System

Abstract

An object of a system according to an embodiment is to smoothly perform work progress even when a specific employee is absent.SOLUTION: A system according to an embodiment includes an answer generation unit, an accuracy display unit, and an indication unit. The answer generation unit learns documents and data written by employees. The accuracy display unit may display the accuracy of the answer generated by the answer generator as a percentage. The explicit portion explicitly indicates that the response is made by AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a risk that work progress will be delayed if a particular employee is absent.

[0005] The system according to the embodiment aims to smoothly carry out business even when a specific employee is absent. [Means for solving the problem]

[0006] The system according to the embodiment includes a response generation unit, an accuracy display unit, and a notice unit. The response generation unit learns documents and data written by employees. The accuracy display unit indicates the accuracy of the response generated by the response generation unit as a percentage. The notice unit indicates that the response was generated by AI. [Effects of the Invention]

[0007] The system according to the embodiment can smoothly carry out business even when a particular employee is absent. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) In an information acquisition system according to an embodiment of the present invention, when a specific employee is absent, a generation AI that has learned documents and data written by the employee responds on their behalf via chat or email, and displays the accuracy of the answer as a percentage. This allows other employees to quickly obtain the necessary information even when the specific employee is absent, allowing work to proceed smoothly.

[0029] An information acquisition system according to an embodiment includes a response generation unit, an accuracy display unit, and a display unit. The response generation unit learns documents and data written by employees and generates responses on their behalf via chat or email. For example, the response generation unit analyzes the employee's past documents and data and generates appropriate responses based on their content. The response generation unit can also generate responses to questions using a generation AI (e.g., a text generation AI or a multimodal generation AI). The accuracy display unit displays the accuracy of the response generated by the response generation unit as a percentage. For example, the accuracy display unit displays "This information is accurate with a 90% probability." When the generation AI generates a response, the accuracy display unit calculates the accuracy of the response and displays it as a percentage. The display unit displays the accuracy of the response as an AI ghost. For example, the display unit displays "This response was generated by AI ghost" at the beginning of the response. This allows other employees to recognize that the response was generated by AI and to perform additional confirmation if necessary. As a result, the information acquisition system according to the embodiment allows other employees to quickly acquire the necessary information even when a specific employee is absent, enabling smooth business operations. For example, by quickly responding to project progress checks and technical questions, business operations can be made more efficient and delays can be avoided.

[0030] The response generation unit can learn optimal responses to similar questions based on past response history. For example, the response generation unit uses a generation AI to analyze past response history and learn optimal responses to similar questions. For example, based on a past response history in response to the question "What is the progress of Project X?" with "Project X is currently in Phase 2, and the next step is the testing phase," the response will be the same for similar questions. This improves the accuracy of responses by generating optimal responses based on past response history.

[0031] The response generation unit can adjust the level of detail in the response depending on the questioner's job title or level of expertise. For example, the generation AI adjusts the level of detail in the response taking into account the questioner's job title. For example, it provides an overview to a manager and detailed technical information to an engineer. The response generation unit also adjusts the level of detail in the response depending on the questioner's level of expertise. For example, it provides a basic explanation to a beginner, a detailed explanation to an intermediate user, and specialized information to an advanced user. This makes it possible to provide an appropriate response depending on the questioner's job title and level of expertise.

[0032] The response generation unit supports voice input and can provide appropriate responses to questions input through voice. For example, the response generation unit uses a generation AI to analyze voice input and generate appropriate responses to voice questions. For example, in response to a voice question such as "Please tell me the progress of Project X," the response may be in text form, such as "Project X is currently in Phase 2, and the next step is the testing phase." The response generation unit also uses voice recognition technology to convert voice input into text and generate responses based on that text. For example, voice recognition software automatically analyzes the voice and saves it as text. This makes it possible to provide appropriate responses to questions input through voice.

[0033] The response generation unit can support multiple languages, making it usable in international teams. For example, the response generation unit's generation AI can support multiple languages, making it usable in international teams. For example, it generates responses in languages ​​such as English, French, and Chinese. The response generation unit also uses language translation technology to automatically translate questions and generate responses based on the translation results. For example, if a question is entered in Japanese, the generation AI translates the question into English and generates a response in English. This makes it possible to support multiple languages ​​and make it usable in international teams.

[0034] The accuracy display unit can calculate a more accurate accuracy based on past accuracy evaluation data. For example, the generation AI analyzes past accuracy evaluation data to calculate the accuracy of the response. For example, the accuracy display unit calculates the accuracy of the current response based on how accurate the past responses were. The accuracy display unit also displays the accuracy of the response as a percentage based on the past accuracy evaluation data. For example, it displays something like, "This information is accurate with a 90% probability." This improves the reliability of the response by calculating a more accurate accuracy based on past accuracy evaluation data.

[0035] The accuracy display section can clearly indicate the data source on which the response is based, thereby improving reliability. For example, the accuracy display section can clearly indicate the data source on which the generation AI bases its response, thereby improving reliability. For example, it can display, "This information is based on the progress report of Project X." Furthermore, by clearly indicating the data source on which the response is based, the accuracy display section makes it easier for other employees to judge the reliability of the response. For example, it can display, "This information is based on technical documentation." In this way, by clearly indicating the data source on which the response is based, the reliability of the response is improved.

[0036] The accuracy display unit can display the accuracy of the response using a graph or chart that is visually easy to understand. For example, the accuracy display unit uses a graph or chart so that the generation AI can visually display the accuracy of the response. For example, it displays a pie chart that shows the accuracy as a percentage. The accuracy display unit also visually displays the accuracy of the response using a bar graph or line graph. For example, it displays a line graph that shows the fluctuations in accuracy. In this way, visually displaying the accuracy of the response makes it easier for the user to understand.

[0037] The accuracy display unit can refer to the accuracy history of past responses and display trends. For example, the generation AI refers to the accuracy history of past responses and displays trends. For example, it displays a graph showing the fluctuations in accuracy over the past month. The accuracy display unit also displays the trend in response accuracy based on past accuracy history. For example, it displays a graph showing the upward or downward trend in accuracy. In this way, by referring to past accuracy history and displaying trends, the reliability of responses is improved.

[0038] The explicit statement section can clearly indicate that the response is AI not only at the beginning of the response but throughout the entire content of the response. For example, the explicit statement section may clearly indicate that the generating AI is AI Ghost not only at the beginning of the response but throughout the entire content of the response. For example, the explicit statement section may display "This response was written by AI Ghost" at the beginning of each paragraph of the response. The explicit statement section may also clearly indicate that the response is written by AI Ghost throughout the entire content of the response, allowing other employees to clearly recognize the source of the response. For example, the explicit statement section may display "This response was written by AI Ghost" in each section of the response. In this way, by clearly indicating that the response is written by AI Ghost throughout the entire content of the response, users can clearly recognize the source of the response.

[0039] The revealing unit can collect user feedback on the content of the response and improve the way the AI ​​ghost is revealed. For example, the revealing unit collects user feedback on the content of the response made by the generation AI and improves the way the AI ​​ghost is revealed. For example, if the user is dissatisfied with the way the AI ​​ghost is revealed, the revealing unit improves the way the AI ​​ghost is revealed based on that feedback. The revealing unit also adjusts the way the AI ​​ghost is revealed based on user feedback. For example, it changes the way the AI ​​ghost is revealed so that it is easier for the user to understand. In this way, by collecting user feedback and improving the way the AI ​​ghost is revealed, user understanding and trust are improved.

[0040] The revealing unit can collect user feedback on the content of the response in real time and improve the way the AI ​​ghost is revealed. For example, the revealing unit collects user feedback on the content of the response from the generation AI in real time and improves the way the AI ​​ghost is revealed. For example, if the user is dissatisfied with the way the AI ​​ghost is revealed, the revealing unit improves the way the AI ​​ghost is revealed based on that feedback. The revealing unit also adjusts the way the AI ​​ghost is revealed based on user feedback. For example, it changes the way the AI ​​ghost is revealed to make it easier for the user to understand. In this way, by collecting user feedback in real time and improving the way the AI ​​ghost is revealed, user understanding and reliability are improved.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The information acquisition system may further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes, for example, what kind of information the user has searched for in the past and what kind of questions the user has asked, and generates a response based on the results. For example, if the user has frequently searched for information about Project X in the past, the latest information about Project X will be provided preferentially. The behavior analysis unit can also learn the user's behavior patterns and predict and provide the information the user will need next. This makes it possible to generate a more appropriate response based on the user's behavior history.

[0043] The information acquisition system may further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, analyzes the user's calendar or planner and adjusts the timing of replies. For example, if the user is in a meeting, the response may be provided after the meeting ends. The schedule management unit may also set the priority of replies based on the user's schedule. For example, if there is an urgent appointment, information related to that appointment is provided first. This makes it possible to provide appropriate replies according to the user's schedule.

[0044] The information acquisition system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit may, for example, analyze the user's past hobbies and interests and generate a response based on the results. For example, if the user is interested in sports, it may provide sports-related information. The hobby analysis unit may also customize the content of the response based on the user's hobbies and interests. For example, if the user is interested in music, an example related to music will be used. This makes it possible to provide an appropriate response according to the user's hobbies and interests.

[0045] The information acquisition system may further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit, for example, analyzes what kind of learning the user has done in the past and generates a response based on the results. For example, if the user has studied a specific technology, it provides information related to that technology. The learning analysis unit can also customize the content of the response based on the user's learning history. For example, if the user is knowledgeable in a specific field, it provides specialized information related to that field. This makes it possible to provide an appropriate response based on the user's learning history.

[0046] The information acquisition system may further include a feedback collection unit that collects user feedback and improves the accuracy of responses. The feedback collection unit, for example, collects how users have rated responses and improves the accuracy of responses based on the results. For example, if a user has given a high rating to a response, the feedback collection unit learns how that response was generated and reflects this in future responses. The feedback collection unit can also adjust the content of the response based on user feedback. For example, if a user has given a low rating to a response, the feedback collection unit improves the method of generating that response. This makes it possible to improve the accuracy of responses based on user feedback.

[0047] The information acquisition system may further include a project management unit that supports the user's project management. The project management unit, for example, manages the progress status of a project currently underway by the user and generates a response based on the results. For example, if a user wants to know the progress status of Project X, the project management unit provides the latest progress information. The project management unit may also manage tasks related to the user's project and generate a response based on the progress status of those tasks. This makes it possible to support the user's project management and provide appropriate responses.

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

[0049] Step 1: The response generator learns from documents and data written by employees and generates responses on their behalf via chat or email. For example, the response generator analyzes the employee's past documents and data and generates appropriate responses based on their content. The response generator can also use generative AI (e.g., text generation AI or multimodal generation AI) to generate responses to questions. Step 2: The accuracy display unit displays the accuracy of the response generated by the response generation unit as a percentage. For example, the accuracy display unit might display something like, "This information is accurate 90% of the time." When the generation AI generates a response, the accuracy display unit calculates the accuracy of the response and displays it as a percentage. Step 3: The Clarification Section will clearly indicate that the response was made by AI Ghost. For example, the Clarification Section will display "This response was made by AI Ghost" at the beginning of the response. This will allow other employees to recognize that the response was made by AI and to perform additional confirmation if necessary.

[0050] (Example 2) In an information acquisition system according to an embodiment of the present invention, when a specific employee is absent, a generation AI that has learned documents and data written by the employee responds on their behalf via chat or email, and displays the accuracy of the answer as a percentage. This allows other employees to quickly obtain the necessary information even when the specific employee is absent, allowing work to proceed smoothly.

[0051] An information acquisition system according to an embodiment includes a response generation unit, an accuracy display unit, and a display unit. The response generation unit learns documents and data written by employees and generates responses on their behalf via chat or email. For example, the response generation unit analyzes the employee's past documents and data and generates appropriate responses based on their content. The response generation unit can also generate responses to questions using a generation AI (e.g., a text generation AI or a multimodal generation AI). The accuracy display unit displays the accuracy of the response generated by the response generation unit as a percentage. For example, the accuracy display unit displays "This information is accurate with a 90% probability." When the generation AI generates a response, the accuracy display unit calculates the accuracy of the response and displays it as a percentage. The display unit displays the accuracy of the response as an AI ghost. For example, the display unit displays "This response was generated by AI ghost" at the beginning of the response. This allows other employees to recognize that the response was generated by AI and to perform additional confirmation if necessary. As a result, the information acquisition system according to the embodiment allows other employees to quickly acquire the necessary information even when a specific employee is absent, enabling smooth business operations. For example, by quickly responding to project progress checks and technical questions, business operations can be made more efficient and delays can be avoided.

[0052] The response generation unit can learn optimal responses to similar questions based on past response history. For example, the response generation unit uses a generation AI to analyze past response history and learn optimal responses to similar questions. For example, based on a past response history in response to the question "What is the progress of Project X?" with "Project X is currently in Phase 2, and the next step is the testing phase," the response will be the same for similar questions. This improves the accuracy of responses by generating optimal responses based on past response history.

[0053] The response generation unit can adjust the level of detail in the response depending on the questioner's job title or level of expertise. For example, the generation AI adjusts the level of detail in the response taking into account the questioner's job title. For example, it provides an overview to a manager and detailed technical information to an engineer. The response generation unit also adjusts the level of detail in the response depending on the questioner's level of expertise. For example, it provides a basic explanation to a beginner, a detailed explanation to an intermediate user, and specialized information to an advanced user. This makes it possible to provide an appropriate response depending on the questioner's job title and level of expertise.

[0054] The reply generation unit can use the emotion estimation function to estimate the emotion of the questioner and generate a reply in a tone that corresponds to that emotion. For example, the reply generation unit uses a generation AI to estimate the emotion of the questioner and generate a reply in a tone that corresponds to that emotion. For example, if the questioner is feeling anxious, the reply will be in a tone that gives a sense of security. If the questioner is feeling angry, the reply will be in a calm and polite tone. Furthermore, if the questioner is feeling happy, the reply will be in a tone that shows empathy. This makes it possible to reply in an appropriate tone that corresponds to the emotion of the questioner.

[0055] The response generation unit supports voice input and can provide appropriate responses to questions input through voice. For example, the response generation unit uses a generation AI to analyze voice input and generate appropriate responses to voice questions. For example, in response to a voice question such as "Please tell me the progress of Project X," the response may be in text form, such as "Project X is currently in Phase 2, and the next step is the testing phase." The response generation unit also uses voice recognition technology to convert voice input into text and generate responses based on that text. For example, voice recognition software automatically analyzes the voice and saves it as text. This makes it possible to provide appropriate responses to questions input through voice.

[0056] The response generation unit can support multiple languages, making it usable in international teams. For example, the response generation unit's generation AI can support multiple languages, making it usable in international teams. For example, it generates responses in languages ​​such as English, French, and Chinese. The response generation unit also uses language translation technology to automatically translate questions and generate responses based on the translation results. For example, if a question is entered in Japanese, the generation AI translates the question into English and generates a response in English. This makes it possible to support multiple languages ​​and make it usable in international teams.

[0057] The reply generation unit can use the emotion estimation function to monitor the emotion of the questioner in real time and provide feedback according to the emotion. For example, the reply generation unit uses the emotion estimation function to monitor the emotion of the questioner in real time and provide feedback according to the emotion. For example, if the questioner is feeling anxious, feedback that gives a sense of security is provided. If the questioner is feeling angry, calm and polite feedback is provided. Furthermore, if the questioner is feeling happy, feedback that shows empathy is provided. This makes it possible to provide appropriate feedback according to the questioner's emotions.

[0058] The accuracy display unit can calculate a more accurate accuracy based on past accuracy evaluation data. For example, the generation AI analyzes past accuracy evaluation data to calculate the accuracy of the response. For example, the accuracy display unit calculates the accuracy of the current response based on how accurate the past responses were. The accuracy display unit also displays the accuracy of the response as a percentage based on the past accuracy evaluation data. For example, it displays something like, "This information is accurate with a 90% probability." This improves the reliability of the response by calculating a more accurate accuracy based on past accuracy evaluation data.

[0059] The accuracy display section can clearly indicate the data source on which the response is based, thereby improving reliability. For example, the accuracy display section can clearly indicate the data source on which the generation AI bases its response, thereby improving reliability. For example, it can display, "This information is based on the progress report of Project X." Furthermore, by clearly indicating the data source on which the response is based, the accuracy display section makes it easier for other employees to judge the reliability of the response. For example, it can display, "This information is based on technical documentation." In this way, by clearly indicating the data source on which the response is based, the reliability of the response is improved.

[0060] The accuracy display unit can use the emotion estimation function to collect the user's emotional reaction to the accuracy of the reply and use the collected information to improve the accuracy display. The accuracy display unit, for example, uses the emotion estimation function to collect the user's emotional reaction to the accuracy of the reply and use the collected information to improve the accuracy display. For example, if the user feels anxious, the accuracy display is improved. The accuracy display unit also adjusts the method of displaying the accuracy based on the user's emotional reaction. For example, it adopts a display method that makes the user feel reassured. In this way, the reliability of the reply is improved by collecting the user's emotional reaction and using the collected information to improve the accuracy display.

[0061] The accuracy display unit can display the accuracy of the response using a graph or chart that is visually easy to understand. For example, the accuracy display unit uses a graph or chart so that the generation AI can visually display the accuracy of the response. For example, it displays a pie chart that shows the accuracy as a percentage. The accuracy display unit also visually displays the accuracy of the response using a bar graph or line graph. For example, it displays a line graph that shows the fluctuations in accuracy. In this way, visually displaying the accuracy of the response makes it easier for the user to understand.

[0062] The accuracy display unit can refer to the accuracy history of past responses and display trends. For example, the generation AI refers to the accuracy history of past responses and displays trends. For example, it displays a graph showing the fluctuations in accuracy over the past month. The accuracy display unit also displays the trend in response accuracy based on past accuracy history. For example, it displays a graph showing the upward or downward trend in accuracy. In this way, by referring to past accuracy history and displaying trends, the reliability of responses is improved.

[0063] The accuracy display unit can use the emotion estimation function to monitor the user's emotional reaction to the accuracy of the reply in real time and provide feedback. The accuracy display unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the accuracy of the reply in real time and provide feedback. For example, if the user feels anxious, feedback that gives a sense of security is provided. The accuracy display unit also adjusts the method of displaying accuracy based on the user's emotional reaction. For example, a display method that gives the user a sense of security is adopted. In this way, by monitoring the user's emotional reaction in real time and providing feedback, the reliability of the reply is improved.

[0064] The explicit statement section can clearly indicate that the response is AI not only at the beginning of the response but throughout the entire content of the response. For example, the explicit statement section may clearly indicate that the generating AI is AI Ghost not only at the beginning of the response but throughout the entire content of the response. For example, the explicit statement section may display "This response was written by AI Ghost" at the beginning of each paragraph of the response. The explicit statement section may also clearly indicate that the response is written by AI Ghost throughout the entire content of the response, allowing other employees to clearly recognize the source of the response. For example, the explicit statement section may display "This response was written by AI Ghost" in each section of the response. In this way, by clearly indicating that the response is written by AI Ghost throughout the entire content of the response, users can clearly recognize the source of the response.

[0065] The revealing unit can collect user feedback on the content of the response and improve the way the AI ​​ghost is revealed. For example, the revealing unit collects user feedback on the content of the response made by the generation AI and improves the way the AI ​​ghost is revealed. For example, if the user is dissatisfied with the way the AI ​​ghost is revealed, the revealing unit improves the way the AI ​​ghost is revealed based on that feedback. The revealing unit also adjusts the way the AI ​​ghost is revealed based on user feedback. For example, it changes the way the AI ​​ghost is revealed so that it is easier for the user to understand. In this way, by collecting user feedback and improving the way the AI ​​ghost is revealed, user understanding and trust are improved.

[0066] The display unit can use the emotion estimation function to collect the user's emotional reactions to the AI ​​ghost's display and use the collected information to improve the display method. For example, the display unit can use the emotion estimation function to collect the user's emotional reactions to the AI ​​ghost's display and use the collected information to improve the display method. For example, if the user feels anxious, the display method is improved. The display unit also adjusts the AI ​​ghost's display method based on the user's emotional reactions. For example, the display unit can adopt a display method that makes the user feel reassured. In this way, collecting the user's emotional reactions and using the information to improve the display method improves user understanding and trust.

[0067] The revealing unit can collect user feedback on the content of the response in real time and improve the way the AI ​​ghost is revealed. For example, the revealing unit collects user feedback on the content of the response from the generation AI in real time and improves the way the AI ​​ghost is revealed. For example, if the user is dissatisfied with the way the AI ​​ghost is revealed, the revealing unit improves the way the AI ​​ghost is revealed based on that feedback. The revealing unit also adjusts the way the AI ​​ghost is revealed based on user feedback. For example, it changes the way the AI ​​ghost is revealed to make it easier for the user to understand. In this way, by collecting user feedback in real time and improving the way the AI ​​ghost is revealed, user understanding and reliability are improved.

[0068] The display unit can use the emotion estimation function to monitor the user's emotional reaction to the content of the reply in real time and provide feedback. The display unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the content of the reply in real time and provide feedback. For example, if the user feels anxious, it provides feedback that gives a sense of security. The display unit also adjusts the display method of the AI ​​ghost based on the user's emotional reaction. For example, it adopts a display method that makes the user feel secure. In this way, by monitoring the user's emotional reaction in real time and providing feedback, user understanding and trust are improved.

[0069] The display unit can use the emotion estimation function to monitor the user's emotional reaction to the AI ​​ghost's display in real time and provide feedback. The display unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the AI ​​ghost's display in real time and provide feedback. For example, if the user feels anxious, it provides feedback that gives a sense of security. The display unit also adjusts the AI ​​ghost's display method based on the user's emotional reaction. For example, it adopts a display method that makes the user feel secure. In this way, by monitoring the user's emotional reaction in real time and providing feedback, user understanding and trust are improved.

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

[0071] The information acquisition system may further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes, for example, what kind of information the user has searched for in the past and what kind of questions the user has asked, and generates a response based on the results. For example, if the user has frequently searched for information about Project X in the past, the latest information about Project X will be provided preferentially. The behavior analysis unit can also learn the user's behavior patterns and predict and provide the information the user will need next. This makes it possible to generate a more appropriate response based on the user's behavior history.

[0072] The information acquisition system may further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, analyzes the user's calendar or planner and adjusts the timing of replies. For example, if the user is in a meeting, the response may be provided after the meeting ends. The schedule management unit may also set the priority of replies based on the user's schedule. For example, if there is an urgent appointment, information related to that appointment is provided first. This makes it possible to provide appropriate replies according to the user's schedule.

[0073] The information acquisition system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, monitor the user's heart rate and stress level and adjust responses based on the results. For example, if the user shows a high stress level, the health management unit may provide responses in a relaxing tone. The health management unit may also provide appropriate advice depending on the user's health condition. For example, if the user is tired, the health management unit may urge the user to take a break. This makes it possible to provide appropriate responses depending on the user's health condition.

[0074] The information acquisition system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit may, for example, analyze the user's past hobbies and interests and generate a response based on the results. For example, if the user is interested in sports, it may provide sports-related information. The hobby analysis unit may also customize the content of the response based on the user's hobbies and interests. For example, if the user is interested in music, an example related to music will be used. This makes it possible to provide an appropriate response according to the user's hobbies and interests.

[0075] The information acquisition system can further include an emotion feedback unit that estimates the user's emotion and provides feedback according to the emotion. For example, if the user is feeling anxious, the emotion feedback unit provides feedback that gives a sense of security. If the user is feeling angry, the emotion feedback unit provides calm and polite feedback. If the user is feeling happy, the emotion feedback unit provides feedback that shows empathy. This makes it possible to provide appropriate feedback according to the user's emotion.

[0076] The information acquisition system may further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit, for example, analyzes what kind of learning the user has done in the past and generates a response based on the results. For example, if the user has studied a specific technology, it provides information related to that technology. The learning analysis unit can also customize the content of the response based on the user's learning history. For example, if the user is knowledgeable in a specific field, it provides specialized information related to that field. This makes it possible to provide an appropriate response based on the user's learning history.

[0077] The information acquisition system may further include an emotional tone unit that estimates the user's emotions and generates a response in a tone that corresponds to the emotion. For example, if the user is feeling anxious, the emotional tone unit provides a response in a tone that gives a sense of security. If the user is feeling angry, the emotional tone unit provides a response in a calm and polite tone. If the user is feeling happy, the emotional tone unit provides a response in a tone that shows empathy. This makes it possible to provide a response in an appropriate tone that corresponds to the user's emotions.

[0078] The information acquisition system may further include a feedback collection unit that collects user feedback and improves the accuracy of responses. The feedback collection unit, for example, collects how users have rated responses and improves the accuracy of responses based on the results. For example, if a user has given a high rating to a response, the feedback collection unit learns how that response was generated and reflects this in future responses. The feedback collection unit can also adjust the content of the response based on user feedback. For example, if a user has given a low rating to a response, the feedback collection unit improves the method of generating that response. This makes it possible to improve the accuracy of responses based on user feedback.

[0079] The information acquisition system can further include an emotion advice unit that estimates the user's emotion and provides advice according to the emotion. For example, if the user is feeling anxious, the emotion advice unit provides advice to relax. If the user is feeling angry, the emotion advice unit provides advice to stay calm. If the user is feeling happy, the emotion advice unit provides advice to share that happiness. This makes it possible to provide appropriate advice according to the user's emotion.

[0080] The information acquisition system may further include a project management unit that supports the user's project management. The project management unit, for example, manages the progress status of a project currently underway by the user and generates a response based on the results. For example, if a user wants to know the progress status of Project X, the project management unit provides the latest progress information. The project management unit may also manage tasks related to the user's project and generate a response based on the progress status of those tasks. This makes it possible to support the user's project management and provide appropriate responses.

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

[0082] Step 1: The response generator learns from documents and data written by employees and generates responses on their behalf via chat or email. For example, the response generator analyzes the employee's past documents and data and generates appropriate responses based on their content. The response generator can also use generative AI (e.g., text generation AI or multimodal generation AI) to generate responses to questions. Step 2: The accuracy display unit displays the accuracy of the response generated by the response generation unit as a percentage. For example, the accuracy display unit might display something like, "This information is accurate 90% of the time." When the generation AI generates a response, the accuracy display unit calculates the accuracy of the response and displays it as a percentage. Step 3: The Clarification Section will clearly indicate that the response was made by AI Ghost. For example, the Clarification Section will display "This response was made by AI Ghost" at the beginning of the response. This will allow other employees to recognize that the response was made by AI and to perform additional confirmation if necessary.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a response generator that learns from documents and data written by employees; an accuracy display unit that displays the accuracy of the response generated by the response generator as a percentage; and an indication unit that indicates that the response is from an AI. A system characterized by:

2. The response generation unit: Learns optimal responses to similar questions based on past response history 2. The system of claim 1.

3. The response generation unit: Responds to voice input and provides appropriate responses to questions input through the voice input.

2. The system of claim 1.

4. The accuracy display unit Calculate more accurate accuracy based on past accuracy evaluation data 2. The system of claim 1.

5. The response generation unit: Using the emotion estimation function, the emotion of the questioner is estimated and a response is generated in a tone that corresponds to that emotion.

2. The system of claim 1.

6. The accuracy display unit Using emotion estimation functionality, the user's emotional response to the accuracy of the response is collected and used to improve the accuracy display.

2. The system of claim 1.

7. The indicating portion is Using emotion estimation functionality, we will collect users' emotional responses to AI instructions and use them to improve the instructions.

2. The system of claim 1.

8. The indicating portion is Using an emotion estimation function, the user's emotional response to the content of the reply is monitored in real time and feedback is provided.

2. The system of claim 1.

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