogue based on the user's preferences and patterns.

[0031] The dialogue unit can analyze the user's gestures or facial expressions during the dialogue and also store non-verbal information as metadata. For example, the dialogue unit's generation AI can use a camera to analyze the user's gestures and facial expressions and store the non-verbal information as metadata. For example, it can record the number of times the user nods. The dialogue unit can also analyze the user's gestures and facial expressions and the generation AI can adjust the content of the dialogue. For example, if the user shows a confused expression, it can provide additional explanation. The dialogue unit can also analyze the user's gestures and facial expressions and store the non-verbal information as metadata. For example, it can record the number of times the user smiles. In this way, by analyzing the user's gestures and facial expressions and storing non-verbal information as metadata, more detailed information can be provided.

[0032] The dialogue unit can automatically save external materials or links referenced by the user during a dialogue as metadata. For example, the dialogue unit automatically saves external materials or links referenced by the user during a dialogue by the generation AI as metadata. For example, it records the URL of a web page opened by the user. The dialogue unit also automatically saves external materials or links referenced by the user during a dialogue by the generation AI as metadata. For example, it records the title of a PDF file viewed by the user. The dialogue unit also automatically saves external materials or links referenced by the user during a dialogue by the generation AI as metadata. For example, it records the URL of a link clicked by the user. In this way, the external materials and links referenced by the user can be easily referenced later by automatically saving them as metadata.

[0033] The metadata storage unit can automatically summarize the contents of a file and save the summary as metadata. For example, the metadata storage unit allows a generation AI to automatically summarize the contents of a file and save the summary as metadata. For example, it extracts and saves the main points of a report. The metadata storage unit also analyzes the contents of a file, allows a generation AI to automatically generate a summary, and saves the summary as metadata. For example, it summarizes the main slides of a presentation. The metadata storage unit also allows a generation AI to automatically summarize the contents of a file and save the summary as metadata. For example, it summarizes and saves the body of an email. In this way, automatically summarizing the contents of a file and saving the summary as metadata makes it easier to search for files.

[0034] The metadata storage unit can analyze the relevance of files and link related files together. For example, when the generation AI saves metadata, the metadata storage unit analyzes the relevance of files and links related files together. For example, files related to the same project are linked. The metadata storage unit also analyzes the content of files, and the generation AI finds relevance and links related files together. For example, files containing the same keywords are linked. The metadata storage unit also analyzes the relevance of files and links related files together when the generation AI saves metadata. For example, files created by the same creator are linked. In this way, linking related files makes it easier to search for related information.

[0035] The metadata storage unit performs file version management and can also store different versions of metadata. For example, the generation AI performs file version management in the metadata storage unit and stores different versions of metadata. For example, it records the creation date of each version of a report. The metadata storage unit also performs file version management in the metadata storage unit and stores different versions of metadata. For example, it records the changes made to each version of a presentation. The metadata storage unit also performs file version management in the metadata storage unit and stores different versions of metadata. For example, it records the send date and time of each version of an email. In this way, by performing file version management and storing different versions of metadata, it is possible to track the change history of a file.

[0036] The metadata storage unit can record the file access history and store as metadata who accessed it and when. For example, the generation AI records the file access history and stores as metadata who accessed it and when. For example, it records the user who viewed a report and the date and time. The metadata storage unit also records the file access history and stores as metadata who accessed it and when. For example, it records the user who viewed a presentation and the date and time. The metadata storage unit also records the file access history and stores as metadata who accessed it and when. For example, it records the user who viewed an email and the date and time. In this way, by recording the file access history and storing as metadata who accessed it and when, it is possible to understand the usage status of the file.

[0037] The file presentation unit can analyze the user's search history, learn the user's search patterns, and present the most suitable files. For example, the file presentation unit uses a generation AI to analyze the user's search history, learn the user's search patterns, and present the most suitable files. For example, it presents files based on keywords that the user frequently searches for. The file presentation unit also uses a generation AI to learn the user's search patterns based on the user's search history and present the most suitable files. For example, it prioritizes the presentation of files that the user has searched for in the past. The file presentation unit also uses a generation AI to analyze the user's search history, learn the user's search patterns, and present the most suitable files. For example, it presents files that the user searches for during a specific time period. This improves the efficiency of file searches by learning the user's search patterns and presenting the most suitable files.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The dialogue unit can refer to the user's past dialogue history and learn the user's preferences and patterns to optimize the dialogue. For example, the generation AI analyzes the user's past dialogue history and learns the user's preferred question formats and response patterns. For example, if the user prefers detailed explanations, it will ask detailed questions. The dialogue unit also learns the user's preferences and patterns based on the user's past dialogue history and optimizes the dialogue. For example, if the user frequently uses a specific keyword, it will ask a question containing that keyword. The dialogue unit also refers to the user's past dialogue history and advances the dialogue based on information previously provided by the user. For example, it automatically suggests project names previously entered by the user. This makes it possible to improve user satisfaction by optimizing the dialogue based on the user's preferences and patterns.

[0040] The dialogue unit can analyze the user's gestures or facial expressions during the dialogue and also store non-verbal information as metadata. For example, the generation AI can use a camera to analyze the user's gestures and facial expressions and store non-verbal information as metadata. For example, it can record the number of times the user nods. The dialogue unit can also analyze the user's gestures and facial expressions and the generation AI can adjust the content of the dialogue. For example, if the user shows a confused expression, it can provide additional explanation. The dialogue unit can also analyze the user's gestures and facial expressions and store non-verbal information as metadata. For example, it can record the number of times the user smiles. In this way, by analyzing the user's gestures and facial expressions and storing non-verbal information as metadata, more detailed information can be provided.

[0041] The dialogue unit can automatically save external materials or links referenced by the user during a dialogue as metadata. For example, the generation AI automatically saves external materials or links referenced by the user during a dialogue as metadata. For example, it records the URL of the web page opened by the user. The dialogue unit also allows the generation AI to automatically save external materials or links referenced by the user during a dialogue as metadata. For example, it records the title of a PDF file viewed by the user. The dialogue unit also allows the generation AI to automatically save external materials or links referenced by the user during a dialogue as metadata. For example, it records the URL of a link clicked by the user. This allows the external materials and links referenced by the user to be automatically saved as metadata, making them easy to reference later.

[0042] The metadata storage unit can automatically summarize the contents of a file and save that summary as metadata. For example, the generation AI can automatically summarize the contents of a file and save that summary as metadata. For example, it can extract and save the main points of a report. The metadata storage unit can also analyze the contents of a file, have the generation AI automatically generate a summary, and save that summary as metadata. For example, it can summarize the main slides of a presentation. The metadata storage unit can also have the generation AI automatically summarize the contents of a file and save that summary as metadata. For example, it can summarize and save the body of an email. In this way, automatically summarizing the contents of a file and saving that summary as metadata makes it easier to search for files.

[0043] The metadata storage unit can analyze the relevance of files and link related files together. For example, when the generation AI saves metadata, it analyzes the relevance of files and links related files together. For example, it links files related to the same project. The metadata storage unit also analyzes the content of the files, and the generation AI finds relevance and links related files together. For example, it links files that contain the same keywords. The metadata storage unit also analyzes the relevance of files when the generation AI saves metadata and links related files together. For example, it links files created by the same creator. In this way, linking related files makes it easier to search for related information.

[0044] The metadata storage unit manages file versions and can also store different versions of metadata. For example, the generation AI manages file versions and stores different versions of metadata. For example, it records the creation date of each version of a report. The metadata storage unit also manages file versions and stores different versions of metadata. For example, it records the changes made to each version of a presentation. The metadata storage unit also manages file versions and stores different versions of metadata. For example, it records the send date and time of each version of an email. In this way, by managing file versions and storing different versions of metadata, it is possible to track the change history of a file.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The dialogue unit interacts with the user. For example, when a user creates a new project report, the generation AI asks questions such as, "What is the project name of this report?" or "Please tell me the date this report was created." When a user searches for a file, the dialogue unit searches for something like, "Show me the latest sales report," and the generation AI analyzes the metadata and instantly displays the latest sales report. Step 2: The metadata storage unit stores the information collected by the dialogue unit as metadata. For example, when a user creates a report for a new project, the generation AI stores the project name and creation date collected as metadata. The metadata storage unit also stores the search keywords and search date and time collected when a user searches for a file as metadata. Step 3: The file presentation unit presents the most suitable file based on the metadata stored by the metadata storage unit. For example, if a user searches for something like "I'm looking for last year's report on Project X," the generation AI instantly presents the relevant file based on the metadata. Similarly, if a user searches for something like "Show me the latest sales report," the file presentation unit analyzes the metadata and instantly displays the latest sales report.

[0047] (Example 2) The AI ​​file management system according to an embodiment of the present invention is a system that reduces the time business people spend searching for files, helps them maintain their concentration, and realizes a stress-free life. As a result, the AI ​​file management system reduces the time business people spend searching for files, helps them maintain their concentration, and realizes a stress-free life.

[0048] An AI file management system according to an embodiment includes a dialogue unit, a metadata storage unit, and a file presentation unit. The dialogue unit dialogues with a user. For example, when a user creates a report for a new project, the generation AI asks questions such as, "What is the project name of this report?" and "Please tell me the date this report was created." When a user searches for a file, the dialogue unit analyzes the metadata and instantly displays the latest sales report when the user performs a search such as, "Show me the latest sales report." The metadata storage unit stores information collected by the dialogue unit as metadata. For example, the generation AI stores the project name and creation date collected when the user creates a report for a new project as metadata. The metadata storage unit also stores the search keywords and search date and time collected when the user searches for a file as metadata. The file presentation unit presents optimal files based on the metadata stored by the metadata storage unit. For example, when a user performs a search such as, "I'm looking for a report for Project X from last year," the generation AI instantly presents relevant files based on the metadata. In addition, when a user searches for something like "Show me the latest sales report," the file presentation unit analyzes the metadata and immediately displays the latest sales report. This allows the AI ​​file management system according to the embodiment to store the content of the user's dialogue as metadata and quickly present the most suitable file.

[0049] The dialogue unit can analyze the user's tone of voice or speaking style, estimate the user's emotional state, and adjust the dialogue content accordingly. For example, the dialogue unit's generation AI can analyze the user's tone of voice in real time, and if the user is feeling stressed, it can engage in dialogue to relax them. For example, it can speak in a gentle tone. The dialogue unit can also analyze the user's speaking style and estimate their emotional state, allowing the generation AI to select appropriate questions and responses. For example, if the user is in a hurry, it can ask brief questions. The dialogue unit can also analyze the user's tone of voice and speaking style, and if the user is feeling positive, it can provide positive feedback. For example, it can use compliments. This can reduce the user's stress by providing dialogue content that matches the user's emotional state.

[0050] The dialogue unit can optimize the dialogue by referencing the user's past dialogue history and learning the user's preferences and patterns. For example, the generation AI analyzes the user's past dialogue history and learns the user's preferred question formats and response patterns. For example, if the user prefers detailed explanations, the generation AI asks detailed questions. The dialogue unit also learns the user's preferences and patterns based on the user's past dialogue history and optimizes the dialogue. For example, if the user frequently uses a specific keyword, the generation AI asks a question containing that keyword. The dialogue unit also refers to the user's past dialogue history and advances the dialogue based on information previously provided by the user. For example, the generation AI automatically suggests project names previously entered by the user. This makes it possible to improve user satisfaction by optimizing the dialogue based on the user's preferences and patterns.

[0051] The dialogue unit can use the emotion estimation function to engage in dialogue to relax the user if the user is feeling stressed. For example, the generation AI analyzes the user's tone of voice and speaking style, and if the user is feeling stressed, engages in dialogue to relax the user. For example, it speaks in a gentle tone. The dialogue unit also uses the emotion estimation function to engage in dialogue to relax the user if the user is feeling stressed. For example, it plays music that has a relaxing effect. The dialogue unit also estimates the user's emotional state, and if the user is feeling stressed, engages in dialogue to relax the user. For example, it provides a topic that has a relaxing effect. In this way, if the user is feeling stressed, a dialogue to relax the user can be engaged in, thereby reducing the user's stress.

[0052] The dialogue unit can analyze the user's gestures or facial expressions during the dialogue and also store non-verbal information as metadata. For example, the dialogue unit's generation AI can use a camera to analyze the user's gestures and facial expressions and store the non-verbal information as metadata. For example, it can record the number of times the user nods. The dialogue unit can also analyze the user's gestures and facial expressions and the generation AI can adjust the content of the dialogue. For example, if the user shows a confused expression, it can provide additional explanation. The dialogue unit can also analyze the user's gestures and facial expressions and store the non-verbal information as metadata. For example, it can record the number of times the user smiles. In this way, by analyzing the user's gestures and facial expressions and storing non-verbal information as metadata, more detailed information can be provided.

[0053] The dialogue unit can automatically save external materials or links referenced by the user during a dialogue as metadata. For example, the dialogue unit automatically saves external materials or links referenced by the user during a dialogue by the generation AI as metadata. For example, it records the URL of a web page opened by the user. The dialogue unit also automatically saves external materials or links referenced by the user during a dialogue by the generation AI as metadata. For example, it records the title of a PDF file viewed by the user. The dialogue unit also automatically saves external materials or links referenced by the user during a dialogue by the generation AI as metadata. For example, it records the URL of a link clicked by the user. In this way, the external materials and links referenced by the user can be easily referenced later by automatically saving them as metadata.

[0054] The dialogue unit can use the emotion estimation function to preferentially save as metadata dialogue content in which the user has positive emotions. For example, the dialogue unit uses the emotion estimation function to preferentially save as metadata dialogue content in which the user has positive emotions. For example, the dialogue unit records dialogue content in which the user smiles. Furthermore, the dialogue unit uses the generation AI to analyze the emotional state of the user and preferentially save as metadata dialogue content in which the user has positive emotions. For example, the dialogue unit records dialogue content in which the user felt satisfied. Furthermore, the dialogue unit uses the emotion estimation function to preferentially save as metadata dialogue content in which the user has positive emotions. For example, the dialogue unit records dialogue content in which the user felt happy. In this way, by preferentially saving as metadata dialogue content in which the user has positive emotions, user satisfaction can be improved.

[0055] The metadata storage unit can automatically summarize the contents of a file and save the summary as metadata. For example, the metadata storage unit allows a generation AI to automatically summarize the contents of a file and save the summary as metadata. For example, it extracts and saves the main points of a report. The metadata storage unit also analyzes the contents of a file, allows a generation AI to automatically generate a summary, and saves the summary as metadata. For example, it summarizes the main slides of a presentation. The metadata storage unit also allows a generation AI to automatically summarize the contents of a file and save the summary as metadata. For example, it summarizes and saves the body of an email. In this way, automatically summarizing the contents of a file and saving the summary as metadata makes it easier to search for files.

[0056] The metadata storage unit can analyze the relevance of files and link related files together. For example, when the generation AI saves metadata, the metadata storage unit analyzes the relevance of files and links related files together. For example, files related to the same project are linked. The metadata storage unit also analyzes the content of files, and the generation AI finds relevance and links related files together. For example, files containing the same keywords are linked. The metadata storage unit also analyzes the relevance of files and links related files together when the generation AI saves metadata. For example, files created by the same creator are linked. In this way, linking related files makes it easier to search for related information.

[0057] The metadata storage unit can use the emotion estimation function to prioritize saving metadata that the user finds particularly important. The metadata storage unit, for example, uses the emotion estimation function to prioritize saving metadata that the user finds particularly important. For example, it records keywords that the user has shown strong interest in. The metadata storage unit also analyzes the user's emotional state using the generation AI and prioritizes saving metadata that the user finds particularly important. For example, it records metadata of files that the user finds satisfying. The metadata storage unit also uses the emotion estimation function to prioritize saving metadata that the user finds particularly important. For example, it records metadata of files that the user finds enjoyable. In this way, priority is given to saving metadata that the user finds particularly important, making it easier to search for important information.

[0058] The metadata storage unit performs file version management and can also store different versions of metadata. For example, the generation AI performs file version management in the metadata storage unit and stores different versions of metadata. For example, it records the creation date of each version of a report. The metadata storage unit also performs file version management in the metadata storage unit and stores different versions of metadata. For example, it records the changes made to each version of a presentation. The metadata storage unit also performs file version management in the metadata storage unit and stores different versions of metadata. For example, it records the send date and time of each version of an email. In this way, by performing file version management and storing different versions of metadata, it is possible to track the change history of a file.

[0059] The metadata storage unit can record the file access history and store as metadata who accessed it and when. For example, the generation AI records the file access history and stores as metadata who accessed it and when. For example, it records the user who viewed a report and the date and time. The metadata storage unit also records the file access history and stores as metadata who accessed it and when. For example, it records the user who viewed a presentation and the date and time. The metadata storage unit also records the file access history and stores as metadata who accessed it and when. For example, it records the user who viewed an email and the date and time. In this way, by recording the file access history and storing as metadata who accessed it and when, it is possible to understand the usage status of the file.

[0060] The metadata storage unit can use the emotion estimation function to preferentially store metadata of files for which the user has positive emotions. For example, the metadata storage unit uses the emotion estimation function to preferentially store metadata of files for which the user has positive emotions. For example, it records metadata of files that the user is satisfied with. Furthermore, the metadata storage unit uses the generation AI to analyze the user's emotional state and preferentially store metadata of files for which the user has positive emotions. For example, it records metadata of files for which the user felt joy. Furthermore, the metadata storage unit uses the emotion estimation function to preferentially store metadata of files for which the user has positive emotions. For example, it records metadata of files for which the user smiles. In this way, by preferentially storing metadata of files for which the user has positive emotions, user satisfaction can be improved.

[0061] The file presentation unit can analyze the user's search history, learn the user's search patterns, and present the most suitable files. For example, the file presentation unit uses a generation AI to analyze the user's search history, learn the user's search patterns, and present the most suitable files. For example, it presents files based on keywords that the user frequently searches for. The file presentation unit also uses a generation AI to learn the user's search patterns based on the user's search history and present the most suitable files. For example, it prioritizes the presentation of files that the user has searched for in the past. The file presentation unit also uses a generation AI to analyze the user's search history, learn the user's search patterns, and present the most suitable files. For example, it presents files that the user searches for during a specific time period. This improves the efficiency of file searches by learning the user's search patterns and presenting the most suitable files.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The dialogue unit can analyze the user's tone of voice or speaking style, infer the user's emotional state, and adjust the dialogue content accordingly. For example, the generation AI can analyze the user's tone of voice in real time, and if the user is feeling stressed, it can provide dialogue to relax them. For example, it can speak in a gentle tone. The dialogue unit can also analyze the user's speaking style and infer their emotional state, allowing the generation AI to select appropriate questions and responses. For example, if the user is in a hurry, it can ask brief questions. The dialogue unit can also analyze the user's tone of voice and speaking style, and if the user is feeling positive, it can provide positive feedback. For example, it can use compliments. This allows the dialogue content to be provided according to the user's emotional state, thereby reducing the user's stress.

[0064] The dialogue unit can refer to the user's past dialogue history and learn the user's preferences and patterns to optimize the dialogue. For example, the generation AI analyzes the user's past dialogue history and learns the user's preferred question formats and response patterns. For example, if the user prefers detailed explanations, it will ask detailed questions. The dialogue unit also learns the user's preferences and patterns based on the user's past dialogue history and optimizes the dialogue. For example, if the user frequently uses a specific keyword, it will ask a question containing that keyword. The dialogue unit also refers to the user's past dialogue history and advances the dialogue based on information previously provided by the user. For example, it automatically suggests project names previously entered by the user. This makes it possible to improve user satisfaction by optimizing the dialogue based on the user's preferences and patterns.

[0065] The dialogue unit can use the emotion estimation function to engage in dialogue to relax the user if the user is feeling stressed. For example, the generation AI analyzes the user's tone of voice and speaking style, and if the user is feeling stressed, engages in dialogue to relax the user. For example, it speaks in a gentle tone. The dialogue unit can also use the emotion estimation function to engage in dialogue to relax the user if the user is feeling stressed. For example, it can play music that has a relaxing effect. The dialogue unit can also estimate the user's emotional state, and if the user is feeling stressed, engage in dialogue to relax the user. For example, it can provide a topic that has a relaxing effect. In this way, if the user is feeling stressed, a dialogue to relax the user can be engaged in, thereby reducing the user's stress.

[0066] The dialogue unit can analyze the user's gestures or facial expressions during the dialogue and also store non-verbal information as metadata. For example, the generation AI can use a camera to analyze the user's gestures and facial expressions and store non-verbal information as metadata. For example, it can record the number of times the user nods. The dialogue unit can also analyze the user's gestures and facial expressions and the generation AI can adjust the content of the dialogue. For example, if the user shows a confused expression, it can provide additional explanation. The dialogue unit can also analyze the user's gestures and facial expressions and store non-verbal information as metadata. For example, it can record the number of times the user smiles. In this way, by analyzing the user's gestures and facial expressions and storing non-verbal information as metadata, more detailed information can be provided.

[0067] The dialogue unit can automatically save external materials or links referenced by the user during a dialogue as metadata. For example, the generation AI automatically saves external materials or links referenced by the user during a dialogue as metadata. For example, it records the URL of the web page opened by the user. The dialogue unit also allows the generation AI to automatically save external materials or links referenced by the user during a dialogue as metadata. For example, it records the title of a PDF file viewed by the user. The dialogue unit also allows the generation AI to automatically save external materials or links referenced by the user during a dialogue as metadata. For example, it records the URL of a link clicked by the user. This allows the external materials and links referenced by the user to be automatically saved as metadata, making them easy to reference later.

[0068] The dialogue unit can use the emotion estimation function to preferentially save as metadata dialogue content in which the user has positive emotions. For example, the emotion estimation function is used to preferentially save as metadata dialogue content in which the user has positive emotions. For example, dialogue content in which the user smiles is recorded. Furthermore, the dialogue unit uses the generation AI to analyze the emotional state of the user and preferentially save as metadata dialogue content in which the user has positive emotions. For example, dialogue content in which the user felt satisfied is recorded. Furthermore, the dialogue unit uses the emotion estimation function to preferentially save as metadata dialogue content in which the user has positive emotions. For example, dialogue content in which the user felt joy. In this way, by preferentially saving as metadata dialogue content in which the user has positive emotions, user satisfaction can be improved.

[0069] The metadata storage unit can automatically summarize the contents of a file and save that summary as metadata. For example, the generation AI can automatically summarize the contents of a file and save that summary as metadata. For example, it can extract and save the main points of a report. The metadata storage unit can also analyze the contents of a file, have the generation AI automatically generate a summary, and save that summary as metadata. For example, it can summarize the main slides of a presentation. The metadata storage unit can also have the generation AI automatically summarize the contents of a file and save that summary as metadata. For example, it can summarize and save the body of an email. In this way, automatically summarizing the contents of a file and saving that summary as metadata makes it easier to search for files.

[0070] The metadata storage unit can analyze the relevance of files and link related files together. For example, when the generation AI saves metadata, it analyzes the relevance of files and links related files together. For example, it links files related to the same project. The metadata storage unit also analyzes the content of the files, and the generation AI finds relevance and links related files together. For example, it links files that contain the same keywords. The metadata storage unit also analyzes the relevance of files when the generation AI saves metadata and links related files together. For example, it links files created by the same creator. In this way, linking related files makes it easier to search for related information.

[0071] The metadata storage unit can use the emotion estimation function to prioritize saving metadata that the user finds particularly important. For example, the emotion estimation function can be used to prioritize saving metadata that the user finds particularly important. For example, keywords that the user has shown strong interest in can be recorded. The metadata storage unit also uses the generation AI to analyze the user's emotional state and prioritize saving metadata that the user finds particularly important. For example, metadata of files that the user finds satisfying can be recorded. The metadata storage unit also uses the emotion estimation function to prioritize saving metadata that the user finds particularly important. For example, metadata of files that the user finds enjoyable can be recorded. This prioritizes saving metadata that the user finds particularly important, making it easier to search for important information.

[0072] The metadata storage unit manages file versions and can also store different versions of metadata. For example, the generation AI manages file versions and stores different versions of metadata. For example, it records the creation date of each version of a report. The metadata storage unit also manages file versions and stores different versions of metadata. For example, it records the changes made to each version of a presentation. The metadata storage unit also manages file versions and stores different versions of metadata. For example, it records the send date and time of each version of an email. In this way, by managing file versions and storing different versions of metadata, it is possible to track the change history of a file.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The dialogue unit interacts with the user. For example, when a user creates a new project report, the generation AI asks questions such as, "What is the project name of this report?" or "Please tell me the date this report was created." When a user searches for a file, the dialogue unit searches for something like, "Show me the latest sales report," and the generation AI analyzes the metadata and instantly displays the latest sales report. Step 2: The metadata storage unit stores the information collected by the dialogue unit as metadata. For example, when a user creates a report for a new project, the generation AI stores the project name and creation date collected as metadata. The metadata storage unit also stores the search keywords and search date and time collected when a user searches for a file as metadata. Step 3: The file presentation unit presents the most suitable file based on the metadata stored by the metadata storage unit. For example, if a user searches for something like "I'm looking for last year's report on Project X," the generation AI instantly presents the relevant file based on the metadata. Similarly, if a user searches for something like "Show me the latest sales report," the file presentation unit analyzes the metadata and instantly displays the latest sales report.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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]

[0142] 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 dialogue unit that dialogues with a user; a metadata storage unit that stores the information collected by the dialogue unit as metadata; a file presenting unit that presents an optimal file based on the metadata stored by the metadata storage unit. A system characterized by:

2. The dialogue unit Analyzing the tone of voice or speaking style of the user and estimating the emotional state of the user to adjust the dialogue content 2. The system of claim 1.

3. The dialogue unit Refer to the user's past interaction history, learn the user's preferences and patterns, and optimize the interaction.

2. The system of claim 1.

4. The dialogue unit If the user is feeling stressed, a dialogue is provided to help the user relax.

2. The system of claim 1.

5. The dialogue unit Analyze the user's gestures or facial expressions during the interaction and store non-verbal information as metadata.

2. The system of claim 1.

Images