system

Abstract

The system according to the embodiment aims to improve business efficiency by centrally managing in-house contacts, minutes, and schedules. [Solution] A system according to an embodiment includes a management unit, a transcription unit, a minutes-taking unit, and a schedule management unit. The management unit centrally manages internal contact information. The transcription unit transcribes based on the contact information managed by the management unit. The minutes-taking unit creates minutes based on the text data generated by the transcription unit. The schedule management unit manages schedules based on the minutes created by the minutes-taking unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, internal contacts, minutes, and schedule management were scattered, making efficient management difficult.

[0005] The system according to the embodiment aims to improve business efficiency by centrally managing in-house contacts, minutes, and schedules. [Means for solving the problem]

[0006] The system according to the embodiment includes a management unit, a transcription unit, a minutes-taking unit, and a schedule management unit. The management unit centrally manages internal contact information. The transcription unit transcribes information based on the contact information managed by the management unit. The minutes-taking unit creates minutes based on the text data generated by the transcription unit. The schedule management unit manages schedules based on the minutes created by the minutes-taking unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage internal company contacts, minutes, and schedules, thereby improving work efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The metaverse system according to an embodiment of the present invention allows users to instantly participate in meetings using a PC or tablet app, without requiring a dedicated device such as VR goggles. This metaverse system improves work efficiency by combining the metaverse (a comfortable space) with the generation AI (convenience). Specifically, the generation AI centrally manages all internal contacts and consultations, and handles transcription, announcements, meeting minutes, and schedule management. It can also be integrated with various chat and schedule management tools. This allows users to instantly participate in the metaverse using a PC or tablet without requiring a dedicated device, allowing them to efficiently advance their work. For example, when a user launches an app on their PC or tablet, the work environment within the metaverse is displayed. The user can use the generation AI to search for internal contacts or input consultations. The generation AI analyzes the input information and provides appropriate contact information and responses. It can also transcribe comments made during meetings in real time and automatically generate meeting minutes. Furthermore, by integrating with a schedule management tool, meeting schedules can be automatically adjusted and notified to participants. In this way, by combining the metaverse and generative AI, the present invention provides a system that improves work efficiency and allows users to work comfortably. As a result, the metaverse system allows users to instantly join the metaverse using a PC or tablet without requiring a dedicated device, allowing them to work efficiently.

[0029] The metaverse system according to the embodiment includes a management unit, a transcription unit, a minutes-taking unit, and a schedule management unit. The management unit centrally manages internal contacts. For example, the management unit registers internal contacts in a database and provides appropriate contacts. The management unit can also use a generation AI to search for internal contacts and provide appropriate contacts. For example, the management unit searches for and displays relevant contacts based on keywords entered by a user. The transcription unit transcribes statements made during meetings in real time. For example, the transcription unit converts statements made during meetings into text data in real time using speech recognition technology. The transcription unit can also transcribe statements made during meetings in real time using a generation AI. For example, the transcription unit acquires statements made during meetings as audio data and inputs it into the generation AI, which then analyzes the audio data to generate text data. The minutes-taking unit automatically generates minutes based on the generated text data. For example, the minutes-taking unit organizes the generated text data according to a format and creates minutes. The minutes-taking unit can also use the generation AI to automatically generate minutes based on the generated text data. For example, the minutes-taking unit inputs the generated text data into the generation AI, which then automatically generates minutes. The schedule management unit manages the schedule based on the minutes. For example, the schedule management unit automatically adjusts the meeting schedule listed in the minutes and notifies the participants. The schedule management unit can also use the generation AI to manage the schedule based on the minutes. For example, the schedule management unit inputs the minutes into the generation AI, which then automatically adjusts the schedule and notifies the participants. As a result, the metaverse system according to the embodiment can efficiently perform centralized management of internal contacts, transcription, minutes-taking, and schedule management.

[0030] The management unit can search for internal contacts and provide the contacts. The management unit, for example, registers internal contacts in a database and provides appropriate contacts. The management unit can also use a generation AI to search for internal contacts and provide appropriate contacts. For example, the management unit searches for and displays relevant contacts based on keywords entered by the user. This makes it possible to efficiently search for internal contacts and provide appropriate contacts. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs keywords entered by the user into the generation AI, which then searches for and displays relevant contacts.

[0031] The transcription unit can transcribe statements made during a meeting in real time. The transcription unit converts statements made during a meeting into text data in real time using, for example, voice recognition technology. The transcription unit can also transcribe statements made during a meeting in real time using a generation AI. For example, the transcription unit acquires statements made during a meeting as audio data and inputs the data into a generation AI, which then analyzes the audio data to generate text data. This enables the rapid creation of meeting minutes by transcribing statements made during a meeting in real time. Some or all of the above-described processing in the transcription unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the transcription unit acquires statements made during a meeting as audio data and inputs the data into a generation AI, which then analyzes the audio data to generate text data.

[0032] The minutes-taking unit can automatically generate minutes based on the generated character data. For example, the minutes-taking unit organizes the generated character data according to a format and creates minutes. The minutes-taking unit can also automatically generate minutes based on the generated character data using a generation AI. For example, the minutes-taking unit inputs the generated character data into the generation AI, which then automatically generates minutes. This improves the efficiency of creating minutes by automatically generating minutes based on the generated character data. Some or all of the above-mentioned processing in the minutes-taking unit may be performed using, or without, the generation AI. For example, the minutes-taking unit inputs the generated character data into the generation AI, which then automatically generates minutes.

[0033] The schedule management unit can automatically adjust the meeting schedule and notify participants. The schedule management unit, for example, automatically adjusts the meeting schedule recorded in the minutes and notifies participants. The schedule management unit can also manage the schedule based on the minutes using a generation AI. For example, the schedule management unit inputs the minutes into a generation AI, which then automatically adjusts the schedule and notifies participants. This automatically adjusts the meeting schedule and notifies participants, thereby streamlining schedule management. Some or all of the above-mentioned processing in the schedule management unit may be performed using, or without, the generation AI. For example, the schedule management unit inputs the minutes into a generation AI, which then automatically adjusts the schedule and notifies participants.

[0034] The management unit can cooperate with multiple chat or schedule management tools. For example, the management unit cooperates with multiple chat tools or schedule management tools to centrally manage information. The management unit can also cooperate with multiple chat tools or schedule management tools using a generation AI. For example, the management unit acquires information using the APIs of various chat tools or schedule management tools, and the generation AI integrates and manages that information. This enables centralized management of information by coordinating with various chat or schedule management tools. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit acquires information using the APIs of various chat tools or schedule management tools, and the generation AI integrates and manages that information.

[0035] The management unit can analyze the user's past contact history and automatically suggest contacts. The management unit, for example, stores the user's past contact history in a database and suggests appropriate contacts. The management unit can also analyze the user's past contact history and suggest appropriate contacts using a generation AI. For example, the management unit inputs the user's past contact history into the generation AI, which analyzes the contact history and suggests appropriate contacts. In this way, optimal contacts can be automatically suggested by analyzing the user's past contact history. Some or all of the above-mentioned processing in the management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the management unit inputs the user's past contact history into the generation AI, which analyzes the contact history and suggests appropriate contacts.

[0036] When searching for contacts, the management unit can filter based on the user's current project and job title. For example, the management unit filters contacts based on the user's current project and job title. The management unit can also use a generation AI to filter contacts based on the user's current project and job title. For example, the management unit inputs the user's project information and job title information into the generation AI, which analyzes the information and filters the contacts. In this way, by filtering contacts based on the user's current project and job title, highly relevant contacts can be displayed. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs the user's project information and job title information into the generation AI, which analyzes the information and filters the contacts.

[0037] When searching for contacts, the management unit can prioritize displaying highly relevant contacts based on the user's geographical location information. The management unit, for example, acquires the user's geographical location information and prioritizes displaying highly relevant contacts. The management unit can also use a generation AI to prioritize displaying contacts based on the user's geographical location information. For example, the management unit inputs the user's geographical location information into the generation AI, which analyzes the information and prioritizes displaying contacts. This makes it possible to prioritize displaying highly relevant contacts by taking the user's geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs the user's geographical location information into the generation AI, which analyzes the information and prioritizes displaying contacts.

[0038] When searching for contacts, the management unit can analyze the user's social media activity and suggest relevant contacts. For example, the management unit stores the user's social media activity in a database and suggests appropriate contacts. The management unit can also analyze the user's social media activity and suggest appropriate contacts using a generation AI. For example, the management unit inputs the user's social media activity into the generation AI, which analyzes the information and suggests appropriate contacts. In this way, relevant contacts can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs the user's social media activity into the generation AI, which analyzes the information and suggests appropriate contacts.

[0039] The transcription unit can adjust the level of detail of the transcription based on the importance of the utterance when transcribing. The transcription unit, for example, adjusts the level of detail of the transcription based on the importance of the utterance. The transcription unit can also adjust the level of detail of the transcription based on the importance of the utterance using a generation AI. For example, the transcription unit inputs utterance importance data to the generation AI, which analyzes the information and adjusts the level of detail of the transcription. In this way, important utterances can be recorded in detail by adjusting the level of detail of the transcription based on the importance of the utterance. Some or all of the above-mentioned processing in the transcription unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs utterance importance data to the generation AI, which analyzes the information and adjusts the level of detail of the transcription.

[0040] The transcription unit can apply different transcription algorithms depending on the category of the utterance when transcribing. For example, the transcription unit applies different transcription algorithms depending on the category of the utterance. The transcription unit can also use a generation AI to apply different transcription algorithms depending on the category of the utterance. For example, the transcription unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate transcription algorithm. This enables more appropriate transcription by applying different transcription algorithms depending on the category of the utterance. Some or all of the above-mentioned processing in the transcription unit may be performed using, or without, the generation AI. For example, the transcription unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate transcription algorithm.

[0041] The transcription unit can determine the priority of transcription based on the time of submission of a statement when transcribing. The transcription unit determines the priority of transcription based on, for example, the time of submission of a statement. The transcription unit can also use a generation AI to determine the priority of transcription based on the time of submission of a statement. For example, the transcription unit inputs data on the time of submission of a statement into the generation AI, which analyzes the information and determines the priority of transcription. In this way, by determining the priority of transcription based on the time of submission of a statement, important statements can be preferentially recorded. Some or all of the above-mentioned processing in the transcription unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs data on the time of submission of a statement into the generation AI, which analyzes the information and determines the priority of transcription.

[0042] The transcription unit can adjust the order of transcription based on the relevance of utterances during transcription. The transcription unit adjusts the order of transcription based on, for example, the relevance of utterances. The transcription unit can also adjust the order of transcription based on the relevance of utterances using a generation AI. For example, the transcription unit inputs utterance relevance data into the generation AI, which analyzes the information and adjusts the order of transcription. In this way, by adjusting the order of transcription based on the relevance of utterances, important utterances can be recorded preferentially. Some or all of the above-described processing in the transcription unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs utterance relevance data into the generation AI, which analyzes the information and adjusts the order of transcription.

[0043] When creating minutes, the minutes-taking unit can apply different minutes-taking algorithms depending on the category of the utterance. For example, the minutes-taking unit applies different minutes-taking algorithms depending on the category of the utterance. The minutes-taking unit can also use a generation AI to apply different minutes-taking algorithms depending on the category of the utterance. For example, the minutes-taking unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate minutes-taking algorithm. In this way, more appropriate minutes can be created by applying different minutes-taking algorithms depending on the category of the utterance. Some or all of the above-mentioned processing in the minutes-taking unit may be performed using, or without, the generation AI. For example, the minutes-taking unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate minutes-taking algorithm.

[0044] When creating minutes, the minutes-taking section can determine the priority of minutes based on the time when the remarks were submitted. The minutes-taking section determines the priority of minutes based on, for example, the time when the remarks were submitted. The minutes-taking section can also use a generation AI to determine the priority of minutes based on the time when the remarks were submitted. For example, the minutes-taking section inputs data on the time when the remarks were submitted into the generation AI, which then analyzes the information to determine the priority of the minutes. In this way, by determining the priority of minutes based on the time when the remarks were submitted, important remarks can be recorded preferentially. Some or all of the above-mentioned processing in the minutes-taking section may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the minutes-taking section inputs data on the time when the remarks were submitted into the generation AI, which then analyzes the information to determine the priority of the minutes.

[0045] The minutes-taking unit can adjust the order of the minutes based on the relevance of utterances when taking minutes. The minutes-taking unit adjusts the order of the minutes based on, for example, the relevance of utterances. The minutes-taking unit can also adjust the order of the minutes based on the relevance of utterances using a generation AI. For example, the minutes-taking unit inputs relevance data of utterances into the generation AI, which then analyzes the information and adjusts the order of the minutes. In this way, by adjusting the order of the minutes based on the relevance of utterances, important utterances can be recorded preferentially. Some or all of the above-mentioned processing in the minutes-taking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the minutes-taking unit inputs relevance data of utterances into the generation AI, which then analyzes the information and adjusts the order of the minutes.

[0046] The schedule management unit can optimize the current schedule by referring to past schedule data when managing the schedule. For example, the schedule management unit stores past schedule data in a database and optimizes the current schedule. The schedule management unit can also use a generation AI to optimize the current schedule by referring to past schedule data. For example, the schedule management unit inputs past schedule data into the generation AI, which analyzes the information and optimizes the current schedule. In this way, the current schedule can be optimized by referring to the past schedule data. Some or all of the above-mentioned processes in the schedule management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the schedule management unit inputs past schedule data into the generation AI, which analyzes the information and optimizes the current schedule.

[0047] The schedule management unit can apply different schedule management methods to each meeting category during schedule management. For example, the schedule management unit applies different schedule management methods to each meeting category. The schedule management unit can also use a generation AI to apply different schedule management methods to each meeting category. For example, the schedule management unit inputs meeting category data into the generation AI, which analyzes the information and applies an appropriate schedule management method. This enables more appropriate schedule management by applying different schedule management methods to each meeting category. Some or all of the above-described processing in the schedule management unit may be performed using, or without, the generation AI. For example, the schedule management unit inputs meeting category data into the generation AI, which analyzes the information and applies an appropriate schedule management method.

[0048] The schedule management unit can analyze schedule changes based on the time when a meeting is submitted during schedule management. The schedule management unit analyzes schedule changes based on, for example, the time when a meeting is submitted. The schedule management unit can also use a generation AI to analyze schedule changes based on the time when a meeting is submitted. For example, the schedule management unit inputs meeting submission time data into the generation AI, which then analyzes the information to analyze schedule changes. This enables more appropriate schedule management by analyzing schedule changes based on the time when a meeting is submitted. Some or all of the above-described processing in the schedule management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the schedule management unit inputs meeting submission time data into the generation AI, which then analyzes the information to analyze schedule changes.

[0049] The schedule management unit can optimize the schedule by referring to market data related to the conference when managing the schedule. The schedule management unit, for example, optimizes the schedule by referring to market data related to the conference. The schedule management unit can also optimize the schedule by referring to market data related to the conference using a generation AI. For example, the schedule management unit inputs relevant market data into the generation AI, which then analyzes the information to optimize the schedule. This enables more appropriate schedule management by referring to market data related to the conference. Some or all of the above-described processing in the schedule management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the schedule management unit inputs relevant market data into the generation AI, which then analyzes the information to optimize the schedule.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The management unit can analyze the user's past behavior history and suggest optimal contacts. For example, by prioritizing the display of people with whom the user has frequently contacted in the past, efficient communication becomes possible. It can also automatically suggest related contacts based on information about meetings and projects the user has participated in in the past. Furthermore, by analyzing the user's past behavioral patterns and prioritizing the display of people with whom the user often contacts during specific times, the timing of communication can be optimized.

[0052] The transcription unit can translate statements made during meetings in real time and display them in multiple languages. For example, statements made in English can be translated into Japanese or Chinese and displayed to participants, enabling smooth communication even in international meetings. The translated text data can also be saved for later reference. Furthermore, to improve translation accuracy, the system can be trained to include technical terms and industry-specific expressions.

[0053] The minutes-taking module can summarize what is said during a meeting and extract only the important points to create minutes. For example, even in a long meeting, efficient information sharing is possible by summarizing only the important comments and decisions and recording them in the minutes. The module can also automatically classify the summarized minutes and link them to related projects and tasks. Furthermore, it can learn from past minutes data to improve the accuracy of summaries.

[0054] The schedule management unit can monitor the user's health condition and suggest appropriate break times. For example, it can support health management by measuring the user's heart rate and stress level and notifying them to take breaks at appropriate times. It can also suggest an optimal schedule for the user based on past health data. It can also automatically adjust meeting schedules according to the user's health condition.

[0055] The management unit can suggest the most appropriate contacts based on the user's geographic location information. For example, if the user is on a business trip, local contacts can be displayed preferentially to enable quick responses. Also, if the user is active in a specific area, contacts related to that area can be automatically suggested. Furthermore, the management unit can provide the user with appropriate advice and reminders based on the geographic location information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The management department centrally manages internal contacts. For example, the management department registers internal contacts in a database and provides appropriate contact information. The management department can also use generative AI to search for internal contacts and provide appropriate contact information. For example, the management department searches for and displays relevant contact information based on keywords entered by the user. Step 2: The transcription unit transcribes what is said during the meeting in real time. For example, the transcription unit uses voice recognition technology to convert what is said during the meeting into text data in real time. The transcription unit can also use a generation AI to transcribe what is said during the meeting in real time. For example, the transcription unit acquires what is said during the meeting as audio data and inputs it into the generation AI, which then analyzes the audio data and generates text data. Step 3: The minutes-taking unit automatically generates minutes based on the generated character data. For example, the minutes-taking unit organizes the generated character data according to a format and creates minutes. The minutes-taking unit can also use a generation AI to automatically generate minutes based on the generated character data. For example, the minutes-taking unit inputs the generated character data into the generation AI, which then automatically generates minutes. Step 4: The schedule management unit manages the schedule based on the minutes. For example, the schedule management unit automatically adjusts the meeting schedule described in the minutes and notifies the participants. The schedule management unit can also use the generation AI to manage the schedule based on the minutes. For example, the schedule management unit inputs the minutes into the generation AI, which then automatically adjusts the schedule and notifies the participants.

[0058] (Example 2) The metaverse system according to an embodiment of the present invention allows users to instantly participate in meetings using a PC or tablet app, without requiring a dedicated device such as VR goggles. This metaverse system improves work efficiency by combining the metaverse (a comfortable space) with the generation AI (convenience). Specifically, the generation AI centrally manages all internal contacts and consultations, and handles transcription, announcements, meeting minutes, and schedule management. It can also be integrated with various chat and schedule management tools. This allows users to instantly participate in the metaverse using a PC or tablet without requiring a dedicated device, allowing them to efficiently advance their work. For example, when a user launches an app on their PC or tablet, the work environment within the metaverse is displayed. The user can use the generation AI to search for internal contacts or input consultations. The generation AI analyzes the input information and provides appropriate contact information and responses. It can also transcribe comments made during meetings in real time and automatically generate meeting minutes. Furthermore, by integrating with a schedule management tool, meeting schedules can be automatically adjusted and notified to participants. In this way, by combining the metaverse and generative AI, the present invention provides a system that improves work efficiency and allows users to work comfortably. As a result, the metaverse system allows users to instantly join the metaverse using a PC or tablet without requiring a dedicated device, allowing them to work efficiently.

[0059] The metaverse system according to the embodiment includes a management unit, a transcription unit, a minutes-taking unit, and a schedule management unit. The management unit centrally manages internal contacts. For example, the management unit registers internal contacts in a database and provides appropriate contacts. The management unit can also use a generation AI to search for internal contacts and provide appropriate contacts. For example, the management unit searches for and displays relevant contacts based on keywords entered by a user. The transcription unit transcribes statements made during meetings in real time. For example, the transcription unit converts statements made during meetings into text data in real time using speech recognition technology. The transcription unit can also transcribe statements made during meetings in real time using a generation AI. For example, the transcription unit acquires statements made during meetings as audio data and inputs it into the generation AI, which then analyzes the audio data to generate text data. The minutes-taking unit automatically generates minutes based on the generated text data. For example, the minutes-taking unit organizes the generated text data according to a format and creates minutes. The minutes-taking unit can also use the generation AI to automatically generate minutes based on the generated text data. For example, the minutes-taking unit inputs the generated text data into the generation AI, which then automatically generates minutes. The schedule management unit manages the schedule based on the minutes. For example, the schedule management unit automatically adjusts the meeting schedule listed in the minutes and notifies the participants. The schedule management unit can also use the generation AI to manage the schedule based on the minutes. For example, the schedule management unit inputs the minutes into the generation AI, which then automatically adjusts the schedule and notifies the participants. As a result, the metaverse system according to the embodiment can efficiently perform centralized management of internal contacts, transcription, minutes-taking, and schedule management.

[0060] The management unit can search for internal contacts and provide the contacts. The management unit, for example, registers internal contacts in a database and provides appropriate contacts. The management unit can also use a generation AI to search for internal contacts and provide appropriate contacts. For example, the management unit searches for and displays relevant contacts based on keywords entered by the user. This makes it possible to efficiently search for internal contacts and provide appropriate contacts. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs keywords entered by the user into the generation AI, which then searches for and displays relevant contacts.

[0061] The transcription unit can transcribe statements made during a meeting in real time. The transcription unit converts statements made during a meeting into text data in real time using, for example, voice recognition technology. The transcription unit can also transcribe statements made during a meeting in real time using a generation AI. For example, the transcription unit acquires statements made during a meeting as audio data and inputs the data into a generation AI, which then analyzes the audio data to generate text data. This enables the rapid creation of meeting minutes by transcribing statements made during a meeting in real time. Some or all of the above-described processing in the transcription unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the transcription unit acquires statements made during a meeting as audio data and inputs the data into a generation AI, which then analyzes the audio data to generate text data.

[0062] The minutes-taking unit can automatically generate minutes based on the generated character data. For example, the minutes-taking unit organizes the generated character data according to a format and creates minutes. The minutes-taking unit can also automatically generate minutes based on the generated character data using a generation AI. For example, the minutes-taking unit inputs the generated character data into the generation AI, which then automatically generates minutes. This improves the efficiency of creating minutes by automatically generating minutes based on the generated character data. Some or all of the above-mentioned processing in the minutes-taking unit may be performed using, or without, the generation AI. For example, the minutes-taking unit inputs the generated character data into the generation AI, which then automatically generates minutes.

[0063] The schedule management unit can automatically adjust the meeting schedule and notify participants. The schedule management unit, for example, automatically adjusts the meeting schedule recorded in the minutes and notifies participants. The schedule management unit can also manage the schedule based on the minutes using a generation AI. For example, the schedule management unit inputs the minutes into a generation AI, which then automatically adjusts the schedule and notifies participants. This automatically adjusts the meeting schedule and notifies participants, thereby streamlining schedule management. Some or all of the above-mentioned processing in the schedule management unit may be performed using, or without, the generation AI. For example, the schedule management unit inputs the minutes into a generation AI, which then automatically adjusts the schedule and notifies participants.

[0064] The management unit can cooperate with multiple chat or schedule management tools. For example, the management unit cooperates with multiple chat tools or schedule management tools to centrally manage information. The management unit can also cooperate with multiple chat tools or schedule management tools using a generation AI. For example, the management unit acquires information using the APIs of various chat tools or schedule management tools, and the generation AI integrates and manages that information. This enables centralized management of information by coordinating with various chat or schedule management tools. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit acquires information using the APIs of various chat tools or schedule management tools, and the generation AI integrates and manages that information.

[0065] The management unit can estimate the user's emotions and adjust the display order of contacts based on the estimated user's emotions. For example, the management unit estimates the user's emotions and adjusts the display order of contacts based on the estimated user's emotions. The management unit can also estimate the user's emotions and adjust the display order of contacts based on the estimated user's emotions using a generation AI. For example, the management unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and adjusts the display order of contacts. This allows the optimal contacts for the user to be displayed by adjusting the display order of contacts based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the management unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and adjusts the display order of contacts.

[0066] The management unit can analyze the user's past contact history and automatically suggest contacts. The management unit, for example, stores the user's past contact history in a database and suggests appropriate contacts. The management unit can also analyze the user's past contact history and suggest appropriate contacts using a generation AI. For example, the management unit inputs the user's past contact history into the generation AI, which analyzes the contact history and suggests appropriate contacts. In this way, optimal contacts can be automatically suggested by analyzing the user's past contact history. Some or all of the above-mentioned processing in the management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the management unit inputs the user's past contact history into the generation AI, which analyzes the contact history and suggests appropriate contacts.

[0067] When searching for contacts, the management unit can filter based on the user's current project and job title. For example, the management unit filters contacts based on the user's current project and job title. The management unit can also use a generation AI to filter contacts based on the user's current project and job title. For example, the management unit inputs the user's project information and job title information into the generation AI, which analyzes the information and filters the contacts. In this way, by filtering contacts based on the user's current project and job title, highly relevant contacts can be displayed. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs the user's project information and job title information into the generation AI, which analyzes the information and filters the contacts.

[0068] The management unit can estimate the user's emotions and determine the priority of contacts based on the estimated user emotions. For example, the management unit can estimate the user's emotions and determine the priority of contacts based on the estimated user emotions. The management unit can also estimate the user's emotions and determine the priority of contacts based on the estimated user emotions using a generation AI. For example, the management unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and determines the priority of contacts. This allows the optimal contacts for the user to be displayed by determining the priority of contacts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the management unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and determines the priority of contacts.

[0069] When searching for contacts, the management unit can prioritize displaying highly relevant contacts based on the user's geographical location information. The management unit, for example, acquires the user's geographical location information and prioritizes displaying highly relevant contacts. The management unit can also use a generation AI to prioritize displaying contacts based on the user's geographical location information. For example, the management unit inputs the user's geographical location information into the generation AI, which analyzes the information and prioritizes displaying contacts. This makes it possible to prioritize displaying highly relevant contacts by taking the user's geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs the user's geographical location information into the generation AI, which analyzes the information and prioritizes displaying contacts.

[0070] When searching for contacts, the management unit can analyze the user's social media activity and suggest relevant contacts. For example, the management unit stores the user's social media activity in a database and suggests appropriate contacts. The management unit can also analyze the user's social media activity and suggest appropriate contacts using a generation AI. For example, the management unit inputs the user's social media activity into the generation AI, which analyzes the information and suggests appropriate contacts. In this way, relevant contacts can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the management unit may be performed using, or without, the generation AI. For example, the management unit inputs the user's social media activity into the generation AI, which analyzes the information and suggests appropriate contacts.

[0071] The transcription unit can estimate the user's emotions and adjust the transcription expression method based on the estimated user's emotions. For example, the transcription unit can estimate the user's emotions and adjust the transcription expression method based on the estimated user's emotions. The transcription unit can also estimate the user's emotions using a generation AI and adjust the transcription expression method based on the estimated user's emotions. For example, the transcription unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and adjusts the transcription expression method. This enables more appropriate transcription by adjusting the transcription expression method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transcription unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs the user's emotional data into the generation AI, which then analyzes the emotional data and adjusts the way the transcription is expressed.

[0072] The transcription unit can adjust the level of detail of the transcription based on the importance of the utterance when transcribing. The transcription unit, for example, adjusts the level of detail of the transcription based on the importance of the utterance. The transcription unit can also adjust the level of detail of the transcription based on the importance of the utterance using a generation AI. For example, the transcription unit inputs utterance importance data to the generation AI, which analyzes the information and adjusts the level of detail of the transcription. In this way, important utterances can be recorded in detail by adjusting the level of detail of the transcription based on the importance of the utterance. Some or all of the above-mentioned processing in the transcription unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs utterance importance data to the generation AI, which analyzes the information and adjusts the level of detail of the transcription.

[0073] The transcription unit can apply different transcription algorithms depending on the category of the utterance when transcribing. For example, the transcription unit applies different transcription algorithms depending on the category of the utterance. The transcription unit can also use a generation AI to apply different transcription algorithms depending on the category of the utterance. For example, the transcription unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate transcription algorithm. This enables more appropriate transcription by applying different transcription algorithms depending on the category of the utterance. Some or all of the above-mentioned processing in the transcription unit may be performed using, or without, the generation AI. For example, the transcription unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate transcription algorithm.

[0074] The transcription unit can estimate the user's emotion and adjust the length of the transcription based on the estimated user's emotion. For example, the transcription unit can estimate the user's emotion and adjust the length of the transcription based on the estimated user's emotion. The transcription unit can also use a generation AI to estimate the user's emotion and adjust the length of the transcription based on the estimated user's emotion. For example, the transcription unit inputs user emotion data into the generation AI, which analyzes the emotion data and adjusts the length of the transcription. This allows for more appropriate transcription by adjusting the length of the transcription based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the transcription unit can be performed, for example, with or without the generation AI. For example, the transcription unit inputs the user's emotional data into the generation AI, which then analyzes the emotional data and adjusts the length of the transcription.

[0075] The transcription unit can determine the priority of transcription based on the time of submission of a statement when transcribing. The transcription unit determines the priority of transcription based on, for example, the time of submission of a statement. The transcription unit can also use a generation AI to determine the priority of transcription based on the time of submission of a statement. For example, the transcription unit inputs data on the time of submission of a statement into the generation AI, which analyzes the information and determines the priority of transcription. In this way, by determining the priority of transcription based on the time of submission of a statement, important statements can be preferentially recorded. Some or all of the above-mentioned processing in the transcription unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs data on the time of submission of a statement into the generation AI, which analyzes the information and determines the priority of transcription.

[0076] The transcription unit can adjust the order of transcription based on the relevance of utterances during transcription. The transcription unit adjusts the order of transcription based on, for example, the relevance of utterances. The transcription unit can also adjust the order of transcription based on the relevance of utterances using a generation AI. For example, the transcription unit inputs utterance relevance data into the generation AI, which analyzes the information and adjusts the order of transcription. In this way, by adjusting the order of transcription based on the relevance of utterances, important utterances can be recorded preferentially. Some or all of the above-described processing in the transcription unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the transcription unit inputs utterance relevance data into the generation AI, which analyzes the information and adjusts the order of transcription.

[0077] The minutes-taking unit can estimate the user's emotions and adjust the presentation style of the minutes based on the estimated user's emotions. For example, the minutes-taking unit can estimate the user's emotions and adjust the presentation style of the minutes based on the estimated user's emotions. The minutes-taking unit can also estimate the user's emotions using a generation AI and adjust the presentation style of the minutes based on the estimated user's emotions. For example, the minutes-taking unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and adjusts the presentation style of the minutes. This allows more appropriate minutes to be created by adjusting the presentation style of the minutes based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the minutes-taking unit can be performed using, for example, the generation AI, or without the generation AI. For example, the minutes creation department inputs the user's emotional data into the generation AI, which then analyzes the emotional data and adjusts the way the minutes are expressed.

[0078] When creating minutes, the minutes-taking unit can apply different minutes-taking algorithms depending on the category of the utterance. For example, the minutes-taking unit applies different minutes-taking algorithms depending on the category of the utterance. The minutes-taking unit can also use a generation AI to apply different minutes-taking algorithms depending on the category of the utterance. For example, the minutes-taking unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate minutes-taking algorithm. In this way, more appropriate minutes can be created by applying different minutes-taking algorithms depending on the category of the utterance. Some or all of the above-mentioned processing in the minutes-taking unit may be performed using, or without, the generation AI. For example, the minutes-taking unit inputs utterance category data to the generation AI, which analyzes the information and applies an appropriate minutes-taking algorithm.

[0079] The minutes creation unit can estimate the user's emotions and adjust the length of the minutes based on the estimated user's emotions. For example, the minutes creation unit can estimate the user's emotions and adjust the length of the minutes based on the estimated user's emotions. The minutes creation unit can also use a generation AI to estimate the user's emotions and adjust the length of the minutes based on the estimated user's emotions. For example, the minutes creation unit inputs the user's emotion data into the generation AI, which analyzes the emotion data and adjusts the length of the minutes. This allows more appropriate minutes to be created by adjusting the length of the minutes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the minutes creation unit can be performed, for example, using the generation AI, or without using the generation AI. For example, the minutes creation unit inputs the user's emotional data into the generation AI, which then analyzes the emotional data and adjusts the length of the minutes.

[0080] When creating minutes, the minutes-taking section can determine the priority of minutes based on the time when the remarks were submitted. The minutes-taking section determines the priority of minutes based on, for example, the time when the remarks were submitted. The minutes-taking section can also use a generation AI to determine the priority of minutes based on the time when the remarks were submitted. For example, the minutes-taking section inputs data on the time when the remarks were submitted into the generation AI, which then analyzes the information to determine the priority of the minutes. In this way, by determining the priority of minutes based on the time when the remarks were submitted, important remarks can be recorded preferentially. Some or all of the above-mentioned processing in the minutes-taking section may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the minutes-taking section inputs data on the time when the remarks were submitted into the generation AI, which then analyzes the information to determine the priority of the minutes.

[0081] The minutes-taking unit can adjust the order of the minutes based on the relevance of utterances when taking minutes. The minutes-taking unit adjusts the order of the minutes based on, for example, the relevance of utterances. The minutes-taking unit can also adjust the order of the minutes based on the relevance of utterances using a generation AI. For example, the minutes-taking unit inputs relevance data of utterances into the generation AI, which then analyzes the information and adjusts the order of the minutes. In this way, by adjusting the order of the minutes based on the relevance of utterances, important utterances can be recorded preferentially. Some or all of the above-mentioned processing in the minutes-taking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the minutes-taking unit inputs relevance data of utterances into the generation AI, which then analyzes the information and adjusts the order of the minutes.

[0082] The schedule management unit can estimate a user's emotions and adjust the schedule display method based on the estimated user's emotions. For example, the schedule management unit estimates a user's emotions and adjusts the schedule display method based on the estimated user's emotions. The schedule management unit can also estimate a user's emotions using a generation AI and adjust the schedule display method based on the estimated user's emotions. For example, the schedule management unit inputs user's emotion data into the generation AI, which analyzes the emotion data and adjusts the schedule display method. This enables more appropriate schedule management by adjusting the schedule display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the schedule management unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the schedule management unit inputs user's emotion data into the generation AI, which analyzes the emotion data and adjusts the schedule display method.

[0083] The schedule management unit can optimize the current schedule by referring to past schedule data when managing the schedule. For example, the schedule management unit stores past schedule data in a database and optimizes the current schedule. The schedule management unit can also use a generation AI to optimize the current schedule by referring to past schedule data. For example, the schedule management unit inputs past schedule data into the generation AI, which analyzes the information and optimizes the current schedule. In this way, the current schedule can be optimized by referring to the past schedule data. Some or all of the above-mentioned processes in the schedule management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the schedule management unit inputs past schedule data into the generation AI, which analyzes the information and optimizes the current schedule.

[0084] The schedule management unit can apply different schedule management methods to each meeting category during schedule management. For example, the schedule management unit applies different schedule management methods to each meeting category. The schedule management unit can also use a generation AI to apply different schedule management methods to each meeting category. For example, the schedule management unit inputs meeting category data into the generation AI, which analyzes the information and applies an appropriate schedule management method. This enables more appropriate schedule management by applying different schedule management methods to each meeting category. Some or all of the above-described processing in the schedule management unit may be performed using, or without, the generation AI. For example, the schedule management unit inputs meeting category data into the generation AI, which analyzes the information and applies an appropriate schedule management method.

[0085] The schedule management unit can estimate a user's emotions and determine schedule priorities based on the estimated user emotions. The schedule management unit, for example, estimates a user's emotions and determines schedule priorities based on the estimated user emotions. The schedule management unit can also estimate a user's emotions using a generation AI and determine schedule priorities based on the estimated user emotions. For example, the schedule management unit inputs user emotion data into the generation AI, which analyzes the emotion data and determines schedule priorities. This enables more appropriate schedule management by determining schedule priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the schedule management unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the schedule management unit inputs user emotion data into the generation AI, which analyzes the emotion data and determines schedule priorities.

[0086] The schedule management unit can analyze schedule changes based on the time when a meeting is submitted during schedule management. The schedule management unit analyzes schedule changes based on, for example, the time when a meeting is submitted. The schedule management unit can also use a generation AI to analyze schedule changes based on the time when a meeting is submitted. For example, the schedule management unit inputs meeting submission time data into the generation AI, which then analyzes the information to analyze schedule changes. This enables more appropriate schedule management by analyzing schedule changes based on the time when a meeting is submitted. Some or all of the above-described processing in the schedule management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the schedule management unit inputs meeting submission time data into the generation AI, which then analyzes the information to analyze schedule changes.

[0087] The schedule management unit can optimize the schedule by referring to market data related to the conference when managing the schedule. The schedule management unit, for example, optimizes the schedule by referring to market data related to the conference. The schedule management unit can also optimize the schedule by referring to market data related to the conference using a generation AI. For example, the schedule management unit inputs relevant market data into the generation AI, which then analyzes the information to optimize the schedule. This enables more appropriate schedule management by referring to market data related to the conference. Some or all of the above-described processing in the schedule management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the schedule management unit inputs relevant market data into the generation AI, which then analyzes the information to optimize the schedule. === Hard Collateral 1-1 === Each of the multiple elements, including the management unit, transcription unit, minutes-creation unit, and schedule management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, and registers internal contact information in the database 24 and provides appropriate contact information. The transcription unit uses the microphone 38B of the smart device 14 to capture speech during a meeting as audio data and converts it into text data in real time by the specific processing unit 290 of the data processing device 12. The minutes-creation unit organizes the generated text data according to a format by the specific processing unit 290 of the data processing device 12 and automatically generates minutes. The schedule management unit manages the schedule by the specific processing unit 290 of the data processing device 12 based on the minutes and notifies participants. === Hard Collateral 1-2 === Each of the multiple elements, including the management unit, transcription unit, minutes creation unit, and schedule management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, and registers internal contact information in the database 24 and provides appropriate contact information. The transcription unit uses the microphone 238 of the smart glasses 214 to acquire speech during a meeting as audio data, which is then converted into text data in real time by the specific processing unit 290 of the data processing device 12. The minutes creation unit organizes the generated text data according to a format by the specific processing unit 290 of the data processing device 12, and automatically generates minutes. The schedule management unit manages the schedule by the specific processing unit 290 of the data processing device 12 based on the minutes, and notifies participants. === Hard Collateral 1-3 === Each of the multiple elements, including the management unit, transcription unit, minutes-creation unit, and schedule management unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, and registers internal contact information in the database 24 and provides appropriate contact information. The transcription unit uses the microphone 238 of the headset terminal 314 to acquire speech during a meeting as audio data, and converts it into text data in real time by the specific processing unit 290 of the data processing device 12. The minutes-creation unit organizes the generated text data according to a format by the specific processing unit 290 of the data processing device 12, and automatically generates minutes. The schedule management unit manages the schedule by the specific processing unit 290 of the data processing device 12 based on the minutes, and notifies participants. === Hard Collateral 1-4 === Each of the multiple elements, including the management unit, transcription unit, minutes-creation unit, and schedule management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit is realized by the specific processing unit 290 of the data processing device 12, and registers internal contact information in the database 24 and provides appropriate contact information. The transcription unit uses the microphone 238 of the robot 414 to acquire speech during a meeting as audio data, and converts it into text data in real time by the specific processing unit 290 of the data processing device 12. The minutes-creation unit organizes the generated text data according to a format by the specific processing unit 290 of the data processing device 12, and automatically generates minutes. The schedule management unit manages the schedule by the specific processing unit 290 of the data processing device 12 based on the minutes, and notifies participants.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The management unit can analyze the user's past behavior history and suggest optimal contacts. For example, by prioritizing the display of people with whom the user has frequently contacted in the past, efficient communication becomes possible. It can also automatically suggest related contacts based on information about meetings and projects the user has participated in in the past. Furthermore, by analyzing the user's past behavioral patterns and prioritizing the display of people with whom the user often contacts during specific times, the timing of communication can be optimized.

[0090] The transcription unit can translate statements made during meetings in real time and display them in multiple languages. For example, statements made in English can be translated into Japanese or Chinese and displayed to participants, enabling smooth communication even in international meetings. The translated text data can also be saved for later reference. Furthermore, to improve translation accuracy, the system can be trained to include technical terms and industry-specific expressions.

[0091] The minutes-taking module can summarize what is said during a meeting and extract only the important points to create minutes. For example, even in a long meeting, efficient information sharing is possible by summarizing only the important comments and decisions and recording them in the minutes. The module can also automatically classify the summarized minutes and link them to related projects and tasks. Furthermore, it can learn from past minutes data to improve the accuracy of summaries.

[0092] The schedule management unit can monitor the user's health condition and suggest appropriate break times. For example, it can support health management by measuring the user's heart rate and stress level and notifying them to take breaks at appropriate times. It can also suggest an optimal schedule for the user based on past health data. It can also automatically adjust meeting schedules according to the user's health condition.

[0093] The management unit can estimate the user's emotions and adjust the display order of contacts based on the estimated emotions. For example, if the user is feeling stressed, contacts needing support can be displayed first, enabling a quick response. Also, if the user is feeling positive, important contacts can be displayed first, supporting efficient work performance. Furthermore, appropriate advice and reminders can be provided to the user based on the emotion data.

[0094] The transcription unit can estimate the user's emotions and adjust the transcription style based on the estimated emotions. For example, if the user is nervous, the transcription can be more carefully transcribed to provide accurate information. On the other hand, if the user is relaxed, the transcription can be concisely summarized to support efficient information sharing. Furthermore, the transcription tone and style can be adjusted based on the emotion data.

[0095] The minutes creation unit can estimate the user's emotions and adjust the presentation of the minutes based on the estimated emotions. For example, if the user is feeling stressed, the minutes can be written more concisely to support understanding of the information. Alternatively, if the user is feeling positive, detailed minutes can be created to promote information sharing. Furthermore, the format and layout of the minutes can be adjusted based on the emotion data.

[0096] The schedule management unit can estimate the user's emotions and adjust the way the schedule is displayed based on the estimated emotions. For example, if the user is feeling stressed, the schedule can be displayed simply to reduce the burden. Alternatively, if the user is relaxed, a detailed schedule can be displayed to support efficient work performance. Furthermore, schedule reminders and notification methods can be adjusted based on the emotion data.

[0097] The schedule management unit can estimate the user's emotions and determine schedule priorities based on the estimated emotions. For example, if the user is feeling stressed, it can reduce the burden by postponing less important tasks. Also, if the user is feeling positive, it can prioritize important tasks in the schedule to support efficient work execution. Furthermore, it can also adjust the schedule and provide reminders based on the emotion data.

[0098] The management unit can suggest the most appropriate contacts based on the user's geographic location information. For example, if the user is on a business trip, local contacts can be displayed preferentially to enable quick responses. Also, if the user is active in a specific area, contacts related to that area can be automatically suggested. Furthermore, the management unit can provide the user with appropriate advice and reminders based on the geographic location information.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The management department centrally manages internal contacts. For example, the management department registers internal contacts in a database and provides appropriate contact information. The management department can also use generative AI to search for internal contacts and provide appropriate contact information. For example, the management department searches for and displays relevant contact information based on keywords entered by the user. Step 2: The transcription unit transcribes what is said during the meeting in real time. For example, the transcription unit uses voice recognition technology to convert what is said during the meeting into text data in real time. The transcription unit can also use a generation AI to transcribe what is said during the meeting in real time. For example, the transcription unit acquires what is said during the meeting as audio data and inputs it into the generation AI, which then analyzes the audio data and generates text data. Step 3: The minutes-taking unit automatically generates minutes based on the generated character data. For example, the minutes-taking unit organizes the generated character data according to a format and creates minutes. The minutes-taking unit can also use a generation AI to automatically generate minutes based on the generated character data. For example, the minutes-taking unit inputs the generated character data into the generation AI, which then automatically generates minutes. Step 4: The schedule management unit manages the schedule based on the minutes. For example, the schedule management unit automatically adjusts the meeting schedule described in the minutes and notifies the participants. The schedule management unit can also use the generation AI to manage the schedule based on the minutes. For example, the schedule management unit inputs the minutes into the generation AI, which then automatically adjusts the schedule and notifies the participants.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Management Department, which manages all internal contact information, a transcription unit that transcribes based on the contact information managed by the management unit; a minutes creation unit that creates minutes based on the character data generated by the transcription unit; a schedule management unit that manages schedules based on the minutes created by the minutes creation unit; Equipped with A system characterized by:

2. The management unit Find and provide contact information within your company 2. The system of claim 1.

3. The transcription unit Transcribe what's said in meetings in real time 2. The system of claim 1.

4. The minutes preparation department Automatically generate minutes based on the generated text data 2. The system of claim 1.

5. The schedule management unit Automatically schedule meetings and notify participants 2. The system of claim 1.

6. The management unit Integrate with multiple chat or scheduling tools 2. The system of claim 1.

7. The management unit Inferring user sentiment and adjusting the display order of contacts based on the estimated user sentiment 2. The system of claim 1.

8. The management unit Analyzes the user's past contact history and automatically suggests contacts 2. The system of claim 1.

9. The management unit When searching for contacts, filter based on the user's current project or job title 2. The system of claim 1.

Images