System

Abstract

An object of a system according to an embodiment is to increase a user's motivation for learning and enable the user to acquire knowledge with fun.SOLUTION: A system according to an embodiment includes a reception unit, an accumulation unit, an evolution unit, a chatbot unit, a point unit, and a ranking unit. The reception unit receives a question. An expert answers the question received by the reception part, and the storage part stores the answer. The evolving unit evolves the AI based on the answers accumulated by the accumulating unit. In the chat bot unit, the AI evolved by the evolving unit can be used as a chat bot. The point unit gives a point based on the answer provided by the chatbot unit. The ranking section displays a ranking based on the points given by the point section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the question and answer process is monotonous, making it difficult to maintain the user's motivation to learn.

[0005] The system according to the embodiment aims to increase the user's motivation to learn and enable them to acquire knowledge in a fun way. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a storage unit, an evolution unit, a chatbot unit, a point unit, and a ranking unit. The reception unit receives questions. The storage unit has experts provide answers to the questions received by the reception unit and stores the answers. The evolution unit evolves the AI ​​based on the answers stored by the storage unit. The chatbot unit allows the AI ​​evolved by the evolution unit to be used as a chatbot. The point unit awards points based on the answers provided by the chatbot unit. The ranking unit displays a ranking based on the points awarded by the point unit. [Effects of the Invention]

[0007] The system according to the embodiment can increase the user's motivation to learn and enable them to acquire knowledge in a fun way. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A high-performance chatbot system according to an embodiment of the present invention accepts questions, has experts respond, accumulates the answers, evolves the AI, becomes usable as a chatbot, awards points, and displays rankings. The high-performance chatbot system accepts questions, has experts respond, accumulates the answers, evolves the AI, and becomes usable as a chatbot. Points are awarded for answers and knowledge acquired, and rankings are displayed. For example, in a high-performance chatbot system, a user submits a question. The question is then input into the AI. An expert then answers the question. This answer is accumulated in the AI, and the AI ​​evolves based on this information. For example, if a new project participant asks, "What is the purpose of this project?", the expert responds, "The purpose of this project is ____." This answer is accumulated in the AI, and the AI ​​can automatically provide an answer the next time a similar question is submitted. Furthermore, experts can earn points by answering questions. For example, 10 points are awarded for answering one question. In addition, questioners, such as new employees, project participants, and service users, can earn points by acquiring knowledge. For example, if a questioner checks the AI's answer and understands its content, they are awarded 5 points. These points are reflected in the ranking function, allowing them to compete with others. For example, top ranking participants are awarded badges and rewards. When a certain number of points are reached, their name appears in the ranking, encouraging competition with other participants. This allows knowledge to be acquired in a fun way. As a result, a high-performance chatbot system can consistently handle everything from accepting questions to accumulating answers, evolving the AI, using the chatbot, awarding points, and displaying the ranking. For example, new employees, project participants, and service users can enjoy acquiring knowledge while competing with others. Experts can also earn points by sharing their knowledge and aim to rank higher. This improves the overall knowledge level and realizes efficient information sharing.

[0029] A high-performance chatbot system according to an embodiment includes a reception unit, a storage unit, an evolution unit, a chatbot unit, a point unit, and a ranking unit. The reception unit receives questions. The questions may be in text, audio, or image format, but are not limited to these examples. For example, a user inputs a question in text format into the reception unit. The reception unit can also receive audio questions using voice recognition technology. The reception unit can also receive image questions using image recognition technology. For example, the reception unit analyzes images taken by a user with a smartphone camera and receives the images as questions. The storage unit allows experts to respond to the questions received by the reception unit and stores the responses. The storage unit stores the responses in a database, for example. The storage unit can also award points based on the quality and frequency of answers. For example, the storage unit awards points based on the accuracy and detail of the answers. The evolution unit evolves an AI based on the answers accumulated by the storage unit. The evolution unit evolves the AI ​​using, for example, a machine learning algorithm. The evolution unit can also improve the accuracy of the AI ​​by expanding the dataset. For example, the evolution unit adds new answer data to increase the AI's learning data. The chatbot unit makes the AI ​​evolved by the evolution unit available as a chatbot. The chatbot unit, for example, automatically provides answers to user questions. The chatbot unit can also provide optimal responses by referring to the user's past dialogue history. For example, the chatbot unit provides related information based on the user's past questions. The point unit awards points based on the answers provided by the chatbot unit. For example, the point unit awards points based on the quality and frequency of answers. The point unit can also award points based on the user's activity history. For example, the point unit awards points based on the number of questions and answers the user has made within a certain period of time. The ranking unit displays a ranking based on the points awarded by the point unit. For example, the ranking unit displays a ranking based on the accumulated points. The ranking unit can also display a ranking based on the user's activity history.For example, the ranking unit displays the ranking based on the number of points or badges that users have earned in the past. This allows the high-performance chatbot system according to the embodiment to consistently perform processes from accepting questions to accumulating answers, evolving AI, using chatbots, awarding points, and displaying rankings.

[0030] The point section can award points for answers or acquisition of knowledge. For example, the point section awards points when a user answers a question. The point section can also award points when a user acquires knowledge. For example, the point section awards points when a user checks an AI answer and understands its content. The point section can also award points when a user completes specific learning content. For example, the point section awards points when a user completes an online course. In this way, by awarding points for answers or acquisition of knowledge, it is possible to increase user motivation.

[0031] The ranking unit may include a badge unit that awards badges to users who reach a predetermined number of points. For example, the badge unit awards badges when a user reaches a certain number of points. The badge unit may also award badges based on the user's activity history. For example, the badge unit may award badges when a user provides an excellent answer to a specific question. The badge unit may also award badges when a user answers many questions within a specific period of time. For example, the badge unit may award badges when a user answers 10 or more questions within one week. In this way, awarding badges to users who reach a certain number of points can enhance the user's sense of accomplishment.

[0032] The ranking unit may include a reward unit that provides a reward to a user who reaches a predetermined number of points. For example, the reward unit provides a reward when a user reaches a certain number of points. The reward unit may also provide a reward based on the user's activity history. For example, the reward unit may provide a reward when a user provides an excellent answer to a specific question. The reward unit may also provide a reward when a user answers many questions within a specific period of time. For example, the reward unit may provide a reward when a user answers 10 or more questions within one week. In this way, by providing a reward to a user who reaches a certain number of points, it is possible to further increase the user's motivation.

[0033] The reception unit may include an acquisition unit that allows the questioner to acquire knowledge. The acquisition unit may, for example, provide an online course that allows the questioner to acquire knowledge. The acquisition unit may also hold a webinar that allows the questioner to acquire knowledge. For example, the acquisition unit may periodically hold a webinar on a specific topic. The acquisition unit may also provide materials that allow the questioner to acquire knowledge. For example, the acquisition unit may make materials on a specific topic available for download. Thus, by providing an acquisition unit that allows the questioner to acquire knowledge, the learning effect of the user can be improved.

[0034] When accepting a question, the acceptance unit can analyze the user's past question history and select an appropriate acceptance method. For example, the acceptance unit prioritizes accepting topics that the user has frequently asked questions about in the past. The acceptance unit can also automatically select the most appropriate answerer from the user's past question history. For example, the acceptance unit automatically suggests related questions based on the user's past question history. The acceptance unit can also automatically suggest related questions based on the user's past question history. For example, the acceptance unit analyzes the user's past question history and suggests related questions. In this way, by analyzing the user's past question history, the most appropriate acceptance method can be selected, enabling efficient question acceptance.

[0035] When receiving a question, the reception unit can filter the questions based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives questions related to a project in which the user is currently involved. The reception unit can also filter and receive related questions based on the user's area of ​​interest. For example, the reception unit suggests appropriate questions depending on the progress of the user's project. The reception unit can also filter and receive related questions based on the user's area of ​​interest. For example, the reception unit suggests related questions based on the user's area of ​​interest. In this way, by filtering questions based on the user's current project or area of ​​interest, it is possible to preferentially receive highly relevant questions.

[0036] When accepting a question, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user selects voice input, the acceptance unit accepts the question using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the question using text analysis technology. For example, if the user selects image input, the acceptance unit accepts the question using image recognition technology. Furthermore, the acceptance unit can also select the optimal acceptance means according to the user's input method. For example, if the user selects voice input, the acceptance unit accepts the question using voice recognition technology. This allows for selecting the optimal acceptance means according to the user's input method, thereby improving user convenience.

[0037] When accepting questions, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting questions related to that area. The acceptance unit can also provide relevant local information based on the user's current location. For example, the acceptance unit selects the most appropriate answerer based on the user's geographical location information. This allows highly relevant questions to be prioritized by taking into account the user's geographical location information.

[0038] When accepting a question, the acceptance unit can analyze the user's social media activity and accept related questions. For example, the acceptance unit can analyze the content of the user's social media posts and suggest related questions. The acceptance unit can also accept optimal questions based on the user's social media activity history. For example, the acceptance unit can suggest related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related questions can be accepted efficiently.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit selects the optimal reception method, for example, based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. For example, the reception unit improves the question reception procedure based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal reception method can be selected and user satisfaction can be improved.

[0040] When storing answers, the storage unit can adjust the level of detail of the storage based on the importance of the answer. For example, the storage unit stores answers with high importance in detail and stores answers with low importance in brief. The storage unit can also adjust the range of information to be stored depending on the importance of the answer. For example, the storage unit collects and stores information on answers with high importance from multiple data sources. This allows for efficient storage of answers by adjusting the level of detail of the storage based on the importance of the answer.

[0041] When storing answers, the storage unit can apply different storage algorithms depending on the category of the answer. For example, the storage unit stores answers to technical questions using an algorithm suitable for the technical category. The storage unit can also store answers to business-related questions using an algorithm suitable for the business category. For example, the storage unit stores answers to general questions using an algorithm suitable for the general category. This allows for efficient storage of answers by applying different storage algorithms depending on the category of the answer.

[0042] When storing answers, the storage unit can improve the accuracy of storage by referring to the user's past answer results. The storage unit, for example, analyzes the user's past answer results to improve the accuracy of storage. The storage unit can also select the optimal storage method based on the user's past answer results. For example, the storage unit adjusts the storage algorithm by referring to the user's past answer results. In this way, the accuracy of storage can be improved by referring to the user's past answer results.

[0043] When storing answers, the storage unit can determine the storage priority based on the time of submission of the answers. For example, the storage unit stores the most recent answers with priority, and older answers are stored later. The storage unit can also adjust the storage order based on the time of submission of the answers. For example, the storage unit stores answers that were submitted close together. In this way, by determining the storage priority based on the time of submission of the answers, the most recent information can be stored with priority.

[0044] When storing answers, the storage unit can adjust the order of storage based on the relevance of the answers. For example, the storage unit stores highly relevant answers with priority, and stores less relevant answers later. The storage unit can also adjust the order of storage based on the relevance of the answers. For example, the storage unit stores highly relevant answers together. This allows for efficient storage of answers by adjusting the order of storage based on the relevance of the answers.

[0045] When storing answers, the storage unit can adjust the use of technical terms in the stored answers according to the user's level of expertise. For example, the storage unit stores answers that use a lot of technical terms for users with a high level of expertise. The storage unit can also store concise, easy-to-understand answers for users with a low level of expertise. For example, the storage unit adjusts the use of technical terms in the answers to be stored according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, answers that are easy for users to understand can be stored.

[0046] When evolving an AI, the evolution unit can optimize the evolutionary algorithm by referring to past evolution data. For example, the evolution unit analyzes past evolution data and selects the optimal evolutionary algorithm. The evolution unit can also adjust the evolutionary algorithm based on past evolution data. For example, the evolution unit improves the accuracy of evolution by referring to past evolution data. In this way, by referring to past evolution data, the evolutionary algorithm can be optimized and efficient evolution can be achieved.

[0047] The evolution unit can update the evolution data by reflecting user feedback when evolving the AI. For example, the evolution unit updates the evolution data based on user feedback. The evolution unit can also adjust the evolution algorithm by reflecting user feedback. For example, the evolution unit refers to user feedback to improve the accuracy of evolution. In this way, by reflecting user feedback, the evolution data can be updated and the accuracy of evolution can be improved.

[0048] When evolving the AI, the evolution unit can integrate information from different data sources to expand the evolution data. For example, the evolution unit integrates information from different data sources to expand the evolution data. The evolution unit can also adjust the evolutionary algorithm based on information from different data sources. For example, the evolution unit refers to information from different data sources to improve the accuracy of evolution. In this way, by integrating information from different data sources, the evolution data can be expanded and the accuracy of evolution can be improved.

[0049] When evolving the AI, the evolution unit can weight the evolution data based on the time of submission of the answer. For example, the evolution unit evolves by assigning a higher weight to the most recent answer data. The evolution unit can also evolve by assigning a lower weight to older answer data. For example, the evolution unit evolves by weighting answer data that were submitted recently together. In this way, by weighting the evolution data based on the time of submission of the answer, it is possible to prioritize the evolution of the most recent information.

[0050] The evolution unit can adjust the evolutionary algorithm by reflecting user feedback when evolving the AI. For example, the evolution unit adjusts the evolutionary algorithm based on user feedback. The evolution unit can also improve the accuracy of the evolution by reflecting user feedback. For example, the evolution unit optimizes the evolutionary algorithm by referring to user feedback. In this way, the evolutionary algorithm can be adjusted by reflecting user feedback, and the accuracy of the evolution can be improved.

[0051] When evolving the AI, the evolution unit can integrate information from different data sources to expand the evolution data. For example, the evolution unit integrates information from different data sources to expand the evolution data. The evolution unit can also adjust the evolutionary algorithm based on information from different data sources. For example, the evolution unit refers to information from different data sources to improve the accuracy of evolution. In this way, by integrating information from different data sources, the evolution data can be expanded and the accuracy of evolution can be improved.

[0052] When the chatbot responds, the chatbot unit can provide an optimal response by referring to the user's past dialogue history. For example, the chatbot unit provides related information based on the user's past dialogue history. The chatbot unit can also select an optimal answer by referring to the user's past dialogue history. For example, the chatbot unit analyzes the user's past dialogue history to improve the accuracy of the response. In this way, by referring to the user's past dialogue history, an optimal response can be provided and user satisfaction can be improved.

[0053] When the chatbot responds, the chatbot unit can customize the response content according to the user's current task. For example, the chatbot unit provides information related to the task the user is currently working on. The chatbot unit can also select the optimal response according to the user's current task. For example, the chatbot unit customizes the response content based on the user's task progress. In this way, by customizing the response content according to the user's current task, it is possible to provide the user with the most appropriate information.

[0054] The chatbot unit can improve the response method by reflecting user feedback when the chatbot responds. For example, the chatbot unit improves the response method based on user feedback. The chatbot unit can also improve the accuracy of the response by reflecting user feedback. For example, the chatbot unit adjusts the response algorithm by referring to user feedback. In this way, by reflecting user feedback, the response method can be improved and the accuracy of the response can be improved.

[0055] When the chatbot responds, the chatbot unit can provide an optimal response by taking into account the user's geographical location information. For example, the chatbot unit provides relevant local information based on the user's current location. The chatbot unit can also select an optimal response based on the user's geographical location information. For example, the chatbot unit customizes the response content by referring to the user's geographical location information. This makes it possible to provide highly relevant information by taking into account the user's geographical location information.

[0056] When the chatbot responds, the chatbot unit can analyze the user's social media activity and provide a relevant response. For example, the chatbot unit analyzes the content of the user's social media posts and provides relevant information. The chatbot unit can also select an optimal response based on the user's social media activity history. For example, the chatbot unit provides relevant information by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to efficiently provide relevant information.

[0057] The chatbot unit can customize the response method by reflecting the user's past feedback when the chatbot responds. For example, the chatbot unit selects the optimal response method based on the user's past feedback. The chatbot unit can also customize the response interface by reflecting the user's past feedback. For example, the chatbot unit improves the procedure for accepting questions based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal response method can be selected and user satisfaction can be improved.

[0058] When awarding points, the point department can select the optimal point awarding method by referring to the user's past point history. For example, the point department selects the optimal point awarding method based on the user's past point history. The point department can also improve the accuracy of point awarding by referring to the user's past point history. For example, the point department analyzes the user's past point history and customizes the point awarding method. In this way, by referring to the user's past point history, the optimal point awarding method can be selected and the accuracy of point awarding can be improved.

[0059] When awarding points, the point unit can customize the awarding of points based on the user's current activity status. For example, the point unit selects the optimal point awarding method based on the user's current activity status. The point unit can also improve the accuracy of point awarding according to the user's activity status. For example, the point unit customizes the point awarding method by referring to the user's activity status. In this way, by customizing the awarding of points based on the user's current activity status, it is possible to achieve optimal point awarding for the user.

[0060] The point unit can improve the point awarding method by reflecting user feedback when awarding points. For example, the point unit improves the point awarding method based on user feedback. The point unit can also improve the accuracy of point awarding by reflecting user feedback. For example, the point unit adjusts the point awarding algorithm by referring to user feedback. In this way, by reflecting user feedback, the point awarding method can be improved and the accuracy of point awarding can be improved.

[0061] When awarding points, the point unit can select the optimal awarding method by taking into account the user's geographical location information. The point unit selects the optimal point awarding method, for example, based on the user's current location. The point unit can also improve the accuracy of point awarding based on the user's geographical location information. For example, the point unit customizes the point awarding method by referring to the user's geographical location information. This allows the optimal point awarding method to be selected by taking into account the user's geographical location information, thereby improving the accuracy of point awarding.

[0062] When awarding points, the point department can analyze the user's social media activity and award the points. For example, the point department selects the optimal point awarding method based on the user's social media activity history. The point department can also analyze the content of the user's social media posts to improve the accuracy of point awarding. For example, the point department customizes the point awarding method based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, the optimal point awarding method can be selected and the accuracy of point awarding can be improved.

[0063] When awarding points, the point department can customize the awarding method by reflecting the user's past feedback. For example, the point department selects the optimal point awarding method based on the user's past feedback. The point department can also improve the accuracy of point awarding by reflecting the user's past feedback. For example, the point department improves the point awarding procedure based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal point awarding method can be selected and the accuracy of point awarding can be improved.

[0064] When displaying rankings, the ranking unit can select the optimal display method by referring to the user's past ranking history. For example, the ranking unit selects the optimal display method based on the user's past ranking history. The ranking unit can also improve the accuracy of the ranking display by referring to the user's past ranking history. For example, the ranking unit analyzes the user's past ranking history and customizes the ranking display method. In this way, by referring to the user's past ranking history, the optimal display method can be selected and the accuracy of the ranking display can be improved.

[0065] When displaying the rankings, the ranking unit can customize the display content based on the user's current activity status. For example, the ranking unit selects the optimal ranking display method based on the user's current activity status. The ranking unit can also improve the accuracy of the ranking display according to the user's activity status. For example, the ranking unit customizes the ranking display method by referring to the user's activity status. In this way, by customizing the display content based on the user's current activity status, it is possible to provide the user with optimal ranking information.

[0066] The ranking unit can improve the display method by reflecting user feedback when displaying rankings. For example, the ranking unit improves the ranking display method based on user feedback. The ranking unit can also improve the accuracy of the ranking display by reflecting user feedback. For example, the ranking unit adjusts the ranking display algorithm by referring to user feedback. In this way, by reflecting user feedback, the ranking display method can be improved and the accuracy of the ranking display can be improved.

[0067] When displaying rankings, the ranking unit can select the optimal display method by taking into account the user's geographical location information. The ranking unit selects the optimal ranking display method based on, for example, the user's current location. The ranking unit can also improve the accuracy of the ranking display based on the user's geographical location information. For example, the ranking unit customizes the ranking display method by referring to the user's geographical location information. In this way, by taking into account the user's geographical location information, the optimal ranking display method can be selected and the accuracy of the ranking display can be improved.

[0068] When displaying rankings, the ranking unit can analyze the user's social media activity and display the rankings. The ranking unit selects the optimal ranking display method based on, for example, the user's social media activity history. The ranking unit can also analyze the content of the user's posts on social media to improve the accuracy of the ranking display. For example, the ranking unit customizes the ranking display method by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, the optimal ranking display method can be selected and the accuracy of the ranking display can be improved.

[0069] When displaying rankings, the ranking unit can customize the display method by reflecting the user's past feedback. For example, the ranking unit selects the optimal ranking display method based on the user's past feedback. The ranking unit can also improve the accuracy of the ranking display by reflecting the user's past feedback. For example, the ranking unit improves the ranking display procedure based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal ranking display method can be selected and the accuracy of the ranking display can be 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] When storing answers, the storage unit can evaluate the reliability of the answers and store highly reliable answers preferentially. For example, the storage unit can evaluate reliability based on the answerer's expertise and past answer history. It can also evaluate the reliability of the answer content and the source of the quote and store highly reliable answers preferentially. Furthermore, it can evaluate reliability based on user feedback and store highly reliable answers preferentially. This allows the storage of highly reliable answers preferentially to provide useful information to users.

[0072] The evolutionary unit can adjust the evolutionary algorithm to take into account different cultural backgrounds when evolving the AI. For example, it can learn cultural nuances to provide appropriate responses to users from different cultures. It can also adjust the evolutionary algorithm to accommodate different languages ​​and customs. Furthermore, it can optimize the evolutionary algorithm based on feedback from users with different cultural backgrounds. This allows it to provide appropriate responses to global users by taking different cultural backgrounds into account.

[0073] The reception unit can analyze the user's past question history and automatically suggest related questions. For example, it can suggest new questions related to topics the user has previously asked questions about. It can also automatically select the most suitable answerer from the user's past question history. Furthermore, it can suggest related learning content based on the user's past question history. This makes it possible to efficiently suggest related questions and learning content by analyzing the user's past question history.

[0074] When storing answers, the storage unit can apply different storage algorithms depending on the category of the answer. For example, answers to technical questions can be stored using an algorithm suitable for the technical category. Answers to business-related questions can also be stored using an algorithm suitable for the business category. Furthermore, answers to general questions can be stored using an algorithm suitable for the general category. This allows for efficient storage of answers by applying different storage algorithms depending on the category of the answer.

[0075] When evolving an AI, the evolution unit can integrate information from different data sources to expand the evolution data. For example, information from different data sources can be integrated to expand the evolution data. The evolution unit can also adjust the evolutionary algorithm based on information from different data sources. Furthermore, the accuracy of the evolution can be improved by referring to information from different data sources. In this way, by integrating information from different data sources, the evolution data can be expanded and the accuracy of the evolution can be improved.

[0076] When the chatbot responds, the chatbot unit can customize the response content according to the user's current task. For example, it can provide information related to the task the user is currently working on. It can also select the optimal response according to the user's current task. Furthermore, it can customize the response content based on the user's task progress. This allows it to provide the user with the most appropriate information by customizing the response content according to the user's current task.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives a question. Questions can be in text, audio, or image format. For example, a user may input a question in text format, or audio questions may be received using voice recognition technology, or image questions may be received using image recognition technology. For example, an image taken by a user with a smartphone camera may be analyzed and accepted as a question. Step 2: In the storage unit, experts respond to the questions received by the reception unit and the responses are stored. For example, the responses are stored in a database. Points can also be awarded based on the quality and frequency of the responses. For example, points can be awarded based on the accuracy and detail of the answers. Step 3: The evolution unit evolves the AI ​​based on the answers accumulated by the accumulation unit. For example, the AI ​​can be evolved using a machine learning algorithm. The accuracy of the AI ​​can also be improved by expanding the dataset. For example, new answer data can be added to increase the AI's learning data. Step 4: The chatbot section can use the AI ​​evolved by the evolution section as a chatbot. For example, it can automatically provide answers to user questions. It can also provide optimal responses by referring to the user's past dialogue history. For example, it can provide related information based on the content of the user's past questions. Step 5: The points section awards points based on the answers provided by the chatbot section. For example, points may be awarded based on the quality and frequency of answers. Points may also be awarded based on the user's activity history. For example, points may be awarded based on the number of questions and answers the user has asked within a certain period of time. Step 6: The ranking unit displays rankings based on the points awarded by the points unit. For example, the rankings are displayed based on the accumulated points. The rankings can also be displayed based on the user's activity history. For example, the rankings are displayed based on the number of points or badges the user has earned in the past.

[0079] (Example 2) A high-performance chatbot system according to an embodiment of the present invention accepts questions, has experts respond, accumulates the answers, evolves the AI, becomes usable as a chatbot, awards points, and displays rankings. The high-performance chatbot system accepts questions, has experts respond, accumulates the answers, evolves the AI, and becomes usable as a chatbot. Points are awarded for answers and knowledge acquired, and rankings are displayed. For example, in a high-performance chatbot system, a user submits a question. The question is then input into the AI. An expert then answers the question. This answer is accumulated in the AI, and the AI ​​evolves based on this information. For example, if a new project participant asks, "What is the purpose of this project?", the expert responds, "The purpose of this project is ____." This answer is accumulated in the AI, and the AI ​​can automatically provide an answer the next time a similar question is submitted. Furthermore, experts can earn points by answering questions. For example, 10 points are awarded for answering one question. In addition, questioners, such as new employees, project participants, and service users, can earn points by acquiring knowledge. For example, if a questioner checks the AI's answer and understands its content, they are awarded 5 points. These points are reflected in the ranking function, allowing them to compete with others. For example, top ranking participants are awarded badges and rewards. When a certain number of points are reached, their name appears in the ranking, encouraging competition with other participants. This allows knowledge to be acquired in a fun way. As a result, a high-performance chatbot system can consistently handle everything from accepting questions to accumulating answers, evolving the AI, using the chatbot, awarding points, and displaying the ranking. For example, new employees, project participants, and service users can enjoy acquiring knowledge while competing with others. Experts can also earn points by sharing their knowledge and aim to rank higher. This improves the overall knowledge level and realizes efficient information sharing.

[0080] A high-performance chatbot system according to an embodiment includes a reception unit, a storage unit, an evolution unit, a chatbot unit, a point unit, and a ranking unit. The reception unit receives questions. The questions may be in text, audio, or image format, but are not limited to these examples. For example, a user inputs a question in text format into the reception unit. The reception unit can also receive audio questions using voice recognition technology. The reception unit can also receive image questions using image recognition technology. For example, the reception unit analyzes images taken by a user with a smartphone camera and receives the images as questions. The storage unit allows experts to respond to the questions received by the reception unit and stores the responses. The storage unit stores the responses in a database, for example. The storage unit can also award points based on the quality and frequency of answers. For example, the storage unit awards points based on the accuracy and detail of the answers. The evolution unit evolves an AI based on the answers accumulated by the storage unit. The evolution unit evolves the AI ​​using, for example, a machine learning algorithm. The evolution unit can also improve the accuracy of the AI ​​by expanding the dataset. For example, the evolution unit adds new answer data to increase the AI's learning data. The chatbot unit makes the AI ​​evolved by the evolution unit available as a chatbot. The chatbot unit, for example, automatically provides answers to user questions. The chatbot unit can also provide optimal responses by referring to the user's past dialogue history. For example, the chatbot unit provides related information based on the user's past questions. The point unit awards points based on the answers provided by the chatbot unit. For example, the point unit awards points based on the quality and frequency of answers. The point unit can also award points based on the user's activity history. For example, the point unit awards points based on the number of questions and answers the user has made within a certain period of time. The ranking unit displays a ranking based on the points awarded by the point unit. For example, the ranking unit displays a ranking based on the accumulated points. The ranking unit can also display a ranking based on the user's activity history.For example, the ranking unit displays the ranking based on the number of points or badges that users have earned in the past. This allows the high-performance chatbot system according to the embodiment to consistently perform processes from accepting questions to accumulating answers, evolving AI, using chatbots, awarding points, and displaying rankings.

[0081] The point section can award points for answers or acquisition of knowledge. For example, the point section awards points when a user answers a question. The point section can also award points when a user acquires knowledge. For example, the point section awards points when a user checks an AI answer and understands its content. The point section can also award points when a user completes specific learning content. For example, the point section awards points when a user completes an online course. In this way, by awarding points for answers or acquisition of knowledge, it is possible to increase user motivation.

[0082] The ranking unit may include a badge unit that awards badges to users who reach a predetermined number of points. For example, the badge unit awards badges when a user reaches a certain number of points. The badge unit may also award badges based on the user's activity history. For example, the badge unit may award badges when a user provides an excellent answer to a specific question. The badge unit may also award badges when a user answers many questions within a specific period of time. For example, the badge unit may award badges when a user answers 10 or more questions within one week. In this way, awarding badges to users who reach a certain number of points can enhance the user's sense of accomplishment.

[0083] The ranking unit may include a reward unit that provides a reward to a user who reaches a predetermined number of points. For example, the reward unit provides a reward when a user reaches a certain number of points. The reward unit may also provide a reward based on the user's activity history. For example, the reward unit may provide a reward when a user provides an excellent answer to a specific question. The reward unit may also provide a reward when a user answers many questions within a specific period of time. For example, the reward unit may provide a reward when a user answers 10 or more questions within one week. In this way, by providing a reward to a user who reaches a certain number of points, it is possible to further increase the user's motivation.

[0084] The reception unit may include an acquisition unit that allows the questioner to acquire knowledge. The acquisition unit may, for example, provide an online course that allows the questioner to acquire knowledge. The acquisition unit may also hold a webinar that allows the questioner to acquire knowledge. For example, the acquisition unit may periodically hold a webinar on a specific topic. The acquisition unit may also provide materials that allow the questioner to acquire knowledge. For example, the acquisition unit may make materials on a specific topic available for download. Thus, by providing an acquisition unit that allows the questioner to acquire knowledge, the learning effect of the user can be improved.

[0085] The reception unit can analyze the user's emotions and adjust the timing of question reception based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit temporarily delays the reception of questions to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit can promptly accept questions to promote smooth dialogue. For example, if the reception unit determines that the user is relaxed, it can promptly accept questions. Furthermore, if the user is in a hurry, the reception unit can promptly accept questions and provide immediate answers. For example, if the reception unit determines that the user is in a hurry, it can prioritize urgent questions. This allows for adjusting the timing of question reception according to the user's emotions, thereby reducing the user's stress and promoting smooth dialogue. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When accepting a question, the acceptance unit can analyze the user's past question history and select an appropriate acceptance method. For example, the acceptance unit prioritizes accepting topics that the user has frequently asked questions about in the past. The acceptance unit can also automatically select the most appropriate answerer from the user's past question history. For example, the acceptance unit automatically suggests related questions based on the user's past question history. The acceptance unit can also automatically suggest related questions based on the user's past question history. For example, the acceptance unit analyzes the user's past question history and suggests related questions. In this way, by analyzing the user's past question history, the most appropriate acceptance method can be selected, enabling efficient question acceptance.

[0087] When receiving a question, the reception unit can filter the questions based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives questions related to a project in which the user is currently involved. The reception unit can also filter and receive related questions based on the user's area of ​​interest. For example, the reception unit suggests appropriate questions depending on the progress of the user's project. The reception unit can also filter and receive related questions based on the user's area of ​​interest. For example, the reception unit suggests related questions based on the user's area of ​​interest. In this way, by filtering questions based on the user's current project or area of ​​interest, it is possible to preferentially receive highly relevant questions.

[0088] When accepting a question, the acceptance unit can select an appropriate acceptance means according to the user's input method. For example, if the user selects voice input, the acceptance unit accepts the question using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the question using text analysis technology. For example, if the user selects image input, the acceptance unit accepts the question using image recognition technology. Furthermore, the acceptance unit can also select the optimal acceptance means according to the user's input method. For example, if the user selects voice input, the acceptance unit accepts the question using voice recognition technology. This allows for selecting the optimal acceptance means according to the user's input method, thereby improving user convenience.

[0089] The reception unit can analyze the user's emotions and determine the priority of questions to be received based on the analyzed user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving questions with a high level of importance. Furthermore, when the user is relaxed, the reception unit can also receive questions with a normal priority. For example, when the user is in a hurry, the reception unit prioritizes receiving questions with a high level of urgency. In this way, by determining the priority of questions according to the user's emotions, it is possible to prioritize receiving questions with a high level of importance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] When accepting questions, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting questions related to that area. The acceptance unit can also provide relevant local information based on the user's current location. For example, the acceptance unit selects the most appropriate answerer based on the user's geographical location information. This allows highly relevant questions to be prioritized by taking into account the user's geographical location information.

[0091] When accepting a question, the acceptance unit can analyze the user's social media activity and accept related questions. For example, the acceptance unit can analyze the content of the user's social media posts and suggest related questions. The acceptance unit can also accept optimal questions based on the user's social media activity history. For example, the acceptance unit can suggest related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related questions can be accepted efficiently.

[0092] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit selects the optimal reception method, for example, based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. For example, the reception unit improves the question reception procedure based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal reception method can be selected and user satisfaction can be improved.

[0093] The storage unit can estimate the user's emotions and adjust the method of storing answers based on the estimated user emotions. For example, when the user is relaxed, the storage unit stores detailed answers. Furthermore, when the user is in a hurry, the storage unit can store concise answers. For example, when the user is feeling stressed, the storage unit prioritizes storing answers of high importance. In this way, by adjusting the method of storing answers according to the user's emotions, it is possible to store the most appropriate answers for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] When storing answers, the storage unit can adjust the level of detail of the storage based on the importance of the answer. For example, the storage unit stores answers with high importance in detail and stores answers with low importance in brief. The storage unit can also adjust the range of information to be stored depending on the importance of the answer. For example, the storage unit collects and stores information on answers with high importance from multiple data sources. This allows for efficient storage of answers by adjusting the level of detail of the storage based on the importance of the answer.

[0095] When storing answers, the storage unit can apply different storage algorithms depending on the category of the answer. For example, the storage unit stores answers to technical questions using an algorithm suitable for the technical category. The storage unit can also store answers to business-related questions using an algorithm suitable for the business category. For example, the storage unit stores answers to general questions using an algorithm suitable for the general category. This allows for efficient storage of answers by applying different storage algorithms depending on the category of the answer.

[0096] When storing answers, the storage unit can improve the accuracy of storage by referring to the user's past answer results. The storage unit, for example, analyzes the user's past answer results to improve the accuracy of storage. The storage unit can also select the optimal storage method based on the user's past answer results. For example, the storage unit adjusts the storage algorithm by referring to the user's past answer results. In this way, the accuracy of storage can be improved by referring to the user's past answer results.

[0097] The storage unit can estimate the user's emotions and determine the priority of answers to be stored based on the estimated user emotions. For example, when the user is feeling stressed, the storage unit prioritizes storing answers with high importance. Furthermore, when the user is relaxed, the storage unit can also store answers with normal priority. For example, when the user is in a hurry, the storage unit prioritizes storing answers with high urgency. In this way, by determining the priority of answers according to the user's emotions, answers with high importance can be stored with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] When storing answers, the storage unit can determine the storage priority based on the time of submission of the answers. For example, the storage unit stores the most recent answers with priority, and older answers are stored later. The storage unit can also adjust the storage order based on the time of submission of the answers. For example, the storage unit stores answers that were submitted close together. In this way, by determining the storage priority based on the time of submission of the answers, the most recent information can be stored with priority.

[0099] When storing answers, the storage unit can adjust the order of storage based on the relevance of the answers. For example, the storage unit stores highly relevant answers with priority, and stores less relevant answers later. The storage unit can also adjust the order of storage based on the relevance of the answers. For example, the storage unit stores highly relevant answers together. This allows for efficient storage of answers by adjusting the order of storage based on the relevance of the answers.

[0100] When storing answers, the storage unit can adjust the use of technical terms in the stored answers according to the user's level of expertise. For example, the storage unit stores answers that use a lot of technical terms for users with a high level of expertise. The storage unit can also store concise, easy-to-understand answers for users with a low level of expertise. For example, the storage unit adjusts the use of technical terms in the answers to be stored according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, answers that are easy for users to understand can be stored.

[0101] The evolution unit can estimate the user's emotions and adjust the AI's evolution method based on the estimated user's emotions. For example, if the user is relaxed, the evolution unit applies an algorithm that evolves at a leisurely pace. Alternatively, if the user is in a hurry, the evolution unit can apply an algorithm that evolves quickly. For example, if the user is stressed, the evolution unit prioritizes the evolution of information of high importance. This allows the AI's evolution method to be adjusted according to the user's emotions, thereby achieving optimal evolution for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When evolving an AI, the evolution unit can optimize the evolutionary algorithm by referring to past evolution data. For example, the evolution unit analyzes past evolution data and selects the optimal evolutionary algorithm. The evolution unit can also adjust the evolutionary algorithm based on past evolution data. For example, the evolution unit improves the accuracy of evolution by referring to past evolution data. In this way, by referring to past evolution data, the evolutionary algorithm can be optimized and efficient evolution can be achieved.

[0103] The evolution unit can update the evolution data by reflecting user feedback when evolving the AI. For example, the evolution unit updates the evolution data based on user feedback. The evolution unit can also adjust the evolution algorithm by reflecting user feedback. For example, the evolution unit refers to user feedback to improve the accuracy of evolution. In this way, by reflecting user feedback, the evolution data can be updated and the accuracy of evolution can be improved.

[0104] When evolving the AI, the evolution unit can integrate information from different data sources to expand the evolution data. For example, the evolution unit integrates information from different data sources to expand the evolution data. The evolution unit can also adjust the evolutionary algorithm based on information from different data sources. For example, the evolution unit refers to information from different data sources to improve the accuracy of evolution. In this way, by integrating information from different data sources, the evolution data can be expanded and the accuracy of evolution can be improved.

[0105] The evolution unit can estimate the user's emotions and adjust the frequency of evolution based on the estimated user emotions. For example, if the user is relaxed, the evolution unit performs evolution at a normal frequency. Furthermore, if the user is in a hurry, the evolution unit can increase the frequency of evolution to respond quickly. For example, if the user is feeling stressed, the evolution unit adjusts the frequency of evolution to prioritize the evolution of important information. In this way, by adjusting the frequency of evolution according to the user's emotions, optimal evolution for the user can be achieved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] When evolving the AI, the evolution unit can weight the evolution data based on the time of submission of the answer. For example, the evolution unit evolves by assigning a higher weight to the most recent answer data. The evolution unit can also evolve by assigning a lower weight to older answer data. For example, the evolution unit evolves by weighting answer data that were submitted recently together. In this way, by weighting the evolution data based on the time of submission of the answer, it is possible to prioritize the evolution of the most recent information.

[0107] The evolution unit can adjust the evolutionary algorithm by reflecting user feedback when evolving the AI. For example, the evolution unit adjusts the evolutionary algorithm based on user feedback. The evolution unit can also improve the accuracy of the evolution by reflecting user feedback. For example, the evolution unit optimizes the evolutionary algorithm by referring to user feedback. In this way, the evolutionary algorithm can be adjusted by reflecting user feedback, and the accuracy of the evolution can be improved.

[0108] When evolving the AI, the evolution unit can integrate information from different data sources to expand the evolution data. For example, the evolution unit integrates information from different data sources to expand the evolution data. The evolution unit can also adjust the evolutionary algorithm based on information from different data sources. For example, the evolution unit refers to information from different data sources to improve the accuracy of evolution. In this way, by integrating information from different data sources, the evolution data can be expanded and the accuracy of evolution can be improved.

[0109] The chatbot unit can estimate the user's emotions and adjust the chatbot's response method based on the estimated user emotions. For example, if the user is relaxed, the chatbot unit provides a detailed response. The chatbot unit can also provide a concise response if the user is in a hurry. For example, if the user is feeling stressed, the chatbot unit prioritizes responding with information of high importance. This allows the chatbot's response method to be adjusted according to the user's emotions, thereby providing the most appropriate response for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] When the chatbot responds, the chatbot unit can provide an optimal response by referring to the user's past dialogue history. For example, the chatbot unit provides related information based on the user's past dialogue history. The chatbot unit can also select an optimal answer by referring to the user's past dialogue history. For example, the chatbot unit analyzes the user's past dialogue history to improve the accuracy of the response. In this way, by referring to the user's past dialogue history, an optimal response can be provided and user satisfaction can be improved.

[0111] When the chatbot responds, the chatbot unit can customize the response content according to the user's current task. For example, the chatbot unit provides information related to the task the user is currently working on. The chatbot unit can also select the optimal response according to the user's current task. For example, the chatbot unit customizes the response content based on the user's task progress. In this way, by customizing the response content according to the user's current task, it is possible to provide the user with the most appropriate information.

[0112] The chatbot unit can improve the response method by reflecting user feedback when the chatbot responds. For example, the chatbot unit improves the response method based on user feedback. The chatbot unit can also improve the accuracy of the response by reflecting user feedback. For example, the chatbot unit adjusts the response algorithm by referring to user feedback. In this way, by reflecting user feedback, the response method can be improved and the accuracy of the response can be improved.

[0113] The chatbot unit can estimate the user's emotions and determine the priority of the chatbot's responses based on the estimated user emotions. For example, if the user is feeling stressed, the chatbot unit can provide responses with a higher priority. Also, if the user is relaxed, the chatbot unit can provide responses with a normal priority. For example, if the user is in a hurry, the chatbot unit can provide responses with a higher priority. In this way, by determining the priority of responses according to the user's emotions, it is possible to provide responses with a higher priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] When the chatbot responds, the chatbot unit can provide an optimal response by taking into account the user's geographical location information. For example, the chatbot unit provides relevant local information based on the user's current location. The chatbot unit can also select an optimal response based on the user's geographical location information. For example, the chatbot unit customizes the response content by referring to the user's geographical location information. This makes it possible to provide highly relevant information by taking into account the user's geographical location information.

[0115] When the chatbot responds, the chatbot unit can analyze the user's social media activity and provide a relevant response. For example, the chatbot unit analyzes the content of the user's social media posts and provides relevant information. The chatbot unit can also select an optimal response based on the user's social media activity history. For example, the chatbot unit provides relevant information by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to efficiently provide relevant information.

[0116] The chatbot unit can customize the response method by reflecting the user's past feedback when the chatbot responds. For example, the chatbot unit selects the optimal response method based on the user's past feedback. The chatbot unit can also customize the response interface by reflecting the user's past feedback. For example, the chatbot unit improves the procedure for accepting questions based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal response method can be selected and user satisfaction can be improved.

[0117] The point unit can estimate the user's emotions and adjust the point awarding method based on the estimated user's emotions. For example, if the user is relaxed, the point unit applies a normal point awarding method. Furthermore, if the user is in a hurry, the point unit can also award points quickly. For example, if the user is feeling stressed, the point unit prioritizes awarding points to activities with high importance. This allows the point awarding method to be adjusted according to the user's emotions, thereby achieving optimal point awarding for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0118] When awarding points, the point department can select the optimal point awarding method by referring to the user's past point history. For example, the point department selects the optimal point awarding method based on the user's past point history. The point department can also improve the accuracy of point awarding by referring to the user's past point history. For example, the point department analyzes the user's past point history and customizes the point awarding method. In this way, by referring to the user's past point history, the optimal point awarding method can be selected and the accuracy of point awarding can be improved.

[0119] When awarding points, the point unit can customize the awarding of points based on the user's current activity status. For example, the point unit selects the optimal point awarding method based on the user's current activity status. The point unit can also improve the accuracy of point awarding according to the user's activity status. For example, the point unit customizes the point awarding method by referring to the user's activity status. In this way, by customizing the awarding of points based on the user's current activity status, it is possible to achieve optimal point awarding for the user.

[0120] The point unit can improve the point awarding method by reflecting user feedback when awarding points. For example, the point unit improves the point awarding method based on user feedback. The point unit can also improve the accuracy of point awarding by reflecting user feedback. For example, the point unit adjusts the point awarding algorithm by referring to user feedback. In this way, by reflecting user feedback, the point awarding method can be improved and the accuracy of point awarding can be improved.

[0121] The point unit can estimate the user's emotions and determine the priority of points based on the estimated user's emotions. For example, if the user is feeling stressed, the point unit can prioritize points for activities with high importance. Furthermore, if the user is relaxed, the point unit can also assign points with normal priority. For example, if the user is in a hurry, the point unit can quickly assign points. In this way, by determining the priority of points according to the user's emotions, points can be prioritized for activities with high importance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0122] When awarding points, the point unit can select the optimal awarding method by taking into account the user's geographical location information. The point unit selects the optimal point awarding method, for example, based on the user's current location. The point unit can also improve the accuracy of point awarding based on the user's geographical location information. For example, the point unit customizes the point awarding method by referring to the user's geographical location information. This allows the optimal point awarding method to be selected by taking into account the user's geographical location information, thereby improving the accuracy of point awarding.

[0123] When awarding points, the point department can analyze the user's social media activity and award the points. For example, the point department selects the optimal point awarding method based on the user's social media activity history. The point department can also analyze the content of the user's social media posts to improve the accuracy of point awarding. For example, the point department customizes the point awarding method based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, the optimal point awarding method can be selected and the accuracy of point awarding can be improved.

[0124] When awarding points, the point department can customize the awarding method by reflecting the user's past feedback. For example, the point department selects the optimal point awarding method based on the user's past feedback. The point department can also improve the accuracy of point awarding by reflecting the user's past feedback. For example, the point department improves the point awarding procedure based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal point awarding method can be selected and the accuracy of point awarding can be improved.

[0125] The ranking unit can estimate the user's emotions and adjust the way the rankings are displayed based on the estimated user emotions. For example, when the user is relaxed, the ranking unit displays detailed ranking information. Furthermore, when the user is in a hurry, the ranking unit can display concise ranking information. For example, when the user is feeling stressed, the ranking unit prioritizes displaying ranking information of high importance. This allows the ranking display method to be adjusted according to the user's emotions, thereby providing optimal ranking information for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0126] When displaying rankings, the ranking unit can select the optimal display method by referring to the user's past ranking history. For example, the ranking unit selects the optimal display method based on the user's past ranking history. The ranking unit can also improve the accuracy of the ranking display by referring to the user's past ranking history. For example, the ranking unit analyzes the user's past ranking history and customizes the ranking display method. In this way, by referring to the user's past ranking history, the optimal display method can be selected and the accuracy of the ranking display can be improved.

[0127] When displaying the rankings, the ranking unit can customize the display content based on the user's current activity status. For example, the ranking unit selects the optimal ranking display method based on the user's current activity status. The ranking unit can also improve the accuracy of the ranking display according to the user's activity status. For example, the ranking unit customizes the ranking display method by referring to the user's activity status. In this way, by customizing the display content based on the user's current activity status, it is possible to provide the user with optimal ranking information.

[0128] The ranking unit can improve the display method by reflecting user feedback when displaying rankings. For example, the ranking unit improves the ranking display method based on user feedback. The ranking unit can also improve the accuracy of the ranking display by reflecting user feedback. For example, the ranking unit adjusts the ranking display algorithm by referring to user feedback. In this way, by reflecting user feedback, the ranking display method can be improved and the accuracy of the ranking display can be improved.

[0129] The ranking unit can estimate the user's emotions and determine the ranking priority based on the estimated user's emotions. For example, when the user is feeling stressed, the ranking unit can prioritize displaying ranking information with high importance. Furthermore, when the user is relaxed, the ranking unit can also display ranking information with normal priority. For example, when the user is in a hurry, the ranking unit can quickly display ranking information. In this way, by determining the ranking priority according to the user's emotions, it is possible to prioritize displaying ranking information with high importance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0130] When displaying rankings, the ranking unit can select the optimal display method by taking into account the user's geographical location information. The ranking unit selects the optimal ranking display method based on, for example, the user's current location. The ranking unit can also improve the accuracy of the ranking display based on the user's geographical location information. For example, the ranking unit customizes the ranking display method by referring to the user's geographical location information. In this way, by taking into account the user's geographical location information, the optimal ranking display method can be selected and the accuracy of the ranking display can be improved.

[0131] When displaying rankings, the ranking unit can analyze the user's social media activity and display the rankings. The ranking unit selects the optimal ranking display method based on, for example, the user's social media activity history. The ranking unit can also analyze the content of the user's posts on social media to improve the accuracy of the ranking display. For example, the ranking unit customizes the ranking display method by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, the optimal ranking display method can be selected and the accuracy of the ranking display can be improved.

[0132] When displaying rankings, the ranking unit can customize the display method by reflecting the user's past feedback. For example, the ranking unit selects the optimal ranking display method based on the user's past feedback. The ranking unit can also improve the accuracy of the ranking display by reflecting the user's past feedback. For example, the ranking unit improves the ranking display procedure based on the user's feedback. In this way, by reflecting the user's past feedback, the optimal ranking display method can be selected and the accuracy of the ranking display can be improved. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, accumulation unit, evolution unit, chatbot unit, point unit, and ranking unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the accumulation unit is realized by the specific processing unit 290 of the data processing device 12 and stores answers in the database 24. For example, the evolution unit is realized by the specific processing unit 290 of the data processing device 12 and evolves the AI ​​using a machine learning algorithm. For example, the chatbot unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the point unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the ranking unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, accumulation unit, evolution unit, chatbot unit, point unit, and ranking unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the accumulation unit is realized by the specific processing unit 290 of the data processing device 12 and stores answers in the database 24. For example, the evolution unit is realized by the specific processing unit 290 of the data processing device 12 and evolves the AI ​​using a machine learning algorithm. For example, the chatbot unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the point unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the ranking unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, accumulation unit, evolution unit, chatbot unit, point unit, and ranking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the accumulation unit is realized by the specific processing unit 290 of the data processing device 12 and stores answers in the database 24. For example, the evolution unit is realized by the specific processing unit 290 of the data processing device 12 and evolves the AI ​​using a machine learning algorithm. For example, the chatbot unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the point unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the ranking unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, accumulation unit, evolution unit, chatbot unit, point unit, and ranking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the accumulation unit is realized by the specific processing unit 290 of the data processing device 12 and stores answers in the database 24. For example, the evolution unit is realized by the specific processing unit 290 of the data processing device 12 and evolves the AI ​​using a machine learning algorithm. For example, the chatbot unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the point unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the ranking unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The reception unit can monitor the user's current health condition and adjust the method of receiving questions based on the health condition. For example, if the user is tired, a simple question format can be provided to reduce the user's burden. Alternatively, if the user is healthy, a detailed question format can be provided to collect more in-depth information. Furthermore, if the user is feeling stressed, a relaxing question format can be provided to reduce the user's stress. In this way, by adjusting the method of receiving questions according to the user's health condition, the burden on the user can be reduced and efficient information collection can be achieved.

[0135] When storing answers, the storage unit can evaluate the reliability of the answers and store highly reliable answers preferentially. For example, the storage unit can evaluate reliability based on the answerer's expertise and past answer history. It can also evaluate the reliability of the answer content and the source of the quote and store highly reliable answers preferentially. Furthermore, it can evaluate reliability based on user feedback and store highly reliable answers preferentially. This allows the storage of highly reliable answers preferentially to provide useful information to users.

[0136] The evolutionary unit can adjust the evolutionary algorithm to take into account different cultural backgrounds when evolving the AI. For example, it can learn cultural nuances to provide appropriate responses to users from different cultures. It can also adjust the evolutionary algorithm to accommodate different languages ​​and customs. Furthermore, it can optimize the evolutionary algorithm based on feedback from users with different cultural backgrounds. This allows it to provide appropriate responses to global users by taking different cultural backgrounds into account.

[0137] The chatbot unit can estimate the user's emotions and adjust the tone of the response based on the estimated user's emotions. For example, if the user is sad, the chatbot unit can respond in a gentle tone to comfort the user. If the user is excited, the chatbot unit can respond in a cheerful tone to share the user's excitement. Furthermore, if the user is angry, the chatbot unit can respond in a calm tone to calm the user's anger. In this way, by adjusting the tone of the response according to the user's emotions, the chatbot unit can provide the optimal response for the user.

[0138] The point unit can estimate the user's emotions and adjust the timing of point awarding based on the estimated user emotions. For example, if the user is losing motivation, points can be awarded immediately to restore the user's motivation. Also, if the user is highly motivated, the point awarding can be delayed to encourage continued effort. Furthermore, if the user is feeling stressed, points can be awarded earlier to reduce the user's stress. In this way, adjusting the timing of point awarding according to the user's emotions can maintain the user's motivation and promote efficient learning.

[0139] The ranking unit can estimate the user's emotions and customize the way the rankings are displayed based on the estimated user's emotions. For example, if the user is competitive, detailed ranking information can be displayed to encourage competition with other users. If the user is relaxed, concise ranking information can be displayed to reduce the user's burden. Furthermore, if the user is stressed, ranking information can be hidden to reduce the user's stress. In this way, by customizing the way the rankings are displayed according to the user's emotions, it is possible to provide the user with the most suitable ranking information.

[0140] The reception unit can analyze the user's past question history and automatically suggest related questions. For example, it can suggest new questions related to topics the user has previously asked questions about. It can also automatically select the most suitable answerer from the user's past question history. Furthermore, it can suggest related learning content based on the user's past question history. This makes it possible to efficiently suggest related questions and learning content by analyzing the user's past question history.

[0141] When storing answers, the storage unit can apply different storage algorithms depending on the category of the answer. For example, answers to technical questions can be stored using an algorithm suitable for the technical category. Answers to business-related questions can also be stored using an algorithm suitable for the business category. Furthermore, answers to general questions can be stored using an algorithm suitable for the general category. This allows for efficient storage of answers by applying different storage algorithms depending on the category of the answer.

[0142] When evolving an AI, the evolution unit can integrate information from different data sources to expand the evolution data. For example, information from different data sources can be integrated to expand the evolution data. The evolution unit can also adjust the evolutionary algorithm based on information from different data sources. Furthermore, the accuracy of the evolution can be improved by referring to information from different data sources. In this way, by integrating information from different data sources, the evolution data can be expanded and the accuracy of the evolution can be improved.

[0143] When the chatbot responds, the chatbot unit can customize the response content according to the user's current task. For example, it can provide information related to the task the user is currently working on. It can also select the optimal response according to the user's current task. Furthermore, it can customize the response content based on the user's task progress. This allows it to provide the user with the most appropriate information by customizing the response content according to the user's current task.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The reception unit receives a question. Questions can be in text, audio, or image format. For example, a user may input a question in text format, or audio questions may be received using voice recognition technology, or image questions may be received using image recognition technology. For example, an image taken by a user with a smartphone camera may be analyzed and accepted as a question. Step 2: In the storage unit, experts respond to the questions received by the reception unit and the responses are stored. For example, the responses are stored in a database. Points can also be awarded based on the quality and frequency of the responses. For example, points can be awarded based on the accuracy and detail of the answers. Step 3: The evolution unit evolves the AI ​​based on the answers accumulated by the accumulation unit. For example, the AI ​​can be evolved using a machine learning algorithm. The accuracy of the AI ​​can also be improved by expanding the dataset. For example, new answer data can be added to increase the AI's learning data. Step 4: The chatbot section can use the AI ​​evolved by the evolution section as a chatbot. For example, it can automatically provide answers to user questions. It can also provide optimal responses by referring to the user's past dialogue history. For example, it can provide related information based on the content of the user's past questions. Step 5: The points section awards points based on the answers provided by the chatbot section. For example, points may be awarded based on the quality and frequency of answers. Points may also be awarded based on the user's activity history. For example, points may be awarded based on the number of questions and answers the user has asked within a certain period of time. Step 6: The ranking unit displays rankings based on the points awarded by the points unit. For example, the rankings are displayed based on the accumulated points. The rankings can also be displayed based on the user's activity history. For example, the rankings are displayed based on the number of points or badges the user has earned in the past.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Explanation of symbols]

[0218] 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 reception section for accepting questions; a storage unit for storing answers from experts to the questions received by the reception unit; an evolution unit that evolves AI based on the answers accumulated by the accumulation unit; a chatbot unit that allows the AI ​​evolved by the evolution unit to be used as a chatbot; a point unit that awards points based on the answers provided by the chatbot unit; a ranking unit that displays a ranking based on the points awarded by the point unit; Equipped with A system characterized by:

2. The point portion is Award points for answers or knowledge acquisition 2. The system of claim 1.

3. The ranking unit A badge section is provided that awards badges to users who reach a predetermined point.

2. The system of claim 1.

4. The ranking unit A reward unit is provided to provide rewards to users who reach a predetermined point.

2. The system of claim 1.

5. The reception unit Provides a learning area for the questioner to gain knowledge 2. The system of claim 1.

6. The reception unit Analyze user emotions and adjust the timing of question acceptance based on the analyzed user emotions.

2. The system of claim 1.

7. The reception unit When accepting a question, analyze the user's past question history and select the appropriate method of acceptance.

2. The system of claim 1.

8. The reception unit Filter questions based on your current project or area of ​​interest 2. The system of claim 1.

9. The reception unit When accepting a question, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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