system

Abstract

The system according to this embodiment aims to analyze the content of speech during online meetings and propose appropriate revisions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects the content of speech during an online meeting. The analysis unit analyzes the content of speech collected by the collection unit. The proposal unit proposes revised versions based on the results of the analysis performed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to determine whether the content of a speech during an online meeting is appropriate, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the content of a speech during an online meeting and propose an appropriate amendment.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the content of a speech during an online meeting. The analysis unit analyzes the content of the speech collected by the collection unit. The proposal unit proposes an amendment based on the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze the content of speech during an online meeting and suggest appropriate revisions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An online meeting support system according to an embodiment of the present invention is a system that determines whether the content of a statement made during an online meeting is appropriate and proposes revisions as necessary. The online meeting support system collects the content of statements made during the online meeting, the content of chats, and the reactions of participants, and determines whether the content of the statement is appropriate. If the content of the statement is determined to be inappropriate, the online meeting support system proposes revisions to the speaker. The speaker then explains again based on the revisions proposed by the online meeting support system. As a result, the content of the statement is appropriately revised, and the intention is accurately conveyed to other participants. First, the online meeting support system collects the content of statements made during the online meeting. For example, if a speaker says, "I have a problem with my task," that content is collected. Next, the content of chats is also collected. For example, if another participant asks in the chat, "What kind of problem is it specifically?", that content is also collected. Furthermore, the reactions of participants are also collected. For example, if a participant shows a confused expression through the camera, that reaction is also collected. The collected information is analyzed by a generative AI. The generative AI determines whether the content of the statement is appropriate. For example, this includes cases where the content of the statement is unclear or where the speaking speed is too fast. If the AI ​​determines that a statement is inappropriate, it will suggest revisions to the speaker. For example, it might suggest revisions such as "Please explain in more detail" or "Please slow down your speaking speed." The speaker then explains again based on the AI's revisions. This ensures that the statement is appropriately revised and that the speaker's intentions are accurately conveyed to other participants. For example, by explaining again, "Specifically, the task is behind schedule, so we may not meet the deadline," the speaker can convey the details of the problem to other participants. This mechanism allows for real-time assessment of whether statements made during online meetings are appropriate and suggests revisions as needed. This improves the quality of online meetings and enables smoother communication for all participants. In this way, the online meeting support system can appropriately revise statements made during online meetings and ensure that the speaker's intentions are accurately conveyed to other participants.

[0029] The online meeting support system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects the content of speech during the online meeting. For example, if a speaker says, "I've encountered a problem with my task," the collection unit collects that content. The collection unit also collects the content of chats. For example, if another participant asks in the chat, "What exactly is the problem?", the collection unit can collect that content. Furthermore, the collection unit also collects the reactions of participants. For example, if a participant shows a confused expression through the camera, the collection unit can collect that reaction. The analysis unit analyzes the content of speech collected by the collection unit. The analysis unit uses a generation AI to determine whether the content of the speech is appropriate or not. For example, this includes cases where the content of the speech is unclear or the speaker is speaking too fast. If the analysis unit determines that the content of the speech is inappropriate using the generation AI, it proposes a revised version to the speaker. The proposal unit proposes a revised version based on the results of the analysis by the analysis unit. For example, the proposal unit suggests revisions to the speaker such as, "Please explain in more detail" or "Please slow down your speaking speed." As a result, the online meeting support system according to this embodiment can appropriately correct the content of speech during an online meeting, ensuring that the intended meaning is accurately conveyed to other participants.

[0030] The data collection unit collects the content of speech during online meetings. Specifically, it uses speech recognition technology to convert the speaker's voice into text data. This speech recognition technology learns the characteristics of the speaker's voice and can recognize speech with high accuracy even in noisy environments. For example, if a speaker says, "I've encountered a problem with my task," the audio data is converted into text data in real time and stored in the data collection unit. The data collection unit also collects the content of chats. Since the chat content is collected directly as text data, it can be used for analysis in the same way as the audio data. For example, if another participant asks in the chat, "What exactly is the problem?", that content is also collected. Furthermore, the data collection unit also collects participants' reactions. It uses facial recognition technology to analyze participants' facial expressions through the camera and detect expressions of confusion, interest, etc. For example, if a participant shows a confused expression, that reaction is recorded by the data collection unit. In this way, the data collection unit can gather information from three data sources—audio, text, and video—and understand the overall situation of the online meeting. The collected data is stored in a central database, making it accessible to the analysis and proposal departments. This allows the data collection department to efficiently gather diverse information during online meetings, improving the overall system performance.

[0031] The analysis unit analyzes the content of the statements collected by the collection unit. The analysis unit uses a generative AI to determine whether the content of the statements is appropriate or not. Specifically, the generative AI uses natural language processing technology to analyze the meaning of the statements and understand the context. For example, this applies when the content of the statement is unclear or when the speaking speed is too fast. If the generative AI determines that the content of the statement is inappropriate, it identifies the reason and generates a revised version. For example, if the statement is abstract and lacks specificity, the generative AI will generate a revised version such as "Please explain in more detail." Also, if the speaking speed is too fast, the generative AI will generate a revised version such as "Please slow down your speaking speed." Furthermore, the analysis unit also analyzes the participants' reactions. Using facial recognition technology, it analyzes the participants' facial expressions and detects expressions of confusion, interest, etc. For example, if a participant shows a confused expression, the analysis unit identifies the reason and determines that the content of the statement may be unclear. In this way, the analysis unit can quickly and accurately analyze the collected data and determine the appropriateness of the content of the statements. Furthermore, the analysis unit can utilize past data and statistical information to analyze trends and patterns in the content of speech. This allows the analysis unit to not only grasp the situation in real time but also to respond to long-term improvements in the content of speech, thereby improving the reliability and effectiveness of the entire system.

[0032] The proposal department proposes revisions based on the results analyzed by the analysis department. Specifically, the proposal department presents the speaker with revisions generated by the generation AI. For example, it might suggest revisions such as "Please explain in more detail" or "Please slow down your speaking speed." The proposal department provides real-time feedback to the speaker to help them appropriately revise their speech. The proposal department checks whether the speaker accepts the revisions and can also propose additional revisions if necessary. For example, if the speaker fails to provide a specific answer to the question "What exactly is the problem?", the proposal department might suggest an additional revision such as "Please explain with a specific example." The proposal department also monitors the speaker's response to ensure that the revisions are implemented appropriately. For example, it checks whether the speaker was able to slow down their speaking speed and proposes further revisions if necessary. This allows the proposal department to continuously improve the appropriateness of the speech and ensure that the intent is accurately conveyed to other participants. Furthermore, the proposal department can collect feedback from the speaker and continuously improve the accuracy and effectiveness of the revisions. This allows the proposal team to appropriately revise what they say during online meetings, ensuring that their intentions are accurately conveyed to other participants.

[0033] The collection unit can collect chat content and participant responses. For example, the collection unit can collect chat content. For instance, if another participant asks "What exactly is the problem?" in the chat, the collection unit can collect that content. The collection unit can also collect participant responses. For example, if a participant shows a confused expression through the camera, that response can be collected. By collecting chat content and participant responses, the appropriateness of the statements can be judged more accurately. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input chat content into AI, which can then analyze and collect the chat content.

[0034] The analysis unit can analyze the collected chat content and participants' responses. For example, the analysis unit can analyze the collected chat content. For instance, if another participant asks "What exactly is the problem?" in the chat, the analysis unit can analyze that question. The analysis unit can also analyze the collected participants' responses. For example, if a participant shows a confused expression through the camera, the analysis unit can analyze that response. By analyzing the collected chat content and participants' responses, the appropriateness of the statements can be judged more accurately. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the collected chat content and participants' responses into the generative AI, which then analyzes them to determine the appropriateness of the statements.

[0035] The suggestion department can propose revisions to a speaker if it determines that the content of their statement is inappropriate. Examples of situations where the suggestion department might address a speaker include when the statement is unclear or when the speaker is speaking too quickly. The suggestion department can propose revisions such as, "Please explain in more detail," or "Please slow down your speaking speed." By proposing revisions when the content of a statement is deemed inappropriate, the appropriateness of the statement is improved. Some or all of the above processing in the suggestion department is performed using a generation AI. For example, if the suggestion department determines that the content of a statement is inappropriate, it can input that information into the generation AI, which can then generate revisions and propose them to the speaker.

[0036] The proposal unit can notify the speaker of the proposed revisions. The proposal unit can notify the speaker of the proposed revisions by methods such as pop-up notifications, email notifications, or chat notifications. This improves the appropriateness of the content of the statement by notifying the speaker of the proposed revisions. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit can select a method for notifying the speaker of the proposed revisions generated by the generation AI and then send the notification.

[0037] The data collection unit can analyze the user's past speech history and select the optimal collection method when collecting speech content. For example, the data collection unit can prioritize the collection of phrases and keywords that the user has frequently used in the past. The data collection unit can also analyze the user's past speech patterns to ensure that important speeches are not missed. Furthermore, the data collection unit can adjust the collection method by referring to the tone and speed of the user's past speeches. This allows the optimal collection method to be selected by analyzing the user's past speech history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past speech history into AI, which can then select the optimal collection method.

[0038] The collection unit can filter the collected utterances based on the user's current projects and areas of interest. For example, the collection unit can prioritize collecting utterances related to projects the user is currently working on. It can also filter and collect utterances containing keywords related to the user's areas of interest. Furthermore, the collection unit can collect utterances related to topics the user has previously shown interest in. This allows for the collection of highly relevant utterances by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input user project information and areas of interest data into an AI, which can then perform the filtering.

[0039] The data collection unit can prioritize collecting relevant statements based on the user's geographical location information when collecting statements. For example, if the user is in a specific region, the data collection unit can prioritize collecting statements related to that region. Furthermore, if the user is on the move, the data collection unit can prioritize collecting statements related to their destination. Additionally, if the user is participating in a specific event, the data collection unit can prioritize collecting statements related to that event. This allows for the collection of more relevant information by prioritizing the collection of relevant statements based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into an AI, which can then prioritize the collection of relevant statements.

[0040] The data collection unit can analyze the user's social media activity and collect relevant statements when collecting the content of their posts. For example, the data collection unit can prioritize collecting posts that contain keywords frequently used by the user on social media. The data collection unit can also analyze the user's social media activity patterns and collect relevant statements. Furthermore, the data collection unit can collect posts related to topics the user has shown interest in on social media. In this way, relevant statements can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI, which can then collect relevant statements.

[0041] The analysis unit can adjust the level of detail of its analysis based on the importance of the statements it analyzes. For example, it can perform a detailed analysis on important statements and a simplified analysis on less important statements. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the statements. This allows for a detailed analysis of important statements by adjusting the level of detail based on their importance. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs importance data of the statements into the generating AI, which can then adjust the level of detail of the analysis based on their importance.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the utterance during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical utterances. It can also apply a business-oriented analysis algorithm to business-related utterances. Furthermore, it can apply a general analysis algorithm to casual utterances. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the utterance. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs utterance category data into the generation AI, which then applies an appropriate analysis algorithm according to the category.

[0043] The analysis unit can determine the priority of analysis based on the submission date of the statements during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent statements. Furthermore, the analysis unit can determine the priority of analysis for past statements according to their importance. In addition, the analysis unit can adjust the analysis schedule based on the submission date of the statements. This allows for the prioritization of the most recent statements by determining the analysis priority based on the submission date. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the submission date data of the statements into the generation AI, which can then determine the analysis priority based on the submission date.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the statements during analysis. For example, the analysis unit can prioritize the analysis of statements with high relevance. It can also postpone the analysis of statements with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the statements. This allows for the prioritization of highly relevant statements by adjusting the order of analysis based on the relevance of the statements. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs relevance data of the statements into the generation AI, which can then adjust the order of analysis based on the relevance.

[0045] The proposal unit can adjust the level of detail in proposed revisions based on the importance of the statements made. For example, the proposal unit can propose detailed revisions for important statements. Conversely, it can propose simplified revisions for less important statements. Furthermore, the proposal unit can determine the priority of revisions according to the importance of the statements. This allows for the proposal of detailed revisions for important statements by adjusting the level of detail based on the importance of the statements. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs importance data of the statements into the generation AI, which can then adjust the level of detail in the revisions based on their importance.

[0046] The proposal unit can apply different revision algorithms depending on the category of the comment content when proposing revisions. For example, the proposal unit can apply a specialized revision algorithm to technical comments. It can also apply a business-oriented revision algorithm to business-related comments. Furthermore, it can apply a general revision algorithm to casual comments. By applying different revision algorithms depending on the category of the comment content, it can propose more appropriate revisions. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs the category data of the comment content into the generation AI, and the generation AI can apply an appropriate revision algorithm according to the category.

[0047] The proposal department can determine the priority of proposed revisions based on the submission timing of the statements made. For example, the proposal department can prioritize proposals for the most recent statements. Furthermore, for past statements, the proposal department can determine the priority of revisions based on their importance. In addition, the proposal department can adjust the schedule of revisions based on the submission timing of the statements. This allows the proposal department to prioritize proposals for the most recent statements by determining the priority of revisions based on the submission timing. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department inputs the submission timing data of the statements into the generation AI, which can then determine the priority of revisions based on the submission timing.

[0048] The proposal unit can adjust the order of proposed revisions based on the relevance of the statements made. For example, the proposal unit can prioritize proposing revisions to highly relevant statements. It can also postpone proposing revisions to less relevant statements. Furthermore, the proposal unit can adjust the order of the proposed revisions based on the relevance of the statements. This allows the proposal unit to prioritize proposing revisions to highly relevant statements by adjusting the order of revisions based on the relevance of the statements. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs relevance data of the statements into the generation AI, which can then adjust the order of the proposed revisions based on their relevance.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] Online meeting support systems can also be equipped with a translation function. This function can translate spoken content in real time to facilitate communication between participants who speak different languages. For example, it can translate the content of an English-speaking speaker into Japanese and provide it to Japanese-speaking participants. Furthermore, the translation function can include a dictionary function to appropriately translate specialized terminology and industry-specific language. Additionally, the translation function can include a contextual analysis function to understand the context of spoken content and provide appropriate translations. This facilitates smoother communication between participants who speak different languages ​​and improves the quality of online meetings.

[0051] Online meeting support systems can also include a scheduling unit. This unit can monitor the meeting's progress in real time and adjust it to ensure all agenda items are covered within the scheduled time. For example, it can manage the time allocation for each agenda item and suggest time adjustments to speakers if the meeting is likely to exceed the allotted time. The scheduling unit can also notify participants when it's time to move on to the next agenda item, depending on the meeting's progress. Furthermore, it can provide a report after the meeting regarding the time allocation and progress of each agenda item. This ensures smoother meeting progress and more efficient time management.

[0052] Online meeting support systems can also include a reminder function. This function can send reminders to participants before and after meetings. For example, it can send reminders about the meeting start time and agenda. It can also send reminders during the meeting about important points and next actions. Furthermore, it can send follow-up reminders after the meeting to ensure participants don't forget the next steps. This allows participants to remember the meeting content and smoothly transition to their next actions.

[0053] Online meeting support systems can also include an archiving section. This section can provide functionality for saving meeting recordings and minutes for later reference. For example, meeting recordings can be saved to the cloud for participants to view later. The archiving section can also automatically generate and provide meeting minutes to participants. Furthermore, the archiving section can highlight key points and action items from the meeting, making them easily accessible to participants. This allows for a review of the meeting content later, ensuring that important information is not missed.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The data collection unit collects the content of what is said during the online meeting. For example, if a speaker says, "I've encountered a problem with my task," the unit will collect that statement. The data collection unit also collects the content of the chat. For example, if another participant asks in the chat, "What exactly is the problem?", the unit can collect that statement. Furthermore, the data collection unit also collects participants' reactions. For example, if a participant shows a confused expression on camera, the unit can collect that reaction. Step 2: The analysis unit analyzes the utterances collected by the collection unit. The analysis unit uses a generation AI to determine whether the utterances are appropriate or inappropriate. For example, this includes cases where the utterances are unclear or the speaking speed is too fast. If the analysis unit determines that the utterances are inappropriate, it proposes revisions to the speaker. Step 3: The proposal team proposes revisions based on the results analyzed by the analysis team. For example, the proposal team might suggest revisions to the speaker such as, "Please explain in more detail," or "Please slow down your speaking speed."

[0056] (Example of form 2) An online meeting support system according to an embodiment of the present invention is a system that determines whether the content of a statement made during an online meeting is appropriate and proposes revisions as necessary. The online meeting support system collects the content of statements made during the online meeting, the content of chats, and the reactions of participants, and determines whether the content of the statement is appropriate. If the content of the statement is determined to be inappropriate, the online meeting support system proposes revisions to the speaker. The speaker then explains again based on the revisions proposed by the online meeting support system. As a result, the content of the statement is appropriately revised, and the intention is accurately conveyed to other participants. First, the online meeting support system collects the content of statements made during the online meeting. For example, if a speaker says, "I have a problem with my task," that content is collected. Next, the content of chats is also collected. For example, if another participant asks in the chat, "What kind of problem is it specifically?", that content is also collected. Furthermore, the reactions of participants are also collected. For example, if a participant shows a confused expression through the camera, that reaction is also collected. The collected information is analyzed by a generative AI. The generative AI determines whether the content of the statement is appropriate. For example, this includes cases where the content of the statement is unclear or where the speaking speed is too fast. If the AI ​​determines that a statement is inappropriate, it will suggest revisions to the speaker. For example, it might suggest revisions such as "Please explain in more detail" or "Please slow down your speaking speed." The speaker then explains again based on the AI's revisions. This ensures that the statement is appropriately revised and that the speaker's intentions are accurately conveyed to other participants. For example, by explaining again, "Specifically, the task is behind schedule, so we may not meet the deadline," the speaker can convey the details of the problem to other participants. This mechanism allows for real-time assessment of whether statements made during online meetings are appropriate and suggests revisions as needed. This improves the quality of online meetings and enables smoother communication for all participants. In this way, the online meeting support system can appropriately revise statements made during online meetings and ensure that the speaker's intentions are accurately conveyed to other participants.

[0057] The online meeting support system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects the content of speech during the online meeting. For example, if a speaker says, "I've encountered a problem with my task," the collection unit collects that content. The collection unit also collects the content of chats. For example, if another participant asks in the chat, "What exactly is the problem?", the collection unit can collect that content. Furthermore, the collection unit also collects the reactions of participants. For example, if a participant shows a confused expression through the camera, the collection unit can collect that reaction. The analysis unit analyzes the content of speech collected by the collection unit. The analysis unit uses a generation AI to determine whether the content of the speech is appropriate or not. For example, this includes cases where the content of the speech is unclear or the speaker is speaking too fast. If the analysis unit determines that the content of the speech is inappropriate using the generation AI, it proposes a revised version to the speaker. The proposal unit proposes a revised version based on the results of the analysis by the analysis unit. For example, the proposal unit suggests revisions to the speaker such as, "Please explain in more detail" or "Please slow down your speaking speed." As a result, the online meeting support system according to this embodiment can appropriately correct the content of speech during an online meeting, ensuring that the intended meaning is accurately conveyed to other participants.

[0058] The data collection unit collects the content of speech during online meetings. Specifically, it uses speech recognition technology to convert the speaker's voice into text data. This speech recognition technology learns the characteristics of the speaker's voice and can recognize speech with high accuracy even in noisy environments. For example, if a speaker says, "I've encountered a problem with my task," the audio data is converted into text data in real time and stored in the data collection unit. The data collection unit also collects the content of chats. Since the chat content is collected directly as text data, it can be used for analysis in the same way as the audio data. For example, if another participant asks in the chat, "What exactly is the problem?", that content is also collected. Furthermore, the data collection unit also collects participants' reactions. It uses facial recognition technology to analyze participants' facial expressions through the camera and detect expressions of confusion, interest, etc. For example, if a participant shows a confused expression, that reaction is recorded by the data collection unit. In this way, the data collection unit can gather information from three data sources—audio, text, and video—and understand the overall situation of the online meeting. The collected data is stored in a central database, making it accessible to the analysis and proposal departments. This allows the data collection department to efficiently gather diverse information during online meetings, improving the overall system performance.

[0059] The analysis unit analyzes the content of the statements collected by the collection unit. The analysis unit uses a generative AI to determine whether the content of the statements is appropriate or not. Specifically, the generative AI uses natural language processing technology to analyze the meaning of the statements and understand the context. For example, this applies when the content of the statement is unclear or when the speaking speed is too fast. If the generative AI determines that the content of the statement is inappropriate, it identifies the reason and generates a revised version. For example, if the statement is abstract and lacks specificity, the generative AI will generate a revised version such as "Please explain in more detail." Also, if the speaking speed is too fast, the generative AI will generate a revised version such as "Please slow down your speaking speed." Furthermore, the analysis unit also analyzes the participants' reactions. Using facial recognition technology, it analyzes the participants' facial expressions and detects expressions of confusion, interest, etc. For example, if a participant shows a confused expression, the analysis unit identifies the reason and determines that the content of the statement may be unclear. In this way, the analysis unit can quickly and accurately analyze the collected data and determine the appropriateness of the content of the statements. Furthermore, the analysis unit can utilize past data and statistical information to analyze trends and patterns in the content of speech. This allows the analysis unit to not only grasp the situation in real time but also to respond to long-term improvements in the content of speech, thereby improving the reliability and effectiveness of the entire system.

[0060] The proposal department proposes revisions based on the results analyzed by the analysis department. Specifically, the proposal department presents the speaker with revisions generated by the generation AI. For example, it might suggest revisions such as "Please explain in more detail" or "Please slow down your speaking speed." The proposal department provides real-time feedback to the speaker to help them appropriately revise their speech. The proposal department checks whether the speaker accepts the revisions and can also propose additional revisions if necessary. For example, if the speaker fails to provide a specific answer to the question "What exactly is the problem?", the proposal department might suggest an additional revision such as "Please explain with a specific example." The proposal department also monitors the speaker's response to ensure that the revisions are implemented appropriately. For example, it checks whether the speaker was able to slow down their speaking speed and proposes further revisions if necessary. This allows the proposal department to continuously improve the appropriateness of the speech and ensure that the intent is accurately conveyed to other participants. Furthermore, the proposal department can collect feedback from the speaker and continuously improve the accuracy and effectiveness of the revisions. This allows the proposal team to appropriately revise what they say during online meetings, ensuring that their intentions are accurately conveyed to other participants.

[0061] The collection unit can collect chat content and participant responses. For example, the collection unit can collect chat content. For instance, if another participant asks "What exactly is the problem?" in the chat, the collection unit can collect that content. The collection unit can also collect participant responses. For example, if a participant shows a confused expression through the camera, that response can be collected. By collecting chat content and participant responses, the appropriateness of the statements can be judged more accurately. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input chat content into AI, which can then analyze and collect the chat content.

[0062] The analysis unit can analyze the collected chat content and participants' responses. For example, the analysis unit can analyze the collected chat content. For instance, if another participant asks "What exactly is the problem?" in the chat, the analysis unit can analyze that question. The analysis unit can also analyze the collected participants' responses. For example, if a participant shows a confused expression through the camera, the analysis unit can analyze that response. By analyzing the collected chat content and participants' responses, the appropriateness of the statements can be judged more accurately. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the collected chat content and participants' responses into the generative AI, which then analyzes them to determine the appropriateness of the statements.

[0063] The suggestion department can propose revisions to a speaker if it determines that the content of their statement is inappropriate. Examples of situations where the suggestion department might address a speaker include when the statement is unclear or when the speaker is speaking too quickly. The suggestion department can propose revisions such as, "Please explain in more detail," or "Please slow down your speaking speed." By proposing revisions when the content of a statement is deemed inappropriate, the appropriateness of the statement is improved. Some or all of the above processing in the suggestion department is performed using a generation AI. For example, if the suggestion department determines that the content of a statement is inappropriate, it can input that information into the generation AI, which can then generate revisions and propose them to the speaker.

[0064] The proposal unit can notify the speaker of the proposed revisions. The proposal unit can notify the speaker of the proposed revisions by methods such as pop-up notifications, email notifications, or chat notifications. This improves the appropriateness of the content of the statement by notifying the speaker of the proposed revisions. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit can select a method for notifying the speaker of the proposed revisions generated by the generation AI and then send the notification.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of collecting the utterances based on the estimated emotions. For example, if the user is nervous, the data collection unit can slightly delay the collection of the utterances to give the user time to relax. If the user is relaxed, the data collection unit can collect the utterances in real time and immediately send them for analysis. Furthermore, if the user is in a hurry, the data collection unit can quickly collect the utterances and immediately send them for analysis. By adjusting the timing of utterance collection based on the user's emotions, the data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0066] The data collection unit can analyze the user's past speech history and select the optimal collection method when collecting speech content. For example, the data collection unit can prioritize the collection of phrases and keywords that the user has frequently used in the past. The data collection unit can also analyze the user's past speech patterns to ensure that important speeches are not missed. Furthermore, the data collection unit can adjust the collection method by referring to the tone and speed of the user's past speeches. This allows the optimal collection method to be selected by analyzing the user's past speech history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past speech history into AI, which can then select the optimal collection method.

[0067] The collection unit can filter the collected utterances based on the user's current projects and areas of interest. For example, the collection unit can prioritize collecting utterances related to projects the user is currently working on. It can also filter and collect utterances containing keywords related to the user's areas of interest. Furthermore, the collection unit can collect utterances related to topics the user has previously shown interest in. This allows for the collection of highly relevant utterances by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input user project information and areas of interest data into an AI, which can then perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of the statements to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize collecting important statements. If the user is relaxed, the data collection unit can collect all statements equally. Furthermore, if the user is tense, the data collection unit can postpone collecting less important statements. In this way, by determining the priority of the statements to collect based on the user's emotions, important statements can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the statements.

[0069] The data collection unit can prioritize collecting relevant statements based on the user's geographical location information when collecting statements. For example, if the user is in a specific region, the data collection unit can prioritize collecting statements related to that region. Furthermore, if the user is on the move, the data collection unit can prioritize collecting statements related to their destination. Additionally, if the user is participating in a specific event, the data collection unit can prioritize collecting statements related to that event. This allows for the collection of more relevant information by prioritizing the collection of relevant statements based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into an AI, which can then prioritize the collection of relevant statements.

[0070] The data collection unit can analyze the user's social media activity and collect relevant statements when collecting the content of their posts. For example, the data collection unit can prioritize collecting posts that contain keywords frequently used by the user on social media. The data collection unit can also analyze the user's social media activity patterns and collect relevant statements. Furthermore, the data collection unit can collect posts related to topics the user has shown interest in on social media. In this way, relevant statements can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI, which can then collect relevant statements.

[0071] The analysis unit can estimate the user's emotions and adjust the analysis method of the utterance based on the estimated emotions. For example, if the user is nervous, the analysis unit can carefully analyze the utterance to avoid misunderstandings. If the user is relaxed, the analysis unit can quickly analyze the utterance. Furthermore, if the user is excited, the analysis unit can analyze the utterance in detail to avoid missing important points. In this way, by adjusting the analysis method of the utterance based on the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the analysis method.

[0072] The analysis unit can adjust the level of detail of its analysis based on the importance of the statements it analyzes. For example, it can perform a detailed analysis on important statements and a simplified analysis on less important statements. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the statements. This allows for a detailed analysis of important statements by adjusting the level of detail based on their importance. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs importance data of the statements into the generating AI, which can then adjust the level of detail of the analysis based on their importance.

[0073] The analysis unit can apply different analysis algorithms depending on the category of the utterance during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical utterances. It can also apply a business-oriented analysis algorithm to business-related utterances. Furthermore, it can apply a general analysis algorithm to casual utterances. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the utterance. Some or all of the above-described processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs utterance category data into the generation AI, which then applies an appropriate analysis algorithm according to the category.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the display method.

[0075] The analysis unit can determine the priority of analysis based on the submission date of the statements during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent statements. Furthermore, the analysis unit can determine the priority of analysis for past statements according to their importance. In addition, the analysis unit can adjust the analysis schedule based on the submission date of the statements. This allows for the prioritization of the most recent statements by determining the analysis priority based on the submission date. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the submission date data of the statements into the generation AI, which can then determine the analysis priority based on the submission date.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the statements during analysis. For example, the analysis unit can prioritize the analysis of statements with high relevance. It can also postpone the analysis of statements with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the statements. This allows for the prioritization of highly relevant statements by adjusting the order of analysis based on the relevance of the statements. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs relevance data of the statements into the generation AI, which can then adjust the order of analysis based on the relevance.

[0077] The suggestion unit can estimate the user's emotions and adjust the way the suggested revisions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can suggest revisions in gentle language. If the user is relaxed, the suggestion unit can suggest detailed revisions. Furthermore, if the user is in a hurry, the suggestion unit can suggest concise and quick revisions. By adjusting the way the revisions are presented based on the user's emotions, more appropriate revisions can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the way the suggested revisions are presented.

[0078] The proposal unit can adjust the level of detail in proposed revisions based on the importance of the statements made. For example, the proposal unit can propose detailed revisions for important statements. Conversely, it can propose simplified revisions for less important statements. Furthermore, the proposal unit can determine the priority of revisions according to the importance of the statements. This allows for the proposal of detailed revisions for important statements by adjusting the level of detail based on the importance of the statements. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs importance data of the statements into the generation AI, which can then adjust the level of detail in the revisions based on their importance.

[0079] The proposal unit can apply different revision algorithms depending on the category of the comment content when proposing revisions. For example, the proposal unit can apply a specialized revision algorithm to technical comments. It can also apply a business-oriented revision algorithm to business-related comments. Furthermore, it can apply a general revision algorithm to casual comments. By applying different revision algorithms depending on the category of the comment content, it can propose more appropriate revisions. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs the category data of the comment content into the generation AI, and the generation AI can apply an appropriate revision algorithm according to the category.

[0080] The suggestion unit can estimate the user's emotions and adjust the length of the suggested revisions based on those emotions. For example, if the user is nervous, the suggestion unit can suggest a short, concise revision. If the user is relaxed, the suggestion unit can suggest a longer revision with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can suggest a quick and concise revision. By adjusting the length of the revisions based on the user's emotions, more appropriate revisions can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the length of the revisions.

[0081] The proposal department can determine the priority of proposed revisions based on the submission timing of the statements made. For example, the proposal department can prioritize proposals for the most recent statements. Furthermore, for past statements, the proposal department can determine the priority of revisions based on their importance. In addition, the proposal department can adjust the schedule of revisions based on the submission timing of the statements. This allows the proposal department to prioritize proposals for the most recent statements by determining the priority of revisions based on the submission timing. Some or all of the above processing in the proposal department is performed using a generation AI. For example, the proposal department inputs the submission timing data of the statements into the generation AI, which can then determine the priority of revisions based on the submission timing.

[0082] The proposal unit can adjust the order of proposed revisions based on the relevance of the statements made. For example, the proposal unit can prioritize proposing revisions to highly relevant statements. It can also postpone proposing revisions to less relevant statements. Furthermore, the proposal unit can adjust the order of the proposed revisions based on the relevance of the statements. This allows the proposal unit to prioritize proposing revisions to highly relevant statements by adjusting the order of revisions based on the relevance of the statements. Some or all of the above processing in the proposal unit is performed using a generation AI. For example, the proposal unit inputs relevance data of the statements into the generation AI, which can then adjust the order of the proposed revisions based on their relevance.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] Online meeting support systems can also be equipped with a speech recognition unit. The speech recognition unit can analyze the tone and volume of the speaker's voice and provide additional information to determine the appropriateness of the content of the speech. For example, if a speaker is nervous, their voice tone is often higher, so the speech recognition unit can collect this information and provide it to the analysis unit. Also, if a speaker is speaking confidently, their volume is often higher, so this information can also be collected. Furthermore, if a speaker is emotional, the system can detect tremors in their voice and changes in their speaking speed and provide this information to the analysis unit. As a result, by using the speech recognition unit, the appropriateness of the content of the speech can be determined more accurately.

[0085] Online meeting support systems can also be equipped with a translation function. This function can translate spoken content in real time to facilitate communication between participants who speak different languages. For example, it can translate the content of an English-speaking speaker into Japanese and provide it to Japanese-speaking participants. Furthermore, the translation function can include a dictionary function to appropriately translate specialized terminology and industry-specific language. Additionally, the translation function can include a contextual analysis function to understand the context of spoken content and provide appropriate translations. This facilitates smoother communication between participants who speak different languages ​​and improves the quality of online meetings.

[0086] Online meeting support systems can also include a feedback function. This function can provide feedback to participants after the meeting. For example, it can provide feedback on the appropriateness of their contributions and areas for improvement. Furthermore, the feedback function can provide statistical information on participants' speaking frequency and duration. Additionally, it can provide individualized feedback based on participants' emotional states. This allows participants to receive feedback on their contributions and behavior during the meeting, enabling them to improve for future meetings.

[0087] Online meeting support systems can also include a scheduling unit. This unit can monitor the meeting's progress in real time and adjust it to ensure all agenda items are covered within the scheduled time. For example, it can manage the time allocation for each agenda item and suggest time adjustments to speakers if the meeting is likely to exceed the allotted time. The scheduling unit can also notify participants when it's time to move on to the next agenda item, depending on the meeting's progress. Furthermore, it can provide a report after the meeting regarding the time allocation and progress of each agenda item. This ensures smoother meeting progress and more efficient time management.

[0088] Online meeting support systems can also be equipped with an interaction section to further enhance participant engagement. This interaction section can provide interactive features to encourage active participation in meetings. For example, it can offer real-time polling and survey functions to collect participant opinions. Furthermore, the interaction section can provide a chat function that allows participants to easily post questions and comments. Additionally, the interaction section can visualize participant reactions in real time and provide feedback to speakers. This improves participant engagement and enhances the quality of the meeting.

[0089] Online meeting support systems can also include a data analysis unit. This unit can analyze data collected during meetings and generate reports to evaluate the effectiveness of those meetings. For example, it can provide analyses of participants' comments and statistical information on their reactions. Furthermore, the data analysis unit can analyze the progress of the meeting and the time allocation for each agenda item, and suggest areas for improvement. In addition, the data analysis unit can evaluate the atmosphere and engagement level of the meeting based on the emotional state of the participants. This allows for an objective evaluation of the meeting's effectiveness and provides data for improvement in future meetings.

[0090] Online meeting support systems can also include a reminder function. This function can send reminders to participants before and after meetings. For example, it can send reminders about the meeting start time and agenda. It can also send reminders during the meeting about important points and next actions. Furthermore, it can send follow-up reminders after the meeting to ensure participants don't forget the next steps. This allows participants to remember the meeting content and smoothly transition to their next actions.

[0091] Online meeting support systems can also include a virtual background feature. This feature allows participants to customize their backgrounds. For example, participants can choose a background of their office or home. Furthermore, the virtual background feature can provide backgrounds that match the meeting's theme. Additionally, it can suggest relaxing or concentration-enhancing backgrounds based on the participants' emotional state. This allows participants to participate in meetings in a more comfortable environment, improving the quality of the meetings.

[0092] Online meeting support systems can also include a note-taking section. This section can provide participants with features for taking notes during the meeting. For example, they can easily jot down what was said and important points. Furthermore, the note-taking section can organize the notes after the meeting and provide them to the participants. In addition, the note-taking section can suggest note-taking and organization methods based on the participants' emotional state. This allows participants to take notes efficiently and review the meeting content later.

[0093] Online meeting support systems can also include an archiving section. This section can provide functionality for saving meeting recordings and minutes for later reference. For example, meeting recordings can be saved to the cloud for participants to view later. The archiving section can also automatically generate and provide meeting minutes to participants. Furthermore, the archiving section can highlight key points and action items from the meeting, making them easily accessible to participants. This allows for a review of the meeting content later, ensuring that important information is not missed.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The data collection unit collects the content of what is said during the online meeting. For example, if a speaker says, "I've encountered a problem with my task," the unit will collect that statement. The data collection unit also collects the content of the chat. For example, if another participant asks in the chat, "What exactly is the problem?", the unit can collect that statement. Furthermore, the data collection unit also collects participants' reactions. For example, if a participant shows a confused expression on camera, the unit can collect that reaction. Step 2: The analysis unit analyzes the utterances collected by the collection unit. The analysis unit uses a generation AI to determine whether the utterances are appropriate or inappropriate. For example, this includes cases where the utterances are unclear or the speaking speed is too fast. If the analysis unit determines that the utterances are inappropriate, it proposes revisions to the speaker. Step 3: The proposal team proposes revisions based on the results analyzed by the analysis team. For example, the proposal team might suggest revisions to the speaker such as, "Please explain in more detail," or "Please slow down your speaking speed."

[0096] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0099] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects spoken content and participant reactions using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to determine the appropriateness of the spoken content. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates revised proposals based on the analysis results, which are then proposed to the speaker through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] As shown in Figure 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.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects spoken content and participant reactions using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to determine the appropriateness of the spoken content. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates revised proposals based on the analysis results and proposes them to the speaker through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects spoken content and participant reactions using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to determine the appropriateness of the spoken content. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates revised proposals based on the analysis results and proposes them to the speaker through the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects spoken content and participant reactions using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to determine the appropriateness of the spoken content. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates revised proposals based on the analysis results, which are then presented to the speaker through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0158] 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.

[0159] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) A collection department that collects the content of speeches during online meetings, An analysis unit analyzes the content of speeches collected by the aforementioned collection unit, The system includes a proposal unit that proposes a revised plan based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the chat content and participant responses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected chat content and participants' responses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, If a statement is deemed inappropriate, we will propose a revised version to the speaker. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Notify the speaker of the proposed revisions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting their comments based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting user comments, the system analyzes the user's past comment history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting comments, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the content to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting user comments, the system prioritizes collecting comments that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting statements, the system analyzes users' social media activity and collects relevant statements. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method of their statements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the statements made. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the content of the statement. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the statements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the statements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way the proposed revisions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When proposing amendments, adjust the level of detail in the amendments based on the importance of the points raised. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing revisions, different revision algorithms are applied depending on the category of the content of the statement. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the length of the proposed revisions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When proposing amendments, the priority of the amendments will be determined based on when the comments were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing amendments, adjust the order of the amendments based on the relevance of the statements made. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects the content of speeches during online meetings, An analysis unit analyzes the content of speeches collected by the aforementioned collection unit, The system includes a proposal unit that proposes a revised plan based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect the chat content and participant responses. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected chat content and participants' responses. The system according to feature 1.

4. The aforementioned proposal section is, If a statement is deemed inappropriate, we will propose a revised version to the speaker. The system according to feature 1.

5. The aforementioned proposal section is, Notify the speaker of the proposed revisions. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting their comments based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting user comments, the system analyzes the user's past comment history to select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting comments, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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