system

Abstract

An object of a system according to an embodiment is to efficiently search for memo contents and respond to a consultation.SOLUTION: A system includes a memo recording part, a vector DB storage part, an information retrieval part, and a consultation response part. The memo recording unit records memo contents of the user. The vector DB storage unit stores the memo content recorded by the memo recording unit in the vector DB. An information retrieval part retrieves uncertain information on the basis of the memo contents stored by the vector DB storage part. The consultation response unit responds to the consultation based on the memo contents stored by the vector DB 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] Conventional techniques have had the problem that it is difficult to efficiently search the contents of notes and respond to inquiries.

[0005] The system according to the embodiment aims to efficiently search the contents of notes and respond to inquiries. [Means for solving the problem]

[0006] The system according to the embodiment includes a memo recording unit, a vector DB storage unit, an information search unit, and a consultation response unit. The memo recording unit records the contents of a user's memo. The vector DB storage unit stores the contents of the memo recorded by the memo recording unit in a vector DB. The information search unit searches for uncertain information based on the contents of the memo stored by the vector DB storage unit. The consultation response unit responds to the consultation based on the contents of the memo stored by the vector DB storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search the contents of notes and respond to inquiries. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 notepad system according to an embodiment of the present invention is a system that uses AI to memorize the contents of a user's notes and records them in a vector database. This system makes it easy to retrieve vaguely remembered information and enables consultations based on the information in the notes. This allows the notepad system to efficiently accumulate user knowledge and realize knowledge sharing throughout the company.

[0029] The notepad system according to the embodiment includes a note recording unit, a vector DB storage unit, an information search unit, and a consultation / response unit. The note recording unit records the contents of a user's notes. For example, it can record text notes, voice notes, image notes, etc. The note recording unit can also automatically search for related external data and add supplemental information to the note contents using a generation AI. For example, if a user records a note such as "Progress of Project A," the generation AI automatically searches the latest news and related papers on the Internet and adds the latest technological and market trends related to Project A as supplemental information. The vector DB storage unit stores the note contents recorded by the note recording unit in a vector DB. For example, the generation AI analyzes the note contents and converts them into vector format for storage. The information search unit searches for uncertain information based on the note contents stored by the vector DB storage unit. For example, if a user asks, "What's the progress of Project A?", the generation AI searches for relevant information from the vector DB and provides it to the user. The consultation / response unit responds to consultations based on the note contents stored by the vector DB storage unit. For example, if a user says, "I'd like some advice on how to improve the progress of Project A," the generative AI will search for relevant information from the vector database and provide appropriate advice. This allows the memo system to efficiently record the user's notes, easily retrieve vaguely remembered information, and enable consultations based on the notes. For example, being able to easily search and reference project progress and past meeting details improves work efficiency. It also contributes to improving teamwork by facilitating smooth information sharing between employees.

[0030] When recording memo content, the generation AI can automatically search for related external data and add supplemental information to the memo content. For example, when a user records a memo such as "Progress of Project A," the generation AI automatically searches the internet for the latest news and related papers, and adds the latest technological and market trends related to Project A as supplemental information. This improves the quantity and quality of information in the memo by automatically adding external data related to the memo content.

[0031] When recording memo content, the generation AI compares it with past memo content, detecting and notifying users of any duplication or inconsistency. For example, when a user records a memo such as "Progress of Project A," the generation AI compares it with past memo content and notifies users of any duplication if the same content has already been recorded. The generation AI also compares it with past memo content and notifies users of any contradictory information if it finds any. This helps maintain the consistency and accuracy of notes by detecting and notifying users of any duplication or inconsistency.

[0032] The memo recording unit allows users to record memo contents by voice input or handwriting input, and the generation AI can convert them into text and store them in the vector DB. For example, when a user records "Progress of Project A" by voice input, the generation AI converts the voice into text and stores it in the vector DB. Also, when a user records a memo by handwriting input, the generation AI converts the handwritten characters into text and stores it in the vector DB. This diversifies the ways in which memos can be recorded by converting voice input or handwriting input into text and storing it in the vector DB.

[0033] When recording memo content, the generation AI automatically tags it, making it easier to search for later. For example, if a user records a memo as "Progress of Project A," the generation AI automatically tags it with "Project A" and "Progress," making it easier to search for later. The generation AI also analyzes the memo content, extracts related keywords, and tags them. This automatic tagging makes it easier to search for later.

[0034] In response to a user's question, the information search unit can retrieve and provide information not only from relevant past memo content but also from external, highly reliable information sources. For example, when a user asks, "What's the progress on Project A?", the generation AI will retrieve and provide information from relevant, external, highly reliable information sources, along with past memo content. The generation AI will also search for highly reliable information sources on the Internet and provide related information. This allows the system to provide more accurate information by retrieving and providing information from external, highly reliable information sources.

[0035] The information search unit analyzes the content of the user's question, understands the intent of the question, and is able to provide the most relevant information with priority. For example, when a user asks, "What's the progress on project A?", the generation AI understands the intent of the question and provides the most relevant information with priority. The generation AI also analyzes the content of the question, understands the intent of the question, and provides related information. This makes it possible to provide information that meets the user's needs by understanding the intent of the question and providing the most relevant information with priority.

[0036] The information search unit can provide multimedia information, including related images and videos, in response to user questions. For example, when a user asks, "What's the progress on Project A?", the generation AI will provide multimedia information, including related images and videos. The generation AI will also search reliable information sources on the Internet to provide related images and videos. This makes it possible to provide information that is visually easy to understand by providing multimedia information, including related images and videos.

[0037] The information search unit can provide more accurate information in response to a user's question by referencing similar questions from the past and their answers. For example, when a user asks, "What's the progress on project A?", the generation AI will refer to similar questions from the past and their answers to provide more accurate information. The generation AI will also search a database of past questions and provide related information. By referencing similar questions from the past and their answers, it is possible to provide more accurate information.

[0038] The consultation response unit can analyze the content of the user's consultation and provide optimal advice by referring to similar past consultations and their solutions. For example, when a user asks for advice on improving the progress of Project A, the generation AI will refer to similar past consultations and their solutions and provide optimal advice. The generation AI will also search a database of past consultations and provide relevant solutions. This allows optimal advice to be provided by referring to similar past consultations and their solutions.

[0039] The consultation response unit can analyze the content of the user's consultation and suggest relevant external experts and resources. For example, when a user says, "I'd like some advice on how to improve the progress of Project A," the generation AI will suggest relevant external experts and resources. The generation AI will also search for reliable information sources on the internet and provide relevant experts and resources. This allows the system to provide more appropriate advice by suggesting relevant external experts and resources.

[0040] The consultation response unit generates relevant visual notes and mind maps for the user's consultation content, and can provide advice that is easy to understand visually. For example, when a user says, "I'd like some advice on how to improve the progress of Project A," the consultation response unit generates relevant visual notes and mind maps to provide advice that is easy to understand visually. The generation AI also analyzes the user's consultation content and provides relevant visual notes and mind maps. In this way, by generating relevant visual notes and mind maps, it is possible to provide advice that is easy to understand visually.

[0041] The consultation response unit can provide specific advice in response to the user's consultation content by referring to past success stories and failure stories. For example, when a user asks for advice on how to improve the progress of Project A, the generation AI will refer to past success stories and failure stories and provide specific advice. The generation AI will also search a database of past projects and provide related success stories and failure stories. This allows the unit to provide specific advice by referring to past success stories and failure stories.

[0042] The Vector DB Storage Unit analyzes the company's internal Vector DB and automatically tags each employee's expertise and skills, promoting appropriate information sharing. For example, the Vector DB Storage Unit uses a generation AI to analyze the company's internal Vector DB and automatically tag each employee's expertise and skills, allowing appropriate sharing of information related to Project A. The generation AI also analyzes employees' expertise and skills and provides related information. This automatically tags each employee's expertise and skills, promoting appropriate information sharing.

[0043] The vector DB storage unit can analyze an internal company's vector DB and detect and notify duplicate or contradictory information. For example, the generation AI in the vector DB storage unit analyzes an internal company's vector DB and detects and notifies duplicate or contradictory information related to Project A. The generation AI also detects and notifies duplicates and contradictions in order to maintain the consistency and accuracy of information. In this way, by detecting and notifying duplicate or contradictory information, the consistency and accuracy of information are maintained.

[0044] The Vector DB Storage Department can analyze the company's internal vector DB and propose cross-functional projects to promote information sharing between different departments. For example, the Vector DB Storage Department has the generation AI analyze the company's internal vector DB and propose cross-functional projects to promote information sharing between different departments related to Project A. The generation AI also proposes specific projects to promote information sharing between different departments. In this way, by proposing cross-functional projects to promote information sharing between different departments, information sharing within the company is activated.

[0045] The Vector DB Storage Unit can analyze the company's internal Vector DB, refer to the success and failure cases of past projects, and make suggestions for utilizing them in future projects. For example, the Generation AI in the Vector DB Storage Unit analyzes the company's internal Vector DB, refers to the success and failure cases of Project A, and makes suggestions for utilizing them in future projects. The Generation AI can also search the past project database and provide related success and failure cases. In this way, by referring to the success and failure cases of past projects, suggestions for utilizing them in future projects can be made.

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

[0047] When a user takes notes, the memo recording unit can automatically generate tasks based on the contents of the notes and send them to the task management unit. For example, if a user records a note saying "Progress of Project A," the generation AI analyzes the content and automatically generates related tasks. The generation AI also analyzes the contents of the notes, sets task priorities, and sends them to the task management unit. This allows tasks to be automatically generated based on the contents of the notes, making task management more efficient.

[0048] When a user takes notes, the memo recording section can automatically set reminders based on the contents of the notes and send them to the reminders section. For example, if a user records a note saying "Progress of Project A," the generation AI analyzes the content and automatically sets related reminders. The generation AI also analyzes the contents of the notes, sets the date and time of the reminder, and sends it to the reminders section. This allows users to automatically set reminders based on the contents of the notes and manage important tasks and events without forgetting them.

[0049] When a user takes notes, the memo recording unit can automatically link with the relevant project management tool based on the content of the note and send it to the project management unit. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the content and sends the information to the relevant project management tool. The generation AI also analyzes the content of the note and automatically updates the project progress. This automatically links with the project management tool based on the content of the note, making project management more efficient.

[0050] When a user takes notes, the memo recording unit can automatically generate minutes of related meetings based on the contents of the notes and send them to the minutes management unit. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the contents and automatically generates minutes of related meetings. The generation AI also analyzes the contents of the notes and organizes them according to the minutes format. This allows meeting minutes to be automatically generated based on the contents of the notes, making minutes management more efficient.

[0051] When a user takes notes, the memo recording unit can automatically generate related documents based on the contents of the notes and send them to the document management unit. For example, if a user records a note saying "Progress of Project A," the generation AI analyzes the content and automatically generates related documents. The generation AI also analyzes the contents of the notes and organizes them according to the document format. This allows documents to be automatically generated based on the contents of the notes, making document management more efficient.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The memo recording unit records the user's memo content. For example, text memos, audio memos, image memos, etc. can be recorded. The memo recording unit can also automatically search for related external data and add supplemental information to the memo content. For example, if a user records a memo saying "Progress of Project A," the generation AI will automatically search the latest news and related papers on the Internet and add the latest technological and market trends related to Project A as supplemental information. Step 2: The vector DB storage unit stores the memo content recorded by the memo recording unit in the vector DB. For example, the generation AI analyzes the memo content, converts it into vector format, and stores it. Step 3: The information retrieval unit searches for uncertain information based on the memo content stored by the vector DB storage unit. For example, if a user asks, "What's the progress of project A?", the generation AI searches for relevant information from the vector DB and provides it to the user. Step 4: The consultation response unit responds to the consultation based on the memo content stored in the vector DB storage unit. For example, if a user says, "I'd like some advice on how to improve the progress of Project A," the generation AI searches for relevant information in the vector DB and provides appropriate advice.

[0054] (Example 2) The notepad system according to an embodiment of the present invention is a system that uses AI to memorize the contents of a user's notes and records them in a vector database. This system makes it easy to retrieve vaguely remembered information and enables consultations based on the information in the notes. This allows the notepad system to efficiently accumulate user knowledge and realize knowledge sharing throughout the company.

[0055] The notepad system according to the embodiment includes a note recording unit, a vector DB storage unit, an information search unit, and a consultation / response unit. The note recording unit records the contents of a user's notes. For example, it can record text notes, voice notes, image notes, etc. The note recording unit can also automatically search for related external data and add supplemental information to the note contents using a generation AI. For example, if a user records a note such as "Progress of Project A," the generation AI automatically searches the latest news and related papers on the Internet and adds the latest technological and market trends related to Project A as supplemental information. The vector DB storage unit stores the note contents recorded by the note recording unit in a vector DB. For example, the generation AI analyzes the note contents and converts them into vector format for storage. The information search unit searches for uncertain information based on the note contents stored by the vector DB storage unit. For example, if a user asks, "What's the progress of Project A?", the generation AI searches for relevant information from the vector DB and provides it to the user. The consultation / response unit responds to consultations based on the note contents stored by the vector DB storage unit. For example, if a user says, "I'd like some advice on how to improve the progress of Project A," the generative AI will search for relevant information from the vector database and provide appropriate advice. This allows the memo system to efficiently record the user's notes, easily retrieve vaguely remembered information, and enable consultations based on the notes. For example, being able to easily search and reference project progress and past meeting details improves work efficiency. It also contributes to improving teamwork by facilitating smooth information sharing between employees.

[0056] When recording memo content, the generation AI can automatically search for related external data and add supplemental information to the memo content. For example, when a user records a memo such as "Progress of Project A," the generation AI automatically searches the internet for the latest news and related papers, and adds the latest technological and market trends related to Project A as supplemental information. This improves the quantity and quality of information in the memo by automatically adding external data related to the memo content.

[0057] When recording memo content, the generation AI compares it with past memo content, detecting and notifying users of any duplication or inconsistency. For example, when a user records a memo such as "Progress of Project A," the generation AI compares it with past memo content and notifies users of any duplication if the same content has already been recorded. The generation AI also compares it with past memo content and notifies users of any contradictory information if it finds any. This helps maintain the consistency and accuracy of notes by detecting and notifying users of any duplication or inconsistency.

[0058] The memo recording unit uses the emotion estimation function to analyze the user's emotions when taking notes and make suggestions to elicit positive emotions. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the user's facial expressions and voice tone and displays an encouraging message to elicit positive emotions. The generation AI also analyzes the user's emotions and suggests specific actions to elicit positive emotions. This improves the quality of notes by analyzing the user's emotions and making suggestions to elicit positive emotions.

[0059] The memo recording unit allows users to record memo contents by voice input or handwriting input, and the generation AI can convert them into text and store them in the vector DB. For example, when a user records "Progress of Project A" by voice input, the generation AI converts the voice into text and stores it in the vector DB. Also, when a user records a memo by handwriting input, the generation AI converts the handwritten characters into text and stores it in the vector DB. This diversifies the ways in which memos can be recorded by converting voice input or handwriting input into text and storing it in the vector DB.

[0060] When recording memo content, the generation AI automatically tags it, making it easier to search for later. For example, if a user records a memo as "Progress of Project A," the generation AI automatically tags it with "Project A" and "Progress," making it easier to search for later. The generation AI also analyzes the memo content, extracts related keywords, and tags them. This automatic tagging makes it easier to search for later.

[0061] When recording memo content, the generation AI can estimate the user's emotions in real time and suggest ways to organize the notes according to their emotions. For example, when a user records a memo such as "Progress of Project A," the generation AI can estimate the user's emotions in real time and suggest ways to organize the notes to elicit positive emotions. The generation AI also analyzes the user's emotions and suggests specific ways to organize the notes according to their emotions. This improves the quality of notes by estimating the user's emotions in real time and suggesting ways to organize the notes according to their emotions.

[0062] In response to a user's question, the information search unit can retrieve and provide information not only from relevant past memo content but also from external, highly reliable information sources. For example, when a user asks, "What's the progress on Project A?", the generation AI will retrieve and provide information from relevant, external, highly reliable information sources, along with past memo content. The generation AI will also search for highly reliable information sources on the Internet and provide related information. This allows the system to provide more accurate information by retrieving and providing information from external, highly reliable information sources.

[0063] The information search unit analyzes the content of the user's question, understands the intent of the question, and is able to provide the most relevant information with priority. For example, when a user asks, "What's the progress on project A?", the generation AI understands the intent of the question and provides the most relevant information with priority. The generation AI also analyzes the content of the question, understands the intent of the question, and provides related information. This makes it possible to provide information that meets the user's needs by understanding the intent of the question and providing the most relevant information with priority.

[0064] The information search unit uses the emotion estimation function to analyze the user's emotions when asking a question and can adjust the tone and content of the response according to the emotion. For example, when a user asks, "What's the progress on project A?", the information search unit uses the generation AI to analyze the user's emotions and respond with a tone and content that elicits positive emotions. The generation AI also analyzes the user's emotions and adjusts the specific tone and content of the response according to the emotion. This makes it possible to provide more appropriate information by adjusting the tone and content of the response according to the user's emotions.

[0065] The information search unit can provide multimedia information, including related images and videos, in response to user questions. For example, when a user asks, "What's the progress on Project A?", the generation AI will provide multimedia information, including related images and videos. The generation AI will also search reliable information sources on the Internet to provide related images and videos. This makes it possible to provide information that is visually easy to understand by providing multimedia information, including related images and videos.

[0066] The information search unit can provide more accurate information in response to a user's question by referencing similar questions from the past and their answers. For example, when a user asks, "What's the progress on project A?", the generation AI will refer to similar questions from the past and their answers to provide more accurate information. The generation AI will also search a database of past questions and provide related information. By referencing similar questions from the past and their answers, it is possible to provide more accurate information.

[0067] The information search unit uses the emotion estimation function to analyze the user's emotional response to a question and can provide additional information to elicit positive emotions. For example, when a user asks, "What's the progress on project A?", the information search unit uses the generation AI to analyze the user's emotional response and provide additional information to elicit positive emotions. The generation AI also analyzes the user's emotions and provides specific additional information according to the emotions. This makes it possible to provide more appropriate information by analyzing the user's emotional response and providing additional information to elicit positive emotions.

[0068] The consultation response unit can analyze the content of the user's consultation and provide optimal advice by referring to similar past consultations and their solutions. For example, when a user asks for advice on improving the progress of Project A, the generation AI will refer to similar past consultations and their solutions and provide optimal advice. The generation AI will also search a database of past consultations and provide relevant solutions. This allows optimal advice to be provided by referring to similar past consultations and their solutions.

[0069] The consultation response unit can analyze the content of the user's consultation and suggest relevant external experts and resources. For example, when a user says, "I'd like some advice on how to improve the progress of Project A," the generation AI will suggest relevant external experts and resources. The generation AI will also search for reliable information sources on the internet and provide relevant experts and resources. This allows the system to provide more appropriate advice by suggesting relevant external experts and resources.

[0070] The consultation response unit uses the emotion estimation function to analyze the user's emotions when consulting and can adjust the tone and content of the advice according to the emotions. For example, when a user consults saying, "I'd like some advice on how to improve the progress of Project A," the consultation response unit uses the generation AI to analyze the user's emotions and provide advice in a tone and content that elicits positive emotions. The generation AI also analyzes the user's emotions and adjusts the tone and content of the specific advice according to the emotions. This allows the system to provide more appropriate advice by adjusting the tone and content of the advice according to the user's emotions.

[0071] The consultation response unit generates relevant visual notes and mind maps for the user's consultation content, and can provide advice that is easy to understand visually. For example, when a user says, "I'd like some advice on how to improve the progress of Project A," the consultation response unit generates relevant visual notes and mind maps to provide advice that is easy to understand visually. The generation AI also analyzes the user's consultation content and provides relevant visual notes and mind maps. In this way, by generating relevant visual notes and mind maps, it is possible to provide advice that is easy to understand visually.

[0072] The consultation response unit can provide specific advice in response to the user's consultation content by referring to past success stories and failure stories. For example, when a user asks for advice on how to improve the progress of Project A, the generation AI will refer to past success stories and failure stories and provide specific advice. The generation AI will also search a database of past projects and provide related success stories and failure stories. This allows the unit to provide specific advice by referring to past success stories and failure stories.

[0073] The consultation response unit can use the emotion estimation function to analyze the user's emotional response to the consultation and provide additional advice to elicit positive emotions. For example, when a user asks for advice on how to improve the progress of Project A, the generation AI analyzes the user's emotional response and provides additional advice to elicit positive emotions. The generation AI also analyzes the user's emotions and provides specific additional advice according to the emotions. This allows the system to provide more appropriate advice by analyzing the user's emotional response and providing additional advice to elicit positive emotions.

[0074] The Vector DB Storage Unit analyzes the company's internal Vector DB and automatically tags each employee's expertise and skills, promoting appropriate information sharing. For example, the Vector DB Storage Unit uses a generation AI to analyze the company's internal Vector DB and automatically tag each employee's expertise and skills, allowing appropriate sharing of information related to Project A. The generation AI also analyzes employees' expertise and skills and provides related information. This automatically tags each employee's expertise and skills, promoting appropriate information sharing.

[0075] The vector DB storage unit can analyze an internal company's vector DB and detect and notify duplicate or contradictory information. For example, the generation AI in the vector DB storage unit analyzes an internal company's vector DB and detects and notifies duplicate or contradictory information related to Project A. The generation AI also detects and notifies duplicates and contradictions in order to maintain the consistency and accuracy of information. In this way, by detecting and notifying duplicate or contradictory information, the consistency and accuracy of information are maintained.

[0076] The vector DB storage unit uses the emotion estimation function to analyze the emotions employees have when sharing information and make suggestions to bring out positive emotions. For example, the vector DB storage unit uses the generation AI to analyze the emotions employees have when sharing information about Project A and make suggestions to bring out positive emotions. The generation AI also analyzes employees' emotions and suggests specific actions to bring out positive emotions. This improves the quality of information sharing by analyzing the emotions employees have when sharing information and making suggestions to bring out positive emotions.

[0077] The Vector DB Storage Department can analyze the company's internal vector DB and propose cross-functional projects to promote information sharing between different departments. For example, the Vector DB Storage Department has the generation AI analyze the company's internal vector DB and propose cross-functional projects to promote information sharing between different departments related to Project A. The generation AI also proposes specific projects to promote information sharing between different departments. In this way, by proposing cross-functional projects to promote information sharing between different departments, information sharing within the company is activated.

[0078] The Vector DB Storage Unit can analyze the company's internal Vector DB, refer to the success and failure cases of past projects, and make suggestions for utilizing them in future projects. For example, the Generation AI in the Vector DB Storage Unit analyzes the company's internal Vector DB, refers to the success and failure cases of Project A, and makes suggestions for utilizing them in future projects. The Generation AI can also search the past project database and provide related success and failure cases. In this way, by referring to the success and failure cases of past projects, suggestions for utilizing them in future projects can be made.

[0079] The vector DB storage unit can use the emotion estimation function to analyze the emotional reactions of employees when they share information and provide additional information to elicit positive emotions. For example, the vector DB storage unit uses a generation AI to analyze the emotional reactions of employees when they share information about Project A and provide additional information to elicit positive emotions. The generation AI also analyzes employees' emotions and provides specific additional information according to the emotions. This improves the quality of information sharing by analyzing the emotional reactions of employees when they share information and providing additional information to elicit positive emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When a user takes notes, the memo recording unit can automatically generate tasks based on the contents of the notes and send them to the task management unit. For example, if a user records a note saying "Progress of Project A," the generation AI analyzes the content and automatically generates related tasks. The generation AI also analyzes the contents of the notes, sets task priorities, and sends them to the task management unit. This allows tasks to be automatically generated based on the contents of the notes, making task management more efficient.

[0082] When a user takes notes, the memo recording section can automatically set reminders based on the contents of the notes and send them to the reminders section. For example, if a user records a note saying "Progress of Project A," the generation AI analyzes the content and automatically sets related reminders. The generation AI also analyzes the contents of the notes, sets the date and time of the reminder, and sends it to the reminders section. This allows users to automatically set reminders based on the contents of the notes and manage important tasks and events without forgetting them.

[0083] When a user takes notes, the memo recording unit can automatically link with the relevant project management tool based on the content of the note and send it to the project management unit. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the content and sends the information to the relevant project management tool. The generation AI also analyzes the content of the note and automatically updates the project progress. This automatically links with the project management tool based on the content of the note, making project management more efficient.

[0084] When a user takes notes, the memo recording unit can automatically generate minutes of related meetings based on the contents of the notes and send them to the minutes management unit. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the contents and automatically generates minutes of related meetings. The generation AI also analyzes the contents of the notes and organizes them according to the minutes format. This allows meeting minutes to be automatically generated based on the contents of the notes, making minutes management more efficient.

[0085] When a user takes notes, the memo recording unit can automatically generate related documents based on the contents of the notes and send them to the document management unit. For example, if a user records a note saying "Progress of Project A," the generation AI analyzes the content and automatically generates related documents. The generation AI also analyzes the contents of the notes and organizes them according to the document format. This allows documents to be automatically generated based on the contents of the notes, making document management more efficient.

[0086] When a user takes notes, the memo recording unit can use the emotion estimation function to analyze the user's emotions and suggest relaxation methods to reduce stress. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the user's emotions and suggests relaxation methods if the user is feeling stressed. The generation AI also analyzes the user's emotions and suggests specific relaxation methods. This improves the quality of notes by analyzing the user's emotions and suggesting relaxation methods to reduce stress.

[0087] When a user takes notes, the memo recording unit can use the emotion estimation function to analyze the user's emotions and play music that corresponds to the emotion. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the user's emotions and plays relaxing music if the user wants to relax. The generation AI also analyzes the user's emotions and plays music that helps the user concentrate if the user wants to concentrate. In this way, analyzing the user's emotions and playing music that corresponds to the emotion improves the quality of the memo.

[0088] When a user takes notes, the memo recording unit uses the emotion estimation function to analyze the user's emotions and suggest a memo format that matches their emotions. For example, when a user records a memo saying "Progress of Project A," the generation AI analyzes the user's emotions and suggests a format that allows them to relax if they want to relax. The generation AI also analyzes the user's emotions and suggests a format that allows them to concentrate if they want to concentrate. In this way, analyzing the user's emotions and suggesting a memo format that matches their emotions improves the quality of notes.

[0089] When a user takes notes, the memo recording unit can use the emotion estimation function to analyze the user's emotions and suggest ways to organize the content of the notes according to their emotions. For example, when a user records a note saying "Progress of Project A," the generation AI analyzes the user's emotions and suggests ways to organize the notes in a way that helps them relax if they want to relax. The generation AI also analyzes the user's emotions and suggests ways to organize the notes in a way that helps them concentrate if they want to concentrate. This improves the quality of notes by analyzing the user's emotions and suggesting ways to organize the content of the notes according to their emotions.

[0090] When a user takes notes, the memo recording unit can use the emotion estimation function to analyze the user's emotions and suggest ways to emphasize the content of the note according to their emotions. For example, when a user records a note saying "Progress of Project A," the generation AI can analyze the user's emotions and suggest ways to emphasize relaxing content if the user wants to relax. The generation AI can also analyze the user's emotions and suggest ways to emphasize content that helps the user concentrate if the user wants to concentrate. This improves the quality of notes by analyzing the user's emotions and suggesting ways to emphasize the content of the note according to their emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The memo recording unit records the user's memo content. For example, text memos, audio memos, image memos, etc. can be recorded. The memo recording unit can also automatically search for related external data and add supplemental information to the memo content. For example, if a user records a memo saying "Progress of Project A," the generation AI will automatically search the latest news and related papers on the Internet and add the latest technological and market trends related to Project A as supplemental information. Step 2: The vector DB storage unit stores the memo content recorded by the memo recording unit in the vector DB. For example, the generation AI analyzes the memo content, converts it into vector format, and stores it. Step 3: The information retrieval unit searches for uncertain information based on the memo content stored by the vector DB storage unit. For example, if a user asks, "What's the progress of project A?", the generation AI searches for relevant information from the vector DB and provides it to the user. Step 4: The consultation response unit responds to the consultation based on the memo content stored in the vector DB storage unit. For example, if a user says, "I'd like some advice on how to improve the progress of Project A," the generation AI searches for relevant information in the vector DB and provides appropriate advice.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 memo recording unit for recording the contents of a user's memo; a vector DB storage unit that stores the memo content recorded by the memo recording unit in a vector DB; an information retrieval unit that retrieves uncertain information based on the memo content stored by the vector DB storage unit; a consultation response unit that responds to a consultation based on the memo content stored by the vector DB storage unit; A system characterized by:

2. The information search unit In response to the user's questions, the system provides information not only from past notes but also from external, highly reliable sources.

2. The system of claim 1.

3. The consultation response unit Analyze the user's consultation content, refer to past similar consultations and their solutions, and provide optimal advice 2. The system of claim 1.

4. The vector DB storage unit Analyzes the company's internal vector database, automatically tags employees' expertise and skills, and promotes appropriate information sharing 2. The system of claim 1.

5. The memo recording unit Analyzing the emotions of the user when taking notes and making suggestions to elicit the emotions 2. The system of claim 1.

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