system

Abstract

The system according to this embodiment aims to efficiently collect the latest and necessary information related to a specific industry or specific business, and to update the virtual expert AI. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an update unit, a reception unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit. The reception unit receives questions from users. The provision unit provides answers to the questions received by the reception unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently collect the latest and necessary information related to a specific industry or specific business and obtain the knowledge of experts.

[0005] The system according to the embodiment aims to efficiently collect the latest and necessary information related to a specific industry or specific business and update the virtual expert AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an update unit, a reception unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit. The reception unit receives questions from users. The provision unit provides answers to the questions received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect the latest and necessary information related to a specific industry or specific business, and update the virtual expert AI. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The virtual expert AI system according to an embodiment of the present invention is a system that, in the consideration of new businesses, creates a virtual expert AI that aggregates information held by people engaged in specific industries or specific tasks, or potential target personas, and allows for quick and easy interviews with the virtual expert AI to gather industry information, specific tasks, challenges, and thoughts. This virtual expert AI system operates a point-earning service in the background for information gathering, and the AI ​​agent autonomously conducts surveys and interviews with target personas, ensuring that the virtual expert AI always has the necessary and up-to-date information. For example, when planning a service to streamline the work of building cleaners, the virtual expert AI system can gain realistic knowledge and insights by interviewing the virtual expert AI about the work and challenges of building cleaners. Similarly, when planning a car navigation map service specifically for taxi drivers, the system can grasp specific needs by interviewing the virtual expert AI about the work and challenges of taxi drivers. This mechanism solves the problem in new business planning where a lack of knowledge and insights about target users or specific industries can hinder the progress of the project. Even when searching the internet doesn't yield the desired information, or when there's no connection to target users or industry professionals for interviews, information gathering and interviews can be easily conducted through the virtual expert AI. Furthermore, the virtual expert AI is constantly updated to hold the latest information, enabling real-time information provision. This allows for rapid progress in considering new businesses and accelerates business creation. In this way, the virtual expert AI system can aggregate information from people engaged in specific industries or tasks, as well as potential target personas, allowing for quick and easy interviews about industry information, specific tasks, challenges, and thoughts during new business considerations.

[0029] The virtual expert AI system according to this embodiment comprises a collection unit, an analysis unit, an update unit, a reception unit, and a provision unit. The collection unit collects information. The collection unit collects information such as text data, audio data, and image data. The collection unit includes a questionnaire creation unit and can create questionnaires and send them to target individuals. The collection unit includes an interview implementation unit and can coordinate with target personas for interviews and conduct interviews. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the collected information and generates data to be reflected in the virtual expert AI. The update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit. The update unit updates the virtual expert AI using methods such as updating the database and retraining the algorithms. The reception unit receives questions from users. The reception unit accepts questions in text format, audio format, and questions on specific topics. The provision unit provides answers to questions received by the reception unit. The providing unit provides answers in various ways, such as text format, audio format, and by specifying the accuracy and reliability of the answers. This enables the virtual expert AI system according to the embodiment to efficiently collect, analyze, update, receive questions, and provide answers.

[0030] The data collection unit collects information. For example, it collects text data, audio data, and image data. Specifically, the data collection unit can collect text data from publicly available databases on the internet, social media, news sites, etc. Audio data is collected from podcasts, audio interviews, webinars, etc., and converted to text using speech recognition technology. Image data is analyzed using image recognition technology to extract relevant information. The data collection unit includes a questionnaire creation unit that can create and distribute questionnaires to target audiences. The questionnaire creation unit generates customized questions based on user interests and sends them to audiences via email or web forms. Questionnaire responses are automatically collected by the data collection unit and stored in a database. The data collection unit also includes an interview execution unit that can coordinate with target personas and conduct interviews. The interview execution unit schedules interviews with audiences and conducts them using an online video conferencing system. The interview content is recorded and collected as audio data. This allows the data collection unit to gather a wide range of data from diverse sources, enriching the knowledge base of the virtual expert AI. Furthermore, the data collection unit centrally manages the collected data, allowing the analysis and update units to access it efficiently. This enables the data collection unit to effectively collect the data that forms the foundation of the virtual expert AI system, thereby improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it uses data mining techniques to extract important keywords and topics from collected text data and identify highly relevant information. Statistical analysis analyzes trends and patterns in the collected data to reveal data distribution and correlations. Machine learning algorithms train models based on the collected data to make predictions and classifications for new data. The analysis unit analyzes the collected information and generates data to be reflected in the virtual expert AI. For example, it uses natural language processing techniques to analyze collected text data and convert it into a format that the virtual expert AI can easily understand. Similarly, it analyzes audio and image data, converts it into text data, and provides it to the virtual expert AI. This allows the analysis unit to integrate diverse collected data and strengthen the knowledge base of the virtual expert AI. Furthermore, the analysis unit can continuously improve the accuracy and reliability of the virtual expert AI by utilizing past data and user feedback. This helps the analysis unit enable the virtual expert AI to provide users with more accurate and useful information.

[0032] The Update Department updates the virtual expert AI based on the analysis results obtained by the Analysis Department. The Update Department updates the virtual expert AI through methods such as database updates and algorithm retraining. Specifically, it regularly updates the virtual expert AI's knowledge base based on new data and analysis results provided by the Analysis Department. Database updates add newly collected information to the existing database to maintain consistency and integrity. Algorithm retraining retrains machine learning models using collected data to improve the virtual expert AI's prediction accuracy and response quality. The Update Department monitors the virtual expert AI's performance and makes adjustments and improvements as needed. For example, it analyzes user feedback and system usage to optimize the virtual expert AI's response speed and accuracy. The Update Department can also consider introducing new technologies and algorithms to expand the virtual expert AI's capabilities. This ensures that the Update Department maintains the virtual expert AI in a way that provides high-quality services based on the latest information. Furthermore, the Update Department also considers the security and privacy protection of the virtual expert AI, ensuring the safe management and use of data. This enhances the reliability and security of the virtual expert AI, providing users with a secure and reliable environment.

[0033] The reception desk receives questions from users. The reception desk accepts questions in various formats, such as text, voice, and on specific topics. Specifically, users can enter questions in text format via a website or mobile app. For voice questions, users input their questions using a microphone, and speech recognition technology converts them to text. For questions on specific topics, the reception desk categorizes questions based on pre-defined categories and keywords, providing appropriate answers. The reception desk receives user questions quickly and accurately and forwards them to the service department. Furthermore, the reception desk can record the user's question history and past interactions, using this information as reference for future questions. This allows the reception desk to provide personalized services tailored to the user's needs and interests. The reception desk performs initial filtering and categorization of user questions, assisting the service department in efficiently generating answers. For example, it prioritizes questions based on their content and urgency, and responds quickly to important questions. The reception desk can also collect user feedback to improve the system. This allows the reception desk to improve user satisfaction and enhance the value of the virtual expert AI system.

[0034] The service provider provides answers to questions received by the reception department. The service provider provides answers in various formats, such as text and audio, and considers factors like accuracy and reliability. Specifically, the service provider leverages the knowledge base of the virtual expert AI to generate optimal answers to user questions. In text format, it provides answers that include detailed explanations and relevant information. In audio format, it uses speech synthesis technology to provide answers to users in a natural voice. To ensure the accuracy and reliability of answers, the service provider utilizes the analysis results of the virtual expert AI and data from the update department. For example, it integrates data from multiple sources to generate highly reliable answers. Furthermore, the service provider can continuously improve the quality of answers based on user feedback. This allows the service provider to quickly provide users with high-quality information and enhance the reliability and usefulness of the virtual expert AI system. Additionally, the service provider can record answers to user questions and use them as reference information for future questions. This allows the service provider to provide personalized services tailored to user needs and increase the value of the virtual expert AI system.

[0035] The data collection unit includes a questionnaire creation unit, which can create questionnaires and send them to target audiences. The data collection unit can, for example, set questionnaire questions, select answer methods, and set points to be awarded to respondents. The data collection unit can set the questionnaire period and target audience and send questionnaire response requests to respondents. The data collection unit collects the questionnaire responses and stores them in a database. This makes information gathering more efficient by allowing the data collection unit to create questionnaires and send them to target audiences. The specific content and format of the questionnaire include, for example, the types of questions and answer formats (multiple choice, open-ended, etc.). Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the data collection unit can input questionnaire questions into a generation AI, which can then generate the questions.

[0036] The data collection unit includes an interview execution unit, which can coordinate with target personas and conduct interviews. The data collection unit can, for example, set interview questions, select participants, and schedule appointments. The data collection unit can conduct interviews, transcribe responses, and save them in a database. The data collection unit organizes the interview results and provides them to the analysis unit. This allows the data collection unit to gather detailed information by conducting interviews. Specific interview methods and criteria include, for example, the interview format (in-person, online, etc.), the questions asked, and the criteria for selecting participants. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input interview questions into a generative AI, which can then generate the questions.

[0037] The analysis unit can analyze the collected information and generate data to be reflected in the virtual expert AI. For example, the analysis unit can analyze the collected information using data mining techniques to extract important information. The analysis unit can analyze the collected information using statistical analysis techniques to grasp data trends. The analysis unit can analyze the collected information using machine learning algorithms to discover patterns. As a result, the analysis unit improves the accuracy of the AI ​​by analyzing the collected information and generating data to be reflected in the virtual expert AI. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected information into a generative AI, and the generative AI can analyze the data.

[0038] The update unit can update the virtual expert AI based on the analysis results. For example, the update unit can reflect the analysis results in the database and update the knowledge base of the virtual expert AI. The update unit can retrain the algorithm based on the analysis results to improve the accuracy of the virtual expert AI. The update unit can add new information based on the analysis results to keep the information of the virtual expert AI up-to-date. In this way, the update unit can always provide the latest information by updating the virtual expert AI based on the analysis results. Some or all of the above processes in the update unit may be performed using a generative AI, for example, or without a generative AI. For example, the update unit can input the analysis results into a generative AI, and the generative AI can update the virtual expert AI.

[0039] The information provider can generate answers to user questions. For example, the information provider can receive user questions in text format and generate answers using a text generation algorithm. The information provider can also receive user questions in voice format, convert them to text using speech recognition technology, and generate answers. The information provider can provide answers to user questions while considering the accuracy and reliability of the answers. This enables the information provider to provide information quickly by generating answers to user questions. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without a generation AI. For example, the information provider can input a user question into a generation AI, and the generation AI can generate an answer.

[0040] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method from past data collection history and reflect it in the next data collection. The data collection unit can analyze past data collection history, identify areas for improvement in the data collection method, and optimize it. Based on past data collection history, the data collection unit can select a data collection method that suits the characteristics of the data subject. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past data collection history into a generative AI, and the generative AI can select the optimal data collection method.

[0041] The data collection unit can filter information based on the subject's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant information, taking into account the subject's current work situation. The data collection unit can efficiently collect information by filtering it based on the subject's areas of interest. The data collection unit can analyze the subject's work situation and areas of interest and select the optimal data collection method. This enables efficient data collection by filtering information based on the subject's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input data on the subject's work situation and areas of interest into a generating AI, which can then filter the information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of the subject during information collection. For example, the data collection unit can prioritize the collection of region-specific information based on the geographical location information of the subject. The data collection unit can filter and collect highly relevant information by considering the geographical location information of the subject. The data collection unit can analyze the geographical location information of the subject and select the optimal information collection method. As a result, the data collection unit can efficiently collect highly relevant information by considering the geographical location information of the subject. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the geographical location information of the subject into a generating AI, and the generating AI can filter the information.

[0043] The data collection unit can analyze the target person's social media activity and collect relevant information during the information collection process. For example, the data collection unit can analyze the target person's social media activity and prioritize the collection of highly relevant information. Based on the target person's social media activity, the data collection unit can filter the information to be collected and collect it efficiently. The data collection unit can analyze the target person's social media activity and select the optimal information collection method. As a result, the data collection unit can efficiently collect highly relevant information by analyzing the target person's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the target person's social media activity into a generative AI, which can then filter the information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance, and a simplified analysis on information of low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by allowing the analysis unit to adjust the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input information importance data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an industry-specific analysis algorithm to industry information. For business information, the analysis unit can apply a business-specific analysis algorithm. For problem information, the analysis unit can apply a problem-specific analysis algorithm. This allows the analysis unit to perform highly accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input information category data into a generating AI, and the generating AI can apply an analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit can prioritize the analysis of the latest information and provide results quickly. The analysis unit can lower the priority of analysis for older information. The analysis unit can dynamically adjust the priority of analysis according to the timing of information collection. This enables rapid analysis by allowing the analysis unit to determine the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input information collection timing data into a generating AI, and the generating AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information and provide results quickly. The analysis unit can postpone the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis according to the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information relevance data into a generative AI, and the generative AI can adjust the order of analysis.

[0048] The update unit can analyze past update history and select the optimal update method during an update. For example, the update unit can select the most effective update method from past update history and reflect it in the next update. The update unit can analyze past update history, identify areas for improvement in the update method, and optimize it. Based on past update history, the update unit can select an update method that suits the characteristics of the person being updated. In this way, the update unit can select the optimal update method by analyzing past update history. Some or all of the above processing in the update unit may be performed using, for example, a generation AI, or without a generation AI. For example, the update unit can input past update history data into a generation AI, and the generation AI can select the optimal update method.

[0049] The update unit can customize the update method based on the current business situation during the update process. For example, the update unit prioritizes updating highly relevant information, taking into account the current business situation. The update unit can customize the update method based on the current business situation and update efficiently. The update unit can analyze the current business situation and select the optimal update method. This enables efficient updates by customizing the update method based on the current business situation. Some or all of the above processing in the update unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the update unit can input current business situation data into a generating AI, which can then customize the update method.

[0050] The update unit can select the optimal update method when updating, taking into account the geographical location information of the target person. For example, the update unit can prioritize updating region-specific information based on the geographical location information of the target person. The update unit can filter and update highly relevant information, taking into account the geographical location information of the target person. The update unit can analyze the geographical location information of the target person and select the optimal update method. As a result, the update unit can efficiently update highly relevant information by taking into account the geographical location information of the target person. Some or all of the above processing in the update unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the update unit can input the geographical location information of the target person into a generating AI, and the generating AI can filter the information.

[0051] The update unit can analyze the target user's social media activity and propose update methods during the update process. For example, the update unit analyzes the target user's social media activity and prioritizes updating highly relevant information. Based on the target user's social media activity, the update unit can filter the information to be updated and update efficiently. The update unit can analyze the target user's social media activity and select the optimal update method. As a result, the update unit can efficiently update highly relevant information by analyzing the target user's social media activity. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input data on the target user's social media activity into a generative AI, which can then filter the information.

[0052] The reception department can analyze past question history and select the optimal reception method at the time of reception. For example, the reception department can select the most effective reception method from past question history and reflect it in the next reception. The reception department can analyze past question history, identify areas for improvement in the reception method, and optimize it. Based on past question history, the reception department can select a reception method that suits the characteristics of the person being received. In this way, the reception department can select the optimal reception method by analyzing past question history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input past question history data into a generative AI, and the generative AI can select the optimal reception method.

[0053] The reception department can customize the reception process based on the current workload at the time of reception. For example, the reception department can prioritize receiving questions that are highly relevant, taking into account the current workload. The reception department can customize the reception process based on the current workload and receive calls efficiently. The reception department can analyze the current workload and select the optimal reception method. This enables efficient reception by customizing the reception process based on the current workload. Some or all of the above-described processes in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input current workload data into a generative AI, which can then customize the reception process.

[0054] The reception department can select the most appropriate reception method at the time of reception, taking into account the geographical location information of the person concerned. For example, the reception department can prioritize receiving region-specific questions based on the geographical location information of the person concerned. The reception department can filter and receive questions that are highly relevant, taking into account the geographical location information of the person concerned. The reception department can analyze the geographical location information of the person concerned and select the most appropriate reception method. As a result, the reception department can efficiently receive questions that are highly relevant by taking into account the geographical location information of the person concerned. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the reception department can input the geographical location information of the person concerned into a generative AI, and the generative AI can filter the information.

[0055] The reception department can analyze the subject's social media activity at the time of reception and propose a means of reception. For example, the reception department can analyze the subject's social media activity and prioritize receiving questions that are highly relevant. Based on the subject's social media activity, the reception department can filter the questions to be received and receive them efficiently. The reception department can analyze the subject's social media activity and select the optimal reception method. In this way, the reception department can efficiently receive questions that are highly relevant by analyzing the subject's social media activity. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input data on the subject's social media activity into a generative AI, and the generative AI can filter the information.

[0056] The service provider can adjust the level of detail in the answer based on the importance of the question at the time of delivery. For example, the service provider can provide detailed answers to high-importance questions. For low-importance questions, the service provider can provide simplified answers. The service provider can dynamically adjust the level of detail in the answer according to the importance of the question. This enables the service provider to provide efficient answers by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input question importance data into a generative AI, and the generative AI can adjust the level of detail in the answer.

[0057] The service provider can apply different answer algorithms depending on the category of the question at the time of delivery. For example, the service provider can apply an industry-specific answer algorithm to industry information. For business information, the service provider can apply a business-specific answer algorithm. For problem information, the service provider can apply a problem-specific answer algorithm. This allows the service provider to provide highly accurate answers by applying different answer algorithms depending on the category of the question. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the category data of the question into a generative AI, and the generative AI can apply an answer algorithm.

[0058] The service provider can determine the priority of answers based on when the questions were submitted. For example, the service provider can prioritize answers to the most recent questions. The service provider can lower the priority of answers to older questions. The service provider can dynamically adjust the priority of answers according to when the questions were submitted. This allows the service provider to provide quick answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input question submission date data into a generative AI, and the generative AI can determine the priority of answers.

[0059] The answering unit can adjust the order of answers based on the relevance of the questions at the time of delivery. For example, the answering unit can prioritize answers to highly relevant questions. The answering unit can postpone the order of answers to less relevant questions. The answering unit can dynamically adjust the order of answers according to the relevance of the questions. This enables efficient answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the answering unit can input question relevance data into a generative AI, and the generative AI can adjust the order of answers.

[0060] The survey creation unit can analyze past survey results to select the most suitable questions when creating a survey. For example, the survey creation unit can select the most effective questions from past survey results and reflect them in the next survey. The survey creation unit can analyze past survey results to identify areas for improvement in the questions and optimize them. Based on past survey results, the survey creation unit can select questions that are appropriate for the characteristics of the survey participants. In this way, the survey creation unit can select the most suitable questions by analyzing past survey results. Some or all of the above processes in the survey creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the survey creation unit can input past survey result data into a generation AI, and the generation AI can select the most suitable questions.

[0061] The questionnaire creation unit can select the most appropriate questions when creating a questionnaire, taking into account the geographical location information of the respondents. For example, the questionnaire creation unit can prioritize including region-specific questions in the questionnaire based on the geographical location information of the respondents. The questionnaire creation unit can filter and include highly relevant questions in the questionnaire, taking into account the geographical location information of the respondents. The questionnaire creation unit can analyze the geographical location information of the respondents and select the most appropriate questions. As a result, the questionnaire creation unit can efficiently include highly relevant questions in the questionnaire by taking into account the geographical location information of the respondents. Some or all of the above processing in the questionnaire creation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the questionnaire creation unit can input the geographical location information of the respondents into a generation AI, and the generation AI can filter the information.

[0062] The interview unit can analyze past interview results to select the most appropriate questions during an interview. For example, the interview unit can select the most effective questions from past interview results and incorporate them into the next interview. The interview unit can analyze past interview results to identify areas for improvement in the questions and optimize them. Based on past interview results, the interview unit can select questions that are appropriate for the characteristics of the interviewee. In this way, the interview unit can select the most appropriate questions by analyzing past interview results. Some or all of the above processes in the interview unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the interview unit can input past interview result data into a generative AI, which can then select the most appropriate questions.

[0063] The interview unit can select the most appropriate questions during the interview, taking into account the geographical location of the interviewee. For example, the interview unit can prioritize including region-specific questions in the interview based on the interviewee's geographical location. The interview unit can filter and include highly relevant questions in the interview, taking into account the interviewee's geographical location. The interview unit can analyze the interviewee's geographical location and select the most appropriate questions. As a result, the interview unit can efficiently include highly relevant questions in the interview by taking into account the interviewee's geographical location. Some or all of the above processing in the interview unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the interview unit can input the interviewee's geographical location into a generative AI, which can then filter the information.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The virtual expert AI system can further analyze the user's past behavior history and select the optimal method of information delivery. For example, it can select the most effective method of information delivery from past behavior history and reflect it in the next information delivery. It can analyze past behavior history to identify areas for improvement in information delivery methods and optimize them. Based on past behavior history, it can select an information delivery method that suits the user's characteristics. In this way, the optimal method of information delivery can be selected by analyzing past behavior history. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input past behavior history data into a generation AI, and the generation AI can select the optimal method of information delivery.

[0066] The virtual expert AI system can further customize the means of information delivery by considering the user's current work situation. For example, it can prioritize providing highly relevant information based on the current work situation. It can customize the means of information delivery based on the current work situation to deliver information efficiently. It can analyze the current work situation and select the optimal method of information delivery. This makes it possible to deliver information efficiently by customizing the means of information delivery based on the current work situation. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input current work situation data into a generation AI, and the generation AI can customize the means of information delivery.

[0067] The virtual expert AI system can further prioritize providing highly relevant information by considering the user's geographical location. For example, it can prioritize providing region-specific information based on the user's geographical location. It can filter and provide highly relevant information by considering the user's geographical location. It can analyze the user's geographical location and select the optimal method of information provision. This allows for the efficient provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the provisioning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the provisioning unit can input the user's geographical location information into a generation AI, which can then filter the information.

[0068] The virtual expert AI system can further analyze the user's social media activity and provide relevant information. For example, it can analyze the user's social media activity and prioritize providing highly relevant information. Based on the user's social media activity, it can filter the information to be provided and deliver it efficiently. It can analyze the user's social media activity and select the optimal method of information delivery. This allows for the efficient delivery of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input data on the user's social media activity into a generation AI, which can then filter the information.

[0069] The virtual expert AI system can further analyze the user's past question history and select the most appropriate questions. For example, it can select the most effective questions from the past question history and reflect them in the next question. It can analyze the past question history to identify areas for improvement in the questions and optimize them. Based on the past question history, it can select questions that suit the user's characteristics. In this way, the most appropriate questions can be selected by analyzing the past question history. Some or all of the above processing at the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk can input past question history data into a generation AI, and the generation AI can select the most appropriate questions.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The collection unit collects information. The collection unit collects information such as text data, audio data, and image data. The collection unit is equipped with a questionnaire creation unit and can create questionnaires and send them to the target audience. The collection unit is equipped with an interview execution unit and can coordinate with the target personas for interviews and conduct them. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the collected information and generates data to be reflected in the virtual expert AI. Step 3: The update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit. The update unit updates the virtual expert AI by methods such as updating the database or retraining the algorithm. Step 4: The reception desk receives questions from users. The reception desk accepts questions in various formats, such as text, audio, and questions on specific topics. Step 5: The service department provides answers to the questions received by the reception department. The service department provides answers in various ways, such as text format, audio format, and with consideration for accuracy and reliability of the answers.

[0072] (Example of form 2) The virtual expert AI system according to an embodiment of the present invention is a system that, in the consideration of new businesses, creates a virtual expert AI that aggregates information held by people engaged in specific industries or specific tasks, or potential target personas, and allows for quick and easy interviews with the virtual expert AI to gather industry information, specific tasks, challenges, and thoughts. This virtual expert AI system operates a point-earning service in the background for information gathering, and the AI ​​agent autonomously conducts surveys and interviews with target personas, ensuring that the virtual expert AI always has the necessary and up-to-date information. For example, when planning a service to streamline the work of building cleaners, the virtual expert AI system can gain realistic knowledge and insights by interviewing the virtual expert AI about the work and challenges of building cleaners. Similarly, when planning a car navigation map service specifically for taxi drivers, the system can grasp specific needs by interviewing the virtual expert AI about the work and challenges of taxi drivers. This mechanism solves the problem in new business planning where a lack of knowledge and insights about target users or specific industries can hinder the progress of the project. Even when searching the internet doesn't yield the desired information, or when there's no connection to target users or industry professionals for interviews, information gathering and interviews can be easily conducted through the virtual expert AI. Furthermore, the virtual expert AI is constantly updated to hold the latest information, enabling real-time information provision. This allows for rapid progress in considering new businesses and accelerates business creation. In this way, the virtual expert AI system can aggregate information from people engaged in specific industries or tasks, as well as potential target personas, allowing for quick and easy interviews about industry information, specific tasks, challenges, and thoughts during new business considerations.

[0073] The virtual expert AI system according to this embodiment comprises a collection unit, an analysis unit, an update unit, a reception unit, and a provision unit. The collection unit collects information. The collection unit collects information such as text data, audio data, and image data. The collection unit includes a questionnaire creation unit and can create questionnaires and send them to target individuals. The collection unit includes an interview implementation unit and can coordinate with target personas for interviews and conduct interviews. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the collected information and generates data to be reflected in the virtual expert AI. The update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit. The update unit updates the virtual expert AI using methods such as updating the database and retraining the algorithms. The reception unit receives questions from users. The reception unit accepts questions in text format, audio format, and questions on specific topics. The provision unit provides answers to questions received by the reception unit. The providing unit provides answers in various ways, such as text format, audio format, and by specifying the accuracy and reliability of the answers. This enables the virtual expert AI system according to the embodiment to efficiently collect, analyze, update, receive questions, and provide answers.

[0074] The data collection unit collects information. For example, it collects text data, audio data, and image data. Specifically, the data collection unit can collect text data from publicly available databases on the internet, social media, news sites, etc. Audio data is collected from podcasts, audio interviews, webinars, etc., and converted to text using speech recognition technology. Image data is analyzed using image recognition technology to extract relevant information. The data collection unit includes a questionnaire creation unit that can create and distribute questionnaires to target audiences. The questionnaire creation unit generates customized questions based on user interests and sends them to audiences via email or web forms. Questionnaire responses are automatically collected by the data collection unit and stored in a database. The data collection unit also includes an interview execution unit that can coordinate with target personas and conduct interviews. The interview execution unit schedules interviews with audiences and conducts them using an online video conferencing system. The interview content is recorded and collected as audio data. This allows the data collection unit to gather a wide range of data from diverse sources, enriching the knowledge base of the virtual expert AI. Furthermore, the data collection unit centrally manages the collected data, allowing the analysis and update units to access it efficiently. This enables the data collection unit to effectively collect the data that forms the foundation of the virtual expert AI system, thereby improving the overall system performance.

[0075] The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. Specifically, it uses data mining techniques to extract important keywords and topics from collected text data and identify highly relevant information. Statistical analysis analyzes trends and patterns in the collected data to reveal data distribution and correlations. Machine learning algorithms train models based on the collected data to make predictions and classifications for new data. The analysis unit analyzes the collected information and generates data to be reflected in the virtual expert AI. For example, it uses natural language processing techniques to analyze collected text data and convert it into a format that the virtual expert AI can easily understand. Similarly, it analyzes audio and image data, converts it into text data, and provides it to the virtual expert AI. This allows the analysis unit to integrate diverse collected data and strengthen the knowledge base of the virtual expert AI. Furthermore, the analysis unit can continuously improve the accuracy and reliability of the virtual expert AI by utilizing past data and user feedback. This helps the analysis unit enable the virtual expert AI to provide users with more accurate and useful information.

[0076] The Update Department updates the virtual expert AI based on the analysis results obtained by the Analysis Department. The Update Department updates the virtual expert AI through methods such as database updates and algorithm retraining. Specifically, it regularly updates the virtual expert AI's knowledge base based on new data and analysis results provided by the Analysis Department. Database updates add newly collected information to the existing database to maintain consistency and integrity. Algorithm retraining retrains machine learning models using collected data to improve the virtual expert AI's prediction accuracy and response quality. The Update Department monitors the virtual expert AI's performance and makes adjustments and improvements as needed. For example, it analyzes user feedback and system usage to optimize the virtual expert AI's response speed and accuracy. The Update Department can also consider introducing new technologies and algorithms to expand the virtual expert AI's capabilities. This ensures that the Update Department maintains the virtual expert AI in a way that provides high-quality services based on the latest information. Furthermore, the Update Department also considers the security and privacy protection of the virtual expert AI, ensuring the safe management and use of data. This enhances the reliability and security of the virtual expert AI, providing users with a secure and reliable environment.

[0077] The reception desk receives questions from users. The reception desk accepts questions in various formats, such as text, voice, and on specific topics. Specifically, users can enter questions in text format via a website or mobile app. For voice questions, users input their questions using a microphone, and speech recognition technology converts them to text. For questions on specific topics, the reception desk categorizes questions based on pre-defined categories and keywords, providing appropriate answers. The reception desk receives user questions quickly and accurately and forwards them to the service department. Furthermore, the reception desk can record the user's question history and past interactions, using this information as reference for future questions. This allows the reception desk to provide personalized services tailored to the user's needs and interests. The reception desk performs initial filtering and categorization of user questions, assisting the service department in efficiently generating answers. For example, it prioritizes questions based on their content and urgency, and responds quickly to important questions. The reception desk can also collect user feedback to improve the system. This allows the reception desk to improve user satisfaction and enhance the value of the virtual expert AI system.

[0078] The service provider provides answers to questions received by the reception department. The service provider provides answers in various formats, such as text and audio, and considers factors like accuracy and reliability. Specifically, the service provider leverages the knowledge base of the virtual expert AI to generate optimal answers to user questions. In text format, it provides answers that include detailed explanations and relevant information. In audio format, it uses speech synthesis technology to provide answers to users in a natural voice. To ensure the accuracy and reliability of answers, the service provider utilizes the analysis results of the virtual expert AI and data from the update department. For example, it integrates data from multiple sources to generate highly reliable answers. Furthermore, the service provider can continuously improve the quality of answers based on user feedback. This allows the service provider to quickly provide users with high-quality information and enhance the reliability and usefulness of the virtual expert AI system. Additionally, the service provider can record answers to user questions and use them as reference information for future questions. This allows the service provider to provide personalized services tailored to user needs and increase the value of the virtual expert AI system.

[0079] The data collection unit includes a questionnaire creation unit, which can create questionnaires and send them to target audiences. The data collection unit can, for example, set questionnaire questions, select answer methods, and set points to be awarded to respondents. The data collection unit can set the questionnaire period and target audience and send questionnaire response requests to respondents. The data collection unit collects the questionnaire responses and stores them in a database. This makes information gathering more efficient by allowing the data collection unit to create questionnaires and send them to target audiences. The specific content and format of the questionnaire include, for example, the types of questions and answer formats (multiple choice, open-ended, etc.). Some or all of the above processing in the data collection unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the data collection unit can input questionnaire questions into a generation AI, which can then generate the questions.

[0080] The data collection unit includes an interview execution unit, which can coordinate with target personas and conduct interviews. The data collection unit can, for example, set interview questions, select participants, and schedule appointments. The data collection unit can conduct interviews, transcribe responses, and save them in a database. The data collection unit organizes the interview results and provides them to the analysis unit. This allows the data collection unit to gather detailed information by conducting interviews. Specific interview methods and criteria include, for example, the interview format (in-person, online, etc.), the questions asked, and the criteria for selecting participants. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not. For example, the data collection unit can input interview questions into a generative AI, which can then generate the questions.

[0081] The analysis unit can analyze the collected information and generate data to be reflected in the virtual expert AI. For example, the analysis unit can analyze the collected information using data mining techniques to extract important information. The analysis unit can analyze the collected information using statistical analysis techniques to grasp data trends. The analysis unit can analyze the collected information using machine learning algorithms to discover patterns. As a result, the analysis unit improves the accuracy of the AI ​​by analyzing the collected information and generating data to be reflected in the virtual expert AI. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected information into a generative AI, and the generative AI can analyze the data.

[0082] The update unit can update the virtual expert AI based on the analysis results. For example, the update unit can reflect the analysis results in the database and update the knowledge base of the virtual expert AI. The update unit can retrain the algorithm based on the analysis results to improve the accuracy of the virtual expert AI. The update unit can add new information based on the analysis results to keep the information of the virtual expert AI up-to-date. In this way, the update unit can always provide the latest information by updating the virtual expert AI based on the analysis results. Some or all of the above processes in the update unit may be performed using a generative AI, for example, or without a generative AI. For example, the update unit can input the analysis results into a generative AI, and the generative AI can update the virtual expert AI.

[0083] The information provider can generate answers to user questions. For example, the information provider can receive user questions in text format and generate answers using a text generation algorithm. The information provider can also receive user questions in voice format, convert them to text using speech recognition technology, and generate answers. The information provider can provide answers to user questions while considering the accuracy and reliability of the answers. This enables the information provider to provide information quickly by generating answers to user questions. Some or all of the above processing in the information provider may be performed using, for example, a generation AI, or without a generation AI. For example, the information provider can input a user question into a generation AI, and the generation AI can generate an answer.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of information collection and collect it when the user is relaxed. If the user is relaxed, the data collection unit can speed up the timing of information collection and collect it efficiently. If the user is in a hurry, the data collection unit can adjust the timing of information collection and collect it quickly. In this way, the data collection unit can efficiently collect information by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can estimate the emotions.

[0085] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method from past data collection history and reflect it in the next data collection. The data collection unit can analyze past data collection history, identify areas for improvement in the data collection method, and optimize it. Based on past data collection history, the data collection unit can select a data collection method that suits the characteristics of the data subject. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input past data collection history into a generative AI, and the generative AI can select the optimal data collection method.

[0086] The data collection unit can filter information based on the subject's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant information, taking into account the subject's current work situation. The data collection unit can efficiently collect information by filtering it based on the subject's areas of interest. The data collection unit can analyze the subject's work situation and areas of interest and select the optimal data collection method. This enables efficient data collection by filtering information based on the subject's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input data on the subject's work situation and areas of interest into a generating AI, which can then filter the information.

[0087] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information and prioritize collecting more important information. If the user is relaxed, the data collection unit can collect all information equally. If the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. In this way, the data collection unit can prioritize the collection of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of the subject during information collection. For example, the data collection unit can prioritize the collection of region-specific information based on the geographical location information of the subject. The data collection unit can filter and collect highly relevant information by considering the geographical location information of the subject. The data collection unit can analyze the geographical location information of the subject and select the optimal information collection method. As a result, the data collection unit can efficiently collect highly relevant information by considering the geographical location information of the subject. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the geographical location information of the subject into a generating AI, and the generating AI can filter the information.

[0089] The data collection unit can analyze the target person's social media activity and collect relevant information during the information collection process. For example, the data collection unit can analyze the target person's social media activity and prioritize the collection of highly relevant information. Based on the target person's social media activity, the data collection unit can filter the information to be collected and collect it efficiently. The data collection unit can analyze the target person's social media activity and select the optimal information collection method. As a result, the data collection unit can efficiently collect highly relevant information by analyzing the target person's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the target person's social media activity into a generative AI, which can then filter the information.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can provide appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can estimate the emotions.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance, and a simplified analysis on information of low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by allowing the analysis unit to adjust the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input information importance data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply an industry-specific analysis algorithm to industry information. For business information, the analysis unit can apply a business-specific analysis algorithm. For problem information, the analysis unit can apply a problem-specific analysis algorithm. This allows the analysis unit to perform highly accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input information category data into a generating AI, and the generating AI can apply an analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. Thus, the analysis unit can provide appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0094] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit can prioritize the analysis of the latest information and provide results quickly. The analysis unit can lower the priority of analysis for older information. The analysis unit can dynamically adjust the priority of analysis according to the timing of information collection. This enables rapid analysis by allowing the analysis unit to determine the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input information collection timing data into a generating AI, and the generating AI can determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information and provide results quickly. The analysis unit can postpone the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis according to the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information relevance data into a generative AI, and the generative AI can adjust the order of analysis.

[0096] The update unit can estimate the user's emotions and adjust the update method based on the estimated emotions. For example, if the user is stressed, the update unit can provide a simple update method. If the user is relaxed, the update unit can provide a detailed update method. If the user is in a hurry, the update unit can provide a method that allows for a quick update. This enables the update unit to provide appropriate updates by adjusting the update method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using a generative AI, or not using a generative AI. For example, the update unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0097] The update unit can analyze past update history and select the optimal update method during an update. For example, the update unit can select the most effective update method from past update history and reflect it in the next update. The update unit can analyze past update history, identify areas for improvement in the update method, and optimize it. Based on past update history, the update unit can select an update method that suits the characteristics of the person being updated. In this way, the update unit can select the optimal update method by analyzing past update history. Some or all of the above processing in the update unit may be performed using, for example, a generation AI, or without a generation AI. For example, the update unit can input past update history data into a generation AI, and the generation AI can select the optimal update method.

[0098] The update unit can customize the update method based on the current business situation during the update process. For example, the update unit prioritizes updating highly relevant information, taking into account the current business situation. The update unit can customize the update method based on the current business situation and update efficiently. The update unit can analyze the current business situation and select the optimal update method. This enables efficient updates by customizing the update method based on the current business situation. Some or all of the above processing in the update unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the update unit can input current business situation data into a generating AI, which can then customize the update method.

[0099] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated emotions. For example, if the user is stressed, the update unit will postpone less important updates and prioritize more important ones. If the user is relaxed, the update unit can distribute all updates evenly. If the user is in a hurry, the update unit can prioritize information that can be updated quickly. In this way, the update unit can prioritize important updates by determining the priority of updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using a generative AI, or not using a generative AI. For example, the update unit can input user emotion data into a generative AI, which can estimate the emotions.

[0100] The update unit can select the optimal update method when updating, taking into account the geographical location information of the target person. For example, the update unit can prioritize updating region-specific information based on the geographical location information of the target person. The update unit can filter and update highly relevant information, taking into account the geographical location information of the target person. The update unit can analyze the geographical location information of the target person and select the optimal update method. As a result, the update unit can efficiently update highly relevant information by taking into account the geographical location information of the target person. Some or all of the above processing in the update unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the update unit can input the geographical location information of the target person into a generating AI, and the generating AI can filter the information.

[0101] The update unit can analyze the target user's social media activity and propose update methods during the update process. For example, the update unit analyzes the target user's social media activity and prioritizes updating highly relevant information. Based on the target user's social media activity, the update unit can filter the information to be updated and update efficiently. The update unit can analyze the target user's social media activity and select the optimal update method. As a result, the update unit can efficiently update highly relevant information by analyzing the target user's social media activity. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input data on the target user's social media activity into a generative AI, which can then filter the information.

[0102] The reception desk can estimate the user's emotions and adjust the reception process based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input and process the request quickly. This allows the reception desk to provide appropriate reception by adjusting the reception process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input user emotion data into a generative AI, which can then estimate the emotion.

[0103] The reception department can analyze past question history and select the optimal reception method at the time of reception. For example, the reception department can select the most effective reception method from past question history and reflect it in the next reception. The reception department can analyze past question history, identify areas for improvement in the reception method, and optimize it. Based on past question history, the reception department can select a reception method that suits the characteristics of the person being received. In this way, the reception department can select the optimal reception method by analyzing past question history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or without a generative AI. For example, the reception department can input past question history data into a generative AI, and the generative AI can select the optimal reception method.

[0104] The reception department can customize the reception process based on the current workload at the time of reception. For example, the reception department can prioritize receiving questions that are highly relevant, taking into account the current workload. The reception department can customize the reception process based on the current workload and receive calls efficiently. The reception department can analyze the current workload and select the optimal reception method. This enables efficient reception by customizing the reception process based on the current workload. Some or all of the above-described processes in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input current workload data into a generative AI, which can then customize the reception process.

[0105] The reception desk can estimate the user's emotions and determine the priority of inquiries based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone less important questions and prioritize more important ones. If the user is relaxed, the reception desk can handle all questions equally. If the user is in a hurry, the reception desk can prioritize questions that can be answered quickly. In this way, the reception desk can prioritize important questions by determining the priority of inquiries based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions.

[0106] The reception department can select the most appropriate reception method at the time of reception, taking into account the geographical location information of the person concerned. For example, the reception department can prioritize receiving region-specific questions based on the geographical location information of the person concerned. The reception department can filter and receive questions that are highly relevant, taking into account the geographical location information of the person concerned. The reception department can analyze the geographical location information of the person concerned and select the most appropriate reception method. As a result, the reception department can efficiently receive questions that are highly relevant by taking into account the geographical location information of the person concerned. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the reception department can input the geographical location information of the person concerned into a generative AI, and the generative AI can filter the information.

[0107] The reception department can analyze the subject's social media activity at the time of reception and propose a means of reception. For example, the reception department can analyze the subject's social media activity and prioritize receiving questions that are highly relevant. Based on the subject's social media activity, the reception department can filter the questions to be received and receive them efficiently. The reception department can analyze the subject's social media activity and select the optimal reception method. In this way, the reception department can efficiently receive questions that are highly relevant by analyzing the subject's social media activity. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input data on the subject's social media activity into a generative AI, and the generative AI can filter the information.

[0108] The service provider can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and easy-to-read response. If the user is relaxed, the service provider can provide a detailed response. If the user is in a hurry, the service provider can provide a concise response. This allows the service provider to provide an appropriate response by adjusting the way it expresses its response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate the emotions.

[0109] The service provider can adjust the level of detail in the answer based on the importance of the question at the time of delivery. For example, the service provider can provide detailed answers to high-importance questions. For low-importance questions, the service provider can provide simplified answers. The service provider can dynamically adjust the level of detail in the answer according to the importance of the question. This enables the service provider to provide efficient answers by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input question importance data into a generative AI, and the generative AI can adjust the level of detail in the answer.

[0110] The service provider can apply different answer algorithms depending on the category of the question at the time of delivery. For example, the service provider can apply an industry-specific answer algorithm to industry information. For business information, the service provider can apply a business-specific answer algorithm. For problem information, the service provider can apply a problem-specific answer algorithm. This allows the service provider to provide highly accurate answers by applying different answer algorithms depending on the category of the question. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the category data of the question into a generative AI, and the generative AI can apply an answer algorithm.

[0111] The service provider can estimate the user's emotions and adjust the length of its response based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide a short, to-the-point response. If the user is relaxed, the service provider can provide a longer response that includes detailed explanations. If the user is excited, the service provider can provide a response with visually stimulating effects. This allows the service provider to provide an appropriate response by adjusting the length of its response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then estimate the emotions.

[0112] The service provider can determine the priority of answers based on when the questions were submitted. For example, the service provider can prioritize answers to the most recent questions. The service provider can lower the priority of answers to older questions. The service provider can dynamically adjust the priority of answers according to when the questions were submitted. This allows the service provider to provide quick answers by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input question submission date data into a generative AI, and the generative AI can determine the priority of answers.

[0113] The answering unit can adjust the order of answers based on the relevance of the questions at the time of delivery. For example, the answering unit can prioritize answers to highly relevant questions. The answering unit can postpone the order of answers to less relevant questions. The answering unit can dynamically adjust the order of answers according to the relevance of the questions. This enables efficient answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the answering unit may be performed using, for example, a generative AI, or without a generative AI. For example, the answering unit can input question relevance data into a generative AI, and the generative AI can adjust the order of answers.

[0114] The survey creation unit can estimate the user's emotions and adjust the survey questions based on those emotions. For example, if the user is stressed, the survey creation unit can provide simple and easy-to-answer questions. If the user is relaxed, the survey creation unit can provide detailed questions. If the user is in a hurry, the survey creation unit can provide questions that can be answered quickly. In this way, the survey creation unit can provide appropriate questions by adjusting the survey questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey creation unit may be performed using a generative AI, or not using a generative AI. For example, the survey creation unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0115] The survey creation unit can analyze past survey results to select the most suitable questions when creating a survey. For example, the survey creation unit can select the most effective questions from past survey results and reflect them in the next survey. The survey creation unit can analyze past survey results to identify areas for improvement in the questions and optimize them. Based on past survey results, the survey creation unit can select questions that are appropriate for the characteristics of the survey participants. In this way, the survey creation unit can select the most suitable questions by analyzing past survey results. Some or all of the above processes in the survey creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the survey creation unit can input past survey result data into a generation AI, and the generation AI can select the most suitable questions.

[0116] The survey creation unit can estimate the user's emotions and prioritize the survey based on those emotions. For example, if the user is stressed, the survey creation unit can postpone less important questions and prioritize including more important questions in the survey. If the user is relaxed, the survey creation unit can include all questions equally in the survey. If the user is in a hurry, the survey creation unit can prioritize including questions that can be answered quickly. In this way, the survey creation unit can prioritize including important questions in the survey by prioritizing the survey based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the survey creation unit may be performed using a generative AI, or not using a generative AI. For example, the survey creation unit can input user emotion data into a generative AI, which can estimate the emotions.

[0117] The questionnaire creation unit can select the most appropriate questions when creating a questionnaire, taking into account the geographical location information of the respondents. For example, the questionnaire creation unit can prioritize including region-specific questions in the questionnaire based on the geographical location information of the respondents. The questionnaire creation unit can filter and include highly relevant questions in the questionnaire, taking into account the geographical location information of the respondents. The questionnaire creation unit can analyze the geographical location information of the respondents and select the most appropriate questions. As a result, the questionnaire creation unit can efficiently include highly relevant questions in the questionnaire by taking into account the geographical location information of the respondents. Some or all of the above processing in the questionnaire creation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the questionnaire creation unit can input the geographical location information of the respondents into a generation AI, and the generation AI can filter the information.

[0118] The interview unit can estimate the user's emotions and adjust the interview questions based on the estimated emotions. For example, if the user is stressed, the interview unit can provide simple and easy-to-answer questions. If the user is relaxed, the interview unit can provide detailed questions. If the user is in a hurry, the interview unit can provide questions that can be answered quickly. In this way, the interview unit can ask appropriate questions by adjusting the interview questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using a generative AI, or not using a generative AI. For example, the interview unit can input user emotion data into a generative AI, and the generative AI can estimate emotions.

[0119] The interview unit can analyze past interview results to select the most appropriate questions during an interview. For example, the interview unit can select the most effective questions from past interview results and incorporate them into the next interview. The interview unit can analyze past interview results to identify areas for improvement in the questions and optimize them. Based on past interview results, the interview unit can select questions that are appropriate for the characteristics of the interviewee. In this way, the interview unit can select the most appropriate questions by analyzing past interview results. Some or all of the above processes in the interview unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the interview unit can input past interview result data into a generative AI, which can then select the most appropriate questions.

[0120] The interview unit can estimate the user's emotions and prioritize interview questions based on the estimated emotions. For example, if the user is stressed, the interview unit can postpone less important questions and prioritize including more important questions in the interview. If the user is relaxed, the interview unit can include all questions equally in the interview. If the user is in a hurry, the interview unit can prioritize including questions that can be answered quickly. In this way, the interview unit can prioritize including important questions in the interview by prioritizing interview questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using a generative AI, or not using a generative AI. For example, the interview unit can input user emotion data into a generative AI, which can estimate emotions.

[0121] The interview unit can select the most appropriate questions during the interview, taking into account the geographical location of the interviewee. For example, the interview unit can prioritize including region-specific questions in the interview based on the interviewee's geographical location. The interview unit can filter and include highly relevant questions in the interview, taking into account the interviewee's geographical location. The interview unit can analyze the interviewee's geographical location and select the most appropriate questions. As a result, the interview unit can efficiently include highly relevant questions in the interview by taking into account the interviewee's geographical location. Some or all of the above processing in the interview unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the interview unit can input the interviewee's geographical location into a generative AI, which can then filter the information.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The virtual expert AI system can further estimate the user's emotions and adjust the way information is delivered based on those emotions. For example, if the user is stressed, it can provide simple, easy-to-understand information. If the user is relaxed, it can provide detailed information. If the user is in a hurry, it can provide concise and to-the-point information quickly. This allows for appropriate information delivery by adjusting the delivery method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using generative AI or not. For example, the delivery unit can input user emotion data into a generative AI, which can then estimate the emotion.

[0124] The virtual expert AI system can further estimate the user's emotions and adjust its information gathering methods based on those emotions. For example, if the user is stressed, it can provide simple, easy-to-answer questions. If the user is relaxed, it can provide detailed questions. If the user is in a hurry, it can provide questions that can be answered quickly. This allows for appropriate information gathering by adjusting the information gathering methods based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using generative AI or not. For example, the collection unit can input the user's emotion data into a generative AI, which can then estimate the emotion.

[0125] The virtual expert AI system can further estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. In this way, by adjusting the presentation of the analysis results based on the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input the user's emotion data into a generative AI, and the generative AI can estimate the emotions.

[0126] The virtual expert AI system can further estimate the user's emotions and adjust the update method based on the estimated emotions. For example, if the user is stressed, it can provide a simple update method. If the user is relaxed, it can provide a detailed update method. If the user is in a hurry, it can provide a method that allows for quick updates. This allows for appropriate updates by adjusting the update method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the update unit may be performed using generative AI or not. For example, the update unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0127] The virtual expert AI system can further estimate the user's emotions and adjust the reception method based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. If the user is in a hurry, it can prioritize voice input and perform the reception quickly. This allows for appropriate reception by adjusting the reception method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reception area may be performed using generative AI or not. For example, the reception area can input user emotion data into a generative AI, which can then estimate the emotions.

[0128] The virtual expert AI system can further analyze the user's past behavior history and select the optimal method of information delivery. For example, it can select the most effective method of information delivery from past behavior history and reflect it in the next information delivery. It can analyze past behavior history to identify areas for improvement in information delivery methods and optimize them. Based on past behavior history, it can select an information delivery method that suits the user's characteristics. In this way, the optimal method of information delivery can be selected by analyzing past behavior history. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input past behavior history data into a generation AI, and the generation AI can select the optimal method of information delivery.

[0129] The virtual expert AI system can further customize the means of information delivery by considering the user's current work situation. For example, it can prioritize providing highly relevant information based on the current work situation. It can customize the means of information delivery based on the current work situation to deliver information efficiently. It can analyze the current work situation and select the optimal method of information delivery. This makes it possible to deliver information efficiently by customizing the means of information delivery based on the current work situation. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input current work situation data into a generation AI, and the generation AI can customize the means of information delivery.

[0130] The virtual expert AI system can further prioritize providing highly relevant information by considering the user's geographical location. For example, it can prioritize providing region-specific information based on the user's geographical location. It can filter and provide highly relevant information by considering the user's geographical location. It can analyze the user's geographical location and select the optimal method of information provision. This allows for the efficient provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the provisioning unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the provisioning unit can input the user's geographical location information into a generation AI, which can then filter the information.

[0131] The virtual expert AI system can further analyze the user's social media activity and provide relevant information. For example, it can analyze the user's social media activity and prioritize providing highly relevant information. Based on the user's social media activity, it can filter the information to be provided and deliver it efficiently. It can analyze the user's social media activity and select the optimal method of information delivery. This allows for the efficient delivery of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input data on the user's social media activity into a generation AI, which can then filter the information.

[0132] The virtual expert AI system can further analyze the user's past question history and select the most appropriate questions. For example, it can select the most effective questions from the past question history and reflect them in the next question. It can analyze the past question history to identify areas for improvement in the questions and optimize them. Based on the past question history, it can select questions that suit the user's characteristics. In this way, the most appropriate questions can be selected by analyzing the past question history. Some or all of the above processing at the reception desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception desk can input past question history data into a generation AI, and the generation AI can select the most appropriate questions.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The collection unit collects information. The collection unit collects information such as text data, audio data, and image data. The collection unit is equipped with a questionnaire creation unit and can create questionnaires and send them to the target audience. The collection unit is equipped with an interview execution unit and can coordinate with the target personas for interviews and conduct them. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the collected information and generates data to be reflected in the virtual expert AI. Step 3: The update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit. The update unit updates the virtual expert AI by methods such as updating the database or retraining the algorithm. Step 4: The reception desk receives questions from users. The reception desk accepts questions in various formats, such as text, audio, and questions on specific topics. Step 5: The service department provides answers to the questions received by the reception department. The service department provides answers in various ways, such as text format, audio format, and with consideration for accuracy and reliability of the answers.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, update unit, reception unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and conducts questionnaires and interviews using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The update unit is implemented in the specific processing unit 290 of the data processing unit 12 and updates the virtual expert AI based on the analysis results. The reception unit is implemented in the specific processing unit 46A of the smart device 14 and receives questions from the user. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides answers to the questions received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, update unit, reception unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and conducts questionnaires and interviews using the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The update unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and updates the virtual expert AI based on the analysis results. The reception unit is implemented, for example, in the control unit 46A of the smart glasses 214 and receives questions from the user. The provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and provides answers to the questions received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, update unit, reception unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314 and conducts questionnaires and interviews using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and updates the virtual expert AI based on the analysis results. The reception unit is implemented, for example, by the control unit 46A of the headset terminal 314 and receives questions from the user. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides answers to the questions received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, update unit, reception unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414 and conducts questionnaires and interviews using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and updates the virtual expert AI based on the analysis results. The reception unit is implemented, for example, by the control unit 46A of the robot 414 and receives questions from the user. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides answers to the questions received by the reception unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] 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.

[0198] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, An update unit updates the virtual expert AI based on the analysis results obtained by the aforementioned analysis unit, A reception desk that handles questions from users, The system includes a provisioning unit that provides answers to questions received by the aforementioned reception unit. A system characterized by the following features. (Note 2) It has a questionnaire creation department that creates questionnaires and distributes them to target audiences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has an interview department that coordinates with the target personas for interviews and conducts the interviews. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected information is analyzed, and data is generated to be reflected in the virtual expert AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned update unit is The virtual expert AI is updated based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Generate answers to user questions The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filtering is performed based on the target person's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, prioritize the collection of highly relevant information, taking into account the geographical location of the target individuals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, analyze the target person's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is It estimates the user's sentiment and adjusts the update method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is During updates, the system analyzes past update history to select the optimal update method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is During updates, customize the update method based on the current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is During the update process, the optimal update method will be selected, taking into account the geographical location information of the target individual. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is When updating, we analyze the target user's social media activity and suggest methods for updating. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is The system estimates the user's emotions and adjusts the reception process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is At the time of registration, the system analyzes past question history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is At the time of registration, customize the registration process based on the current workload. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is At the time of registration, the most suitable registration method will be selected considering the geographical location information of the person concerned. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reception unit is During registration, we analyze the applicant's social media activity and propose a registration method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the data, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the data, different answer algorithms will be applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the information, we will prioritize the answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the answers, the order of responses will be adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned questionnaire creation department, The system estimates the user's emotions and adjusts the survey questions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned questionnaire creation department, When creating a survey, analyze past survey results to select the most suitable questions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned questionnaire creation department, The system estimates user sentiment and prioritizes survey questions based on the estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned questionnaire creation department, When creating a questionnaire, select the most appropriate questions by considering the geographical location of the target audience. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned interview unit, The system estimates the user's emotions and adjusts the interview questions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned interview unit, When conducting an interview, we analyze past interview results to select the most appropriate questions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned interview unit, The system estimates user emotions and prioritizes interviews based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned interview unit, When conducting interviews, select the most appropriate questions by considering the geographical location of the interviewees. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, An update unit updates the virtual expert AI based on the analysis results obtained by the analysis unit, A reception desk that handles questions from users, The system includes a provisioning unit that provides answers to questions received by the aforementioned reception unit. A system characterized by the following features.

2. It has a questionnaire creation department that creates questionnaires and distributes them to target audiences. The system according to feature 1.

3. The company has an interview department that coordinates with the target personas for interviews and conducts the interviews. The system according to feature 1.

4. The aforementioned analysis unit, The collected information is analyzed, and data is generated to be reflected in the virtual expert AI. The system according to feature 1.

5. The aforementioned update unit is The virtual expert AI is updated based on the analysis results. The system according to feature 1.

6. The aforementioned supply unit is, Generate answers to user questions The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past information gathering history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filtering is performed based on the target person's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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