system

Abstract

To provide a system that quickly and efficiently resolves users' everyday questions. [Solution] The specific processing unit 290 of the data processing device 12 in the system performs a reception process to receive user questions, an analysis process to analyze the questions received through the reception process, and an answer provision process to provide answers based on the results of the analysis. The specific processing unit 290 also estimates the user's emotions and adjusts the timing of question reception based on the estimated user emotions.

Description

Technical Field

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[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is not enough means to quickly and efficiently solve the daily doubts of users, and there is room for improvement.

[0005] The system according to the embodiment aims to quickly and efficiently solve the daily doubts of users.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives the doubts of the user. The analysis unit analyzes the doubts received by the reception unit. The provision unit provides an answer based on the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can quickly and efficiently resolve users' everyday questions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The question-solving system according to an embodiment of the present invention provides a mechanism to resolve everyday questions from all user groups, especially the elderly and those who are not tech-savvy, by creating a new frame at the top of the chat room list and using a generating AI within that frame to resolve unclear points and questions. This mechanism allows users to resolve questions directly within the chat room, making it particularly convenient for those who are not tech-savvy and the elderly. Specifically, it consists of the following steps: First, the user accesses the frame provided at the top of the chat room list. Next, the generating AI receives, analyzes, and provides an appropriate answer to the user's question or unclear point. This allows the user to resolve their question within the chat room. The advantages of this mechanism include the following: First, convenience is improved for those who are not tech-savvy and the elderly, as they can resolve unclear points within the chat room. Second, the time spent in the chat room and the number of visits increase, improving user engagement. Furthermore, economic benefits can be expected as advertising revenue can be generated from the new frame. In addition, this mechanism has great social significance as it benefits society and people. For example, if a user enters a question such as "What time is the bank open?", the generating AI analyzes the question and provides an appropriate answer. This allows users to resolve their questions within the chat room. This system can solve everyday questions for all user groups, especially the elderly and those who are less tech-savvy, and can increase the time spent in the chat room and the number of visits. It can also generate advertising revenue in a new space, so economic benefits can be expected. Furthermore, this system has great social significance as it benefits society and people. As a result, the question-solving system can efficiently receive, analyze, and provide answers to users' questions.

[0029] The question-solving system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives questions from users. User questions include, but are not limited to, questions, inquiries, and feedback. The reception unit can, for example, receive questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can also receive questions in image format. For example, the reception unit analyzes the text entered by the user and receives it as a question. Questions in voice format can be converted to text using speech recognition technology and received. Questions in image format can be converted to text using image analysis technology and received. The analysis unit analyzes the questions received by the reception unit using a generation AI. The analysis is performed by, for example, text analysis, data mining, machine learning algorithms, etc., but is not limited to these methods. For example, the analysis unit analyzes the content of the questions using text analysis technology. The analysis unit can also analyze question patterns using data mining technology. The analysis unit can also classify questions using machine learning algorithms. For example, the analysis unit uses text analysis technology to extract keywords from the question and perform analysis. Data mining technology is a technology that extracts useful information from large amounts of data and is used to analyze question patterns. Machine learning algorithms are used to learn from data and classify and predict questions. The provision unit provides answers based on the results analyzed by the analysis unit. Answers are provided in formats such as text, audio, and image, but are not limited to these examples. For example, the provision unit provides answers in text format. The provision unit can also provide answers in audio format. The provision unit can also provide answers in image format. For example, the provision unit generates answers in text format based on the results analyzed by the analysis unit. Answers in audio format are generated using text-to-speech technology. Answers in image format are generated using text-to-image technology. As a result, the question-solving system according to this embodiment can efficiently receive, analyze, and provide answers to user questions.Some or all of the above-described processes in the reception, analysis, and provision departments may be performed using, for example, a generation AI, or without using a generation AI. For example, the reception department can input the user's question into the generation AI and have the generation AI perform the question reception. The analysis department can input the received question into the generation AI and have the generation AI perform the question analysis. The provision department can input the analyzed results into the generation AI and have the generation AI perform the answer generation.

[0030] The reception desk receives user inquiries. User inquiries include, but are not limited to, questions, inquiries, and feedback. The reception desk can accept inquiries in text format, voice format, and image format. For example, the reception desk can analyze text entered by the user and accept it as an inquiry. Voice inquiries can be converted to text using speech recognition technology and accepted. Image inquiries can be converted to text using image analysis technology and accepted. Specifically, text inquiries can be entered by the user through web forms or chatbots. Voice inquiries can be received when the user speaks via telephone or voice assistant and converted to text using speech recognition technology. Image inquiries can be received when the user uploads photos or screenshots and converted to text using image analysis technology. This allows the reception desk to accept inquiries in various formats, improving user convenience. Furthermore, the reception desk can also collect basic user information and contextual information when receiving inquiries. For example, information such as the user's name, contact information, and the circumstances of the inquiry can be collected and used for subsequent analysis and providing answers. This allows the reception department to receive user inquiries efficiently and accurately, improving the overall performance of the system.

[0031] The analysis unit uses generative AI to analyze questions received by the reception unit. Analysis is performed using methods such as text analysis, data mining, and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit uses text analysis techniques to analyze the content of questions. It can also use data mining techniques to analyze question patterns. Furthermore, it can use machine learning algorithms to classify questions. Specifically, it uses text analysis techniques to extract keywords from questions and identify their intent and subject. Data mining techniques are used to extract useful information from large amounts of data, analyzing patterns based on past question data and grouping similar questions. Machine learning algorithms are used to learn from data and classify or predict questions. For example, generative AI uses natural language processing techniques to understand the context of a question and generate an appropriate answer. Generative AI has previously learned from a large amount of text data, enabling it to perform highly accurate analysis of user questions. Furthermore, based on the analysis results, the analysis unit searches for relevant information and past answers to prepare to provide the optimal answer. This allows the analysis unit to quickly and accurately analyze user questions and build a foundation for providing appropriate answers.

[0032] The service provider provides answers based on the results analyzed by the analysis unit. Answers are provided in various formats, including, but are not limited to, text, audio, and image formats. For example, the service provider can provide answers in text format. It can also provide answers in audio format. Furthermore, it can provide answers in image format. Specifically, the service provider generates answers in text format based on the results analyzed by the analysis unit. Audio answers are generated using text-to-speech technology. Image answers are generated using text-to-image technology. For example, the service provider uses generative AI to generate the optimal answer to a user's question. The generative AI leverages a pre-trained knowledge base to generate appropriate answers to user questions. Additionally, the service provider can customize the format and content of the answers according to the user's preferences and circumstances. For example, if a user is visually impaired, an audio answer can be prioritized. The service provider can also collect user feedback after providing answers to continuously improve the accuracy and quality of the responses. This allows the service provider to provide users with quick and appropriate answers, thereby improving user satisfaction.

[0033] The advertising department can display advertisements. For example, the advertising department can display banner ads. The advertising department can also display text ads. The advertising department can also display video ads. For example, the advertising department can display banner ads at the top of the chat room list. Text ads are displayed to provide information related to the user's questions. Video ads are displayed to attract the user's interest. This allows the advertising department to gain economic benefits by displaying advertisements. Some or all of the above processes in the advertising department may be performed using, for example, generative AI, or without generative AI. For example, the advertising department can have generative AI select and display the most suitable advertisement based on the user's questions.

[0034] The measurement unit can measure the user's dwell time. For example, the measurement unit measures the time spent on a page. The measurement unit can also measure session time. The measurement unit can also measure active time. For example, the measurement unit measures the time a user spends in the chat room list. Session time measures the time from when the user accesses the chat room list until they leave. Active time measures the time the user is actually performing actions in the chat room list. By measuring the user's dwell time, it is possible to understand the user's engagement. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input user dwell time data into a generative AI and have the generative AI perform the dwell time measurement.

[0035] The service provider can provide appropriate answers to user questions. For example, the service provider can provide accurate answers to user questions. The service provider can also provide highly relevant answers to user questions. The service provider can also provide answers that satisfy users. For example, the service provider can provide accurate information to user questions. Highly relevant answers provide the most relevant information to user questions. Answers that satisfy users provide information that satisfies the user in response to their questions. By providing appropriate answers to user questions, user satisfaction can be improved. 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 have a generative AI generate and provide answers to user questions.

[0036] The reception desk can analyze the user's past question history and select the most suitable reception method. For example, the reception desk can automatically display questions that the user has frequently asked in the past as suggestions. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest reception methods to be used during specific time periods based on the user's past question history. For example, the reception desk can extract questions that the user has frequently asked in the past from a database and automatically display them as suggestions. By prioritizing suggestion of reception methods that the user has used in the past, user convenience is improved. By predicting and suggesting reception methods to be used during specific time periods, the system can efficiently receive user inquiries. This allows the system to select the most suitable reception method by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past question history data into a generative AI and have the generative AI select the most suitable reception method.

[0037] The reception unit can filter questions based on the user's current situation and areas of interest when receiving them. For example, the reception unit can prioritize receiving questions relevant to the user's current situation (e.g., traveling). The reception unit can also filter and receive relevant questions based on the user's areas of interest (e.g., health, finance). The reception unit can also prioritize receiving relevant questions based on the user's current activity (e.g., shopping). For example, if the user is traveling, the reception unit will prioritize receiving questions related to the travel destination. Based on the user's areas of interest, it will prioritize receiving questions related to health. If the user is shopping, it will prioritize receiving questions related to shopping. This allows the reception unit to receive more relevant questions by filtering them based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's current situation and areas of interest data into a generative AI and have the generative AI perform the filtering of questions.

[0038] The reception desk can prioritize receiving inquiries that are highly relevant by considering the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. If the user is traveling, the reception desk can also prioritize receiving inquiries related to their travel destination. If the user is in a specific facility (e.g., a hospital or bank), the reception desk can also prioritize receiving inquiries related to that facility. For example, if the reception desk is in a specific region, it will prioritize receiving inquiries related to that region. If the user is traveling, it will prioritize receiving inquiries related to their travel destination. If the user is in a specific facility, it will prioritize receiving inquiries related to that facility. This allows the reception desk to prioritize receiving inquiries that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI perform the filtering of inquiries.

[0039] The reception unit can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception unit can prioritize questions related to topics that the user frequently mentions on social media. The reception unit can also adjust the timing of question acceptance based on the user's social media activity times. The reception unit can also prioritize questions based on the user's social media friendships. For example, the reception unit can prioritize questions related to topics that the user frequently mentions on social media. The timing of question acceptance can be adjusted based on the user's social media activity times. The reception unit prioritizes questions based on the user's social media friendships. This allows the reception unit to prioritize questions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or not using generative AI. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI perform question filtering.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during the analysis. For example, the analysis unit can perform a detailed analysis for questions of high importance. For example, the analysis unit can perform a concise analysis for questions of low importance. For example, the analysis unit can perform an analysis with a moderate level of detail for questions of medium importance. For example, the analysis unit can perform a detailed analysis for questions of high importance. For questions of low importance, it can perform a concise analysis. For questions of medium importance, it can perform an analysis with a moderate level of detail. By adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis results can be provided. Some or all of the above processing 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 question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, for questions about health, the analysis unit can apply an analysis algorithm that references a medical database. For questions about finance, the analysis unit can also apply an analysis algorithm that references a financial database. For questions about travel, the analysis unit can also apply an analysis algorithm that references a travel database. For example, for questions about health, the analysis unit can apply an analysis algorithm that references a medical database. For questions about finance, it can apply an analysis algorithm that references a financial database. For questions about travel, it can apply an analysis algorithm that references a travel database. By applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided. 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 question category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions. The analysis unit may also postpone the analysis of older questions. The analysis unit may also give a moderate priority to questions that were submitted at a moderate time. For example, the analysis unit may prioritize the analysis of recently submitted questions, postpone the analysis of older questions, and give a moderate priority to questions that were submitted at a moderate time. By determining the priority of analysis based on when the questions were submitted, it is possible to provide more appropriate analysis results. 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 may input the question submission time data into a generative AI and have the generative AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the questions during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant questions. For example, the analysis unit may postpone the analysis of less relevant questions. For example, the analysis unit may analyze questions of moderate relevance in an appropriate order. For example, the analysis unit may prioritize the analysis of highly relevant questions. Questions of low relevance may be postponed. Questions of moderate relevance may be analyzed in an appropriate order. By adjusting the order of analysis based on the relevance of the questions, more appropriate analysis results can be provided. 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 may input question relevance data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0044] The answering unit can adjust the level of detail in the answer based on the importance of the question when providing the answer. For example, the answering unit can provide a detailed answer to a highly important question. For example, the answering unit can provide a concise answer to a less important question. For example, the answering unit can provide an answer with a moderate level of detail to a moderately important question. For example, the answering unit can provide a detailed answer to a highly important question. For a less important question, it can provide a concise answer. For a moderately important question, it can provide an answer with a moderate level of detail. By adjusting the level of detail in the answer based on the importance of the question, a more appropriate answer can be provided. 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 importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the answer.

[0045] The answering unit can apply different answering algorithms depending on the category of the question when providing answers. For example, for questions about health, the answering unit can apply an answering algorithm that references a medical database. For questions about finance, the answering unit can also apply an answering algorithm that references a financial database. For questions about travel, the answering unit can also apply an answering algorithm that references a travel database. For example, for questions about health, the answering unit can apply an answering algorithm that references a medical database. For questions about finance, it can apply an answering algorithm that references a financial database. For questions about travel, it can apply an answering algorithm that references a travel database. By applying different answering algorithms depending on the category of the question, more appropriate answers can be provided. 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 category data into a generative AI and have the generative AI perform the application of the answering algorithm.

[0046] 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 recently submitted questions. For example, the service provider can postpone answers to older questions. For example, the service provider can provide answers with a moderate priority to questions that were submitted at a moderate time. For example, the service provider can prioritize answers to recently submitted questions. For older questions, it can postpone answers to questions that were submitted at a moderate time. For questions that were submitted at a moderate time, it can provide answers with a moderate priority. This allows for the provision of more appropriate 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 have the generative AI determine the priority of answers.

[0047] The answering unit can adjust the order of answers based on the relevance of the questions when providing answers. For example, the answering unit can prioritize answers to highly relevant questions. For example, the answering unit can postpone answers to less relevant questions. For example, the answering unit can provide answers in an appropriate order to questions of moderate relevance. For example, the answering unit can prioritize answers to highly relevant questions. For less relevant questions, it can postpone answers to questions of moderate relevance. By adjusting the order of answers based on the relevance of the questions, more appropriate answers can be provided. 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 have the generative AI adjust the order of answers.

[0048] The advertising department can select the most relevant ads by referring to the user's past ad click history when displaying ads. For example, the advertising department can display relevant ads based on ads the user has clicked in the past. The advertising department can also prioritize displaying ads in categories of interest based on the user's past ad click history. The advertising department can also analyze the user's past ad click history and select the most effective ads. For example, the advertising department can display relevant ads based on ads the user has clicked in the past. For example, it can prioritize displaying ads in categories of interest based on the user's past ad click history. For example, it can analyze the user's past ad click history and select the most effective ads. This allows for the display of more relevant ads by referring to the user's past ad click history. Some or all of the above processes in the advertising department may be performed using, for example, a generative AI, or not. For example, the advertising department can input the user's past ad click history data into a generative AI and have the generative AI select the most relevant ads.

[0049] The advertising department can display the most relevant advertisements by considering the user's geographical location when displaying ads. For example, if the user is in a specific region, the advertising department can display ads related to that region. For example, if the user is traveling, the advertising department can display ads related to the travel destination. For example, if the user is in a specific facility (e.g., a shopping mall), the advertising department can display ads related to that facility. For example, if the advertising department is in a specific region, it can display ads related to that region. If the user is traveling, it can display ads related to the travel destination. If the user is in a specific facility, it can display ads related to that facility. By considering the user's geographical location, it is possible to display more relevant advertisements. Some or all of the above processing in the advertising department may be performed using, for example, a generative AI, or not using a generative AI. For example, the advertising department can input the user's geographical location data into a generative AI and have the generative AI select the most relevant advertisements.

[0050] The measurement unit can select the optimal measurement method by referring to the user's past stay history when measuring dwell time. For example, the measurement unit can measure detailed dwell time based on places where the user has stayed for a long time in the past. For example, the measurement unit can also prioritize measuring dwell time at places of interest based on the user's past stay history. For example, the measurement unit can analyze the user's past stay history and select the most effective measurement method. For example, the measurement unit can measure detailed dwell time based on places where the user has stayed for a long time in the past. For example, it can prioritize measuring dwell time at places of interest based on the user's past stay history. For example, it can analyze the user's past stay history and select the most effective measurement method. This makes it possible to measure dwell time more accurately by referring to the user's past stay history. Some or all of the above processing in the measurement unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the measurement unit can input the user's past stay history data into a generation AI and have the generation AI select the optimal measurement method.

[0051] The measurement unit can select the optimal measurement method when measuring dwell time, taking into account the user's device information. For example, if the user is using a smartphone, the measurement unit will select a measurement method that takes into account the device's battery consumption. For example, if the user is using a tablet, the measurement unit can also select a measurement method optimized for a large screen. For example, if the user is using a smartwatch, the measurement unit can also select a simple and highly visible measurement method. By taking into account the user's device information, it becomes possible to measure dwell time more accurately. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input user device information data into a generative AI and have the generative AI select the optimal measurement method.

[0052] The measurement unit can select the optimal measurement method when measuring the time spent, taking into account the user's current activity status. For example, if the user is walking, the measurement unit will select a measurement method suitable for walking. For example, if the user is sitting, the measurement unit can also select a measurement method suitable for sitting. For example, if the user is exercising, the measurement unit can also select a measurement method suitable for exercise. For example, if the user is walking, the measurement unit will select a measurement method suitable for walking. If the user is sitting, the measurement unit will select a measurement method suitable for sitting. If the user is exercising, the measurement unit will select a measurement method suitable for exercise. This makes it possible to measure the time spent more accurately by taking into account the user's current activity status. Some or all of the above processing in the measurement unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the measurement unit can input the user's current activity status data into a generation AI and have the generation AI select the optimal measurement method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can refer to the user's past question history when receiving a user's question and automatically suggest similar questions. For example, if a user previously asked about "bank opening hours," the reception desk can refer to that history and automatically suggest information about "bank opening hours" when a similar question is entered again. Also, if a user previously asked about "how to make a restaurant reservation," the reception desk can use that history to suggest related information such as "restaurant menus" and "restaurant locations." This makes it possible to resolve questions more quickly and accurately by utilizing the user's past question history. Some or all of the above processing in 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 the user's past question history data into a generation AI and have the generation AI suggest similar questions.

[0055] The measurement unit can adjust the measurement method based on the type of device the user is using when measuring the user's dwell time. For example, if the user is using a smartphone, the measurement frequency can be set lower to conserve battery power. If the user is using a tablet, a measurement method optimized for the large screen can be selected. If the user is using a smartwatch, a simple and highly visible measurement method can be selected. By providing the optimal measurement method according to the user's device, more accurate measurement of dwell time becomes possible. Some or all of the above processing in the measurement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the measurement unit can input user device information data into a generation AI and have the generation AI select the optimal measurement method.

[0056] The reception desk can provide relevant information when receiving user inquiries, taking into account the user's current geographical location. For example, if a user is in a specific region, inquiries related to that region can be prioritized. If a user is traveling, inquiries related to their travel destination can also be prioritized. Furthermore, if a user is in a specific facility (e.g., a hospital or bank), inquiries related to that facility can be prioritized. This allows for the reception desk to receive more relevant inquiries by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI perform the filtering of inquiries.

[0057] The answering unit can select the most appropriate answer by referring to the user's past question history when providing an answer. For example, if a user previously asked about "bank opening hours," the answering unit can refer to that history and automatically provide information about "bank opening hours" when a similar question is entered again. Also, if a user previously asked about "how to make a restaurant reservation," the answering unit can use that history to provide related information such as "restaurant menus" and "restaurant locations." This allows for faster and more accurate answers to be provided by utilizing the user's past question history. Some or all of the above processing in the answering unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answering unit can input the user's past question history data into a generation AI and have the generation AI select the most appropriate answer.

[0058] The measurement unit can select the optimal measurement method when measuring the user's dwell time, taking into account the user's current activity status. For example, if the user is walking, it can select a measurement method suitable for walking. If the user is sitting, it can also select a measurement method suitable for sitting. Furthermore, if the user is exercising, it can also select a measurement method suitable for exercise. This allows for more accurate measurement of dwell time by considering the user's current activity status. Some or all of the above processing in the measurement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the measurement unit can input the user's current activity status data into a generation AI and have the generation AI select the optimal measurement method.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives user inquiries. User inquiries include questions, inquiries, and feedback. The reception desk can accept inquiries in text, audio, and image formats. For example, text inquiries are accepted as is, audio inquiries are converted to text using speech recognition technology, and image inquiries are converted to text using image analysis technology before being accepted. Step 2: The analysis unit analyzes the questions received by the reception unit. The analysis is performed using generative AI and employs methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis techniques are used to analyze the content of the questions, data mining techniques are used to analyze the patterns of the questions, and machine learning algorithms are used to classify the questions. Step 3: The provisioning unit provides the answer based on the results analyzed by the analysis unit. The answer is provided in various formats, such as text, audio, and image. For example, the answer is generated in text format, the audio answer is generated using text-to-speech technology, and the image answer is generated using text-to-image technology.

[0061] (Example of form 2) The question-solving system according to an embodiment of the present invention provides a mechanism to resolve everyday questions from all user groups, especially the elderly and those who are not tech-savvy, by creating a new frame at the top of the chat room list and using a generating AI within that frame to resolve unclear points and questions. This mechanism allows users to resolve questions directly within the chat room, making it particularly convenient for those who are not tech-savvy and the elderly. Specifically, it consists of the following steps: First, the user accesses the frame provided at the top of the chat room list. Next, the generating AI receives, analyzes, and provides an appropriate answer to the user's question or unclear point. This allows the user to resolve their question within the chat room. The advantages of this mechanism include the following: First, convenience is improved for those who are not tech-savvy and the elderly, as they can resolve unclear points within the chat room. Second, the time spent in the chat room and the number of visits increase, improving user engagement. Furthermore, economic benefits can be expected as advertising revenue can be generated from the new frame. In addition, this mechanism has great social significance as it benefits society and people. For example, if a user enters a question such as "What time is the bank open?", the generating AI analyzes the question and provides an appropriate answer. This allows users to resolve their questions within the chat room. This system can solve everyday questions for all user groups, especially the elderly and those who are less tech-savvy, and can increase the time spent in the chat room and the number of visits. It can also generate advertising revenue in a new space, so economic benefits can be expected. Furthermore, this system has great social significance as it benefits society and people. As a result, the question-solving system can efficiently receive, analyze, and provide answers to users' questions.

[0062] The question-solving system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives questions from users. User questions include, but are not limited to, questions, inquiries, and feedback. The reception unit can, for example, receive questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can also receive questions in image format. For example, the reception unit analyzes the text entered by the user and receives it as a question. Questions in voice format can be converted to text using speech recognition technology and received. Questions in image format can be converted to text using image analysis technology and received. The analysis unit analyzes the questions received by the reception unit using a generation AI. The analysis is performed by, for example, text analysis, data mining, machine learning algorithms, etc., but is not limited to these methods. For example, the analysis unit analyzes the content of the questions using text analysis technology. The analysis unit can also analyze question patterns using data mining technology. The analysis unit can also classify questions using machine learning algorithms. For example, the analysis unit uses text analysis technology to extract keywords from the question and perform analysis. Data mining technology is a technology that extracts useful information from large amounts of data and is used to analyze question patterns. Machine learning algorithms are used to learn from data and classify and predict questions. The provision unit provides answers based on the results analyzed by the analysis unit. Answers are provided in formats such as text, audio, and image, but are not limited to these examples. For example, the provision unit provides answers in text format. The provision unit can also provide answers in audio format. The provision unit can also provide answers in image format. For example, the provision unit generates answers in text format based on the results analyzed by the analysis unit. Answers in audio format are generated using text-to-speech technology. Answers in image format are generated using text-to-image technology. As a result, the question-solving system according to this embodiment can efficiently receive, analyze, and provide answers to user questions.Some or all of the above-described processes in the reception, analysis, and provision departments may be performed using, for example, a generation AI, or without using a generation AI. For example, the reception department can input the user's question into the generation AI and have the generation AI perform the question reception. The analysis department can input the received question into the generation AI and have the generation AI perform the question analysis. The provision department can input the analyzed results into the generation AI and have the generation AI perform the answer generation.

[0063] The reception desk receives user inquiries. User inquiries include, but are not limited to, questions, inquiries, and feedback. The reception desk can accept inquiries in text format, voice format, and image format. For example, the reception desk can analyze text entered by the user and accept it as an inquiry. Voice inquiries can be converted to text using speech recognition technology and accepted. Image inquiries can be converted to text using image analysis technology and accepted. Specifically, text inquiries can be entered by the user through web forms or chatbots. Voice inquiries can be received when the user speaks via telephone or voice assistant and converted to text using speech recognition technology. Image inquiries can be received when the user uploads photos or screenshots and converted to text using image analysis technology. This allows the reception desk to accept inquiries in various formats, improving user convenience. Furthermore, the reception desk can also collect basic user information and contextual information when receiving inquiries. For example, information such as the user's name, contact information, and the circumstances of the inquiry can be collected and used for subsequent analysis and providing answers. This allows the reception department to receive user inquiries efficiently and accurately, improving the overall performance of the system.

[0064] The analysis unit uses generative AI to analyze questions received by the reception unit. Analysis is performed using methods such as text analysis, data mining, and machine learning algorithms, but is not limited to these examples. For instance, the analysis unit uses text analysis techniques to analyze the content of questions. It can also use data mining techniques to analyze question patterns. Furthermore, it can use machine learning algorithms to classify questions. Specifically, it uses text analysis techniques to extract keywords from questions and identify their intent and subject. Data mining techniques are used to extract useful information from large amounts of data, analyzing patterns based on past question data and grouping similar questions. Machine learning algorithms are used to learn from data and classify or predict questions. For example, generative AI uses natural language processing techniques to understand the context of a question and generate an appropriate answer. Generative AI has previously learned from a large amount of text data, enabling it to perform highly accurate analysis of user questions. Furthermore, based on the analysis results, the analysis unit searches for relevant information and past answers to prepare to provide the optimal answer. This allows the analysis unit to quickly and accurately analyze user questions and build a foundation for providing appropriate answers.

[0065] The service provider provides answers based on the results analyzed by the analysis unit. Answers are provided in various formats, including, but are not limited to, text, audio, and image formats. For example, the service provider can provide answers in text format. It can also provide answers in audio format. Furthermore, it can provide answers in image format. Specifically, the service provider generates answers in text format based on the results analyzed by the analysis unit. Audio answers are generated using text-to-speech technology. Image answers are generated using text-to-image technology. For example, the service provider uses generative AI to generate the optimal answer to a user's question. The generative AI leverages a pre-trained knowledge base to generate appropriate answers to user questions. Additionally, the service provider can customize the format and content of the answers according to the user's preferences and circumstances. For example, if a user is visually impaired, an audio answer can be prioritized. The service provider can also collect user feedback after providing answers to continuously improve the accuracy and quality of the responses. This allows the service provider to provide users with quick and appropriate answers, thereby improving user satisfaction.

[0066] The advertising department can display advertisements. For example, the advertising department can display banner ads. The advertising department can also display text ads. The advertising department can also display video ads. For example, the advertising department can display banner ads at the top of the chat room list. Text ads are displayed to provide information related to the user's questions. Video ads are displayed to attract the user's interest. This allows the advertising department to gain economic benefits by displaying advertisements. Some or all of the above processes in the advertising department may be performed using, for example, generative AI, or without generative AI. For example, the advertising department can have generative AI select and display the most suitable advertisement based on the user's questions.

[0067] The measurement unit can measure the user's dwell time. For example, the measurement unit measures the time spent on a page. The measurement unit can also measure session time. The measurement unit can also measure active time. For example, the measurement unit measures the time a user spends in the chat room list. Session time measures the time from when the user accesses the chat room list until they leave. Active time measures the time the user is actually performing actions in the chat room list. By measuring the user's dwell time, it is possible to understand the user's engagement. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input user dwell time data into a generative AI and have the generative AI perform the dwell time measurement.

[0068] The service provider can provide appropriate answers to user questions. For example, the service provider can provide accurate answers to user questions. The service provider can also provide highly relevant answers to user questions. The service provider can also provide answers that satisfy users. For example, the service provider can provide accurate information to user questions. Highly relevant answers provide the most relevant information to user questions. Answers that satisfy users provide information that satisfies the user in response to their questions. By providing appropriate answers to user questions, user satisfaction can be improved. 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 have a generative AI generate and provide answers to user questions.

[0069] The reception desk can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, if the user is stressed, the reception desk will quickly receive the question and respond immediately. For example, if the user is relaxed, the reception desk will slowly receive the question and collect detailed information. For example, if the user is in a hurry, the reception desk can accept the question in a concise format and send it for quick analysis. For example, the reception desk can analyze the user's facial expressions and quickly receive the question if they are stressed. If they are relaxed, it will slowly receive the question to collect detailed information. If they are in a hurry, it will accept the question in a concise format and send it for quick analysis. This allows for question reception at a more appropriate time by adjusting the timing of question reception according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can analyze the user's past question history and select the most suitable reception method. For example, the reception desk can automatically display questions that the user has frequently asked in the past as suggestions. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest reception methods to be used during specific time periods based on the user's past question history. For example, the reception desk can extract questions that the user has frequently asked in the past from a database and automatically display them as suggestions. By prioritizing suggestion of reception methods that the user has used in the past, user convenience is improved. By predicting and suggesting reception methods to be used during specific time periods, the system can efficiently receive user inquiries. This allows the system to select the most suitable reception method by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past question history data into a generative AI and have the generative AI select the most suitable reception method.

[0071] The reception unit can filter questions based on the user's current situation and areas of interest when receiving them. For example, the reception unit can prioritize receiving questions relevant to the user's current situation (e.g., traveling). The reception unit can also filter and receive relevant questions based on the user's areas of interest (e.g., health, finance). The reception unit can also prioritize receiving relevant questions based on the user's current activity (e.g., shopping). For example, if the user is traveling, the reception unit will prioritize receiving questions related to the travel destination. Based on the user's areas of interest, it will prioritize receiving questions related to health. If the user is shopping, it will prioritize receiving questions related to shopping. This allows the reception unit to receive more relevant questions by filtering them based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's current situation and areas of interest data into a generative AI and have the generative AI perform the filtering of questions.

[0072] The reception desk can estimate the user's emotions and determine the priority of questions to receive based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize receiving urgent questions. For example, if the user is relaxed, the reception desk may also prioritize receiving questions that require detailed information. For example, if the user is in a hurry, the reception desk may also prioritize receiving concise questions. For example, the reception desk can analyze the user's facial expressions and prioritize urgent questions if the user is feeling anxious. If the user is relaxed, it will prioritize receiving questions that require detailed information. If the user is in a hurry, it will prioritize receiving concise questions. This allows for prioritizing questions according to the user's emotions, thereby prioritizing the receipt of more appropriate questions. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, generative AI, or not using generative AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0073] The reception desk can prioritize receiving inquiries that are highly relevant by considering the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. If the user is traveling, the reception desk can also prioritize receiving inquiries related to their travel destination. If the user is in a specific facility (e.g., a hospital or bank), the reception desk can also prioritize receiving inquiries related to that facility. For example, if the reception desk is in a specific region, it will prioritize receiving inquiries related to that region. If the user is traveling, it will prioritize receiving inquiries related to their travel destination. If the user is in a specific facility, it will prioritize receiving inquiries related to that facility. This allows the reception desk to prioritize receiving inquiries that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI perform the filtering of inquiries.

[0074] The reception unit can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception unit can prioritize questions related to topics that the user frequently mentions on social media. The reception unit can also adjust the timing of question acceptance based on the user's social media activity times. The reception unit can also prioritize questions based on the user's social media friendships. For example, the reception unit can prioritize questions related to topics that the user frequently mentions on social media. The timing of question acceptance can be adjusted based on the user's social media activity times. The reception unit prioritizes questions based on the user's social media friendships. This allows the reception unit to prioritize questions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, generative AI, or not using generative AI. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI perform question filtering.

[0075] 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 provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. For example, the analysis unit analyzes the user's facial expressions and provides a simple and easy-to-understand analysis result if the user is tense. If the user is relaxed, it provides a detailed analysis result. If the user is in a hurry, it provides a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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-described 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 user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during the analysis. For example, the analysis unit can perform a detailed analysis for questions of high importance. For example, the analysis unit can perform a concise analysis for questions of low importance. For example, the analysis unit can perform an analysis with a moderate level of detail for questions of medium importance. For example, the analysis unit can perform a detailed analysis for questions of high importance. For questions of low importance, it can perform a concise analysis. For questions of medium importance, it can perform an analysis with a moderate level of detail. By adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis results can be provided. Some or all of the above processing 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 question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, for questions about health, the analysis unit can apply an analysis algorithm that references a medical database. For questions about finance, the analysis unit can also apply an analysis algorithm that references a financial database. For questions about travel, the analysis unit can also apply an analysis algorithm that references a travel database. For example, for questions about health, the analysis unit can apply an analysis algorithm that references a medical database. For questions about finance, it can apply an analysis algorithm that references a financial database. For questions about travel, it can apply an analysis algorithm that references a travel database. By applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided. 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 question category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0078] 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 provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. For example, the analysis unit analyzes the user's facial expressions and provides a short, concise analysis result if the user is in a hurry. If the user is relaxed, it provides a detailed analysis result. If the user is excited, it provides an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0079] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions. The analysis unit may also postpone the analysis of older questions. The analysis unit may also give a moderate priority to questions that were submitted at a moderate time. For example, the analysis unit may prioritize the analysis of recently submitted questions, postpone the analysis of older questions, and give a moderate priority to questions that were submitted at a moderate time. By determining the priority of analysis based on when the questions were submitted, it is possible to provide more appropriate analysis results. 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 may input the question submission time data into a generative AI and have the generative AI determine the priority of analysis.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the questions during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant questions. For example, the analysis unit may postpone the analysis of less relevant questions. For example, the analysis unit may analyze questions of moderate relevance in an appropriate order. For example, the analysis unit may prioritize the analysis of highly relevant questions. Questions of low relevance may be postponed. Questions of moderate relevance may be analyzed in an appropriate order. By adjusting the order of analysis based on the relevance of the questions, more appropriate analysis results can be provided. 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 may input question relevance data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0081] 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 easily understandable response. If the user is relaxed, the service provider can also provide a detailed response. If the user is in a hurry, the service provider can also provide a concise and to-the-point response. For example, the service provider can analyze the user's facial expressions and provide a simple and easily understandable response if the user is nervous. If the user is relaxed, it can provide a detailed response. If the user is in a hurry, it can provide a concise and to-the-point response. This allows the service provider to provide a more appropriate response by adjusting the way it expresses its response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0082] The answering unit can adjust the level of detail in the answer based on the importance of the question when providing the answer. For example, the answering unit can provide a detailed answer to a highly important question. For example, the answering unit can provide a concise answer to a less important question. For example, the answering unit can provide an answer with a moderate level of detail to a moderately important question. For example, the answering unit can provide a detailed answer to a highly important question. For a less important question, it can provide a concise answer. For a moderately important question, it can provide an answer with a moderate level of detail. By adjusting the level of detail in the answer based on the importance of the question, a more appropriate answer can be provided. 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 importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the answer.

[0083] The answering unit can apply different answering algorithms depending on the category of the question when providing answers. For example, for questions about health, the answering unit can apply an answering algorithm that references a medical database. For questions about finance, the answering unit can also apply an answering algorithm that references a financial database. For questions about travel, the answering unit can also apply an answering algorithm that references a travel database. For example, for questions about health, the answering unit can apply an answering algorithm that references a medical database. For questions about finance, it can apply an answering algorithm that references a financial database. For questions about travel, it can apply an answering algorithm that references a travel database. By applying different answering algorithms depending on the category of the question, more appropriate answers can be provided. 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 category data into a generative AI and have the generative AI perform the application of the answering algorithm.

[0084] The service provider can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the service provider will provide a short, concise response. For example, if the user is relaxed, the service provider may also provide a detailed response. For example, if the user is excited, the service provider may also provide a response with visually stimulating effects. For example, the service provider analyzes the user's facial expressions and provides a short, concise response if the user is in a hurry. If the user is relaxed, it provides a detailed response. If the user is excited, it provides a response with visually stimulating effects. This allows for the provision of more appropriate responses by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described 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 user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0085] 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 recently submitted questions. For example, the service provider can postpone answers to older questions. For example, the service provider can provide answers with a moderate priority to questions that were submitted at a moderate time. For example, the service provider can prioritize answers to recently submitted questions. For older questions, it can postpone answers to questions that were submitted at a moderate time. For questions that were submitted at a moderate time, it can provide answers with a moderate priority. This allows for the provision of more appropriate 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 have the generative AI determine the priority of answers.

[0086] The answering unit can adjust the order of answers based on the relevance of the questions when providing answers. For example, the answering unit can prioritize answers to highly relevant questions. For example, the answering unit can postpone answers to less relevant questions. For example, the answering unit can provide answers in an appropriate order to questions of moderate relevance. For example, the answering unit can prioritize answers to highly relevant questions. For less relevant questions, it can postpone answers to questions of moderate relevance. By adjusting the order of answers based on the relevance of the questions, more appropriate answers can be provided. 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 have the generative AI adjust the order of answers.

[0087] The advertising department can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is relaxed, the advertising department can display visually appealing ads. If the user is in a hurry, the advertising department can also display concise and to-the-point ads. If the user is excited, the advertising department can also display visually stimulating ads. For example, the advertising department can analyze the user's facial expressions and display visually appealing ads if the user is relaxed, concise and to-the-point ads if the user is in a hurry, and visually stimulating ads if the user is excited. By adjusting how ads are displayed according to the user's emotions, more effective ad display becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using, for example, generative AI, or not using generative AI. For example, the advertising department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0088] The advertising department can select the most relevant ads by referring to the user's past ad click history when displaying ads. For example, the advertising department can display relevant ads based on ads the user has clicked in the past. The advertising department can also prioritize displaying ads in categories of interest based on the user's past ad click history. The advertising department can also analyze the user's past ad click history and select the most effective ads. For example, the advertising department can display relevant ads based on ads the user has clicked in the past. For example, it can prioritize displaying ads in categories of interest based on the user's past ad click history. For example, it can analyze the user's past ad click history and select the most effective ads. This allows for the display of more relevant ads by referring to the user's past ad click history. Some or all of the above processes in the advertising department may be performed using, for example, a generative AI, or not. For example, the advertising department can input the user's past ad click history data into a generative AI and have the generative AI select the most relevant ads.

[0089] The advertising department can estimate the user's emotions and adjust the timing of ad display based on the estimated emotions. For example, if the user is relaxed, the advertising department can display the ad slowly. If the user is in a hurry, the advertising department can also display the ad quickly. If the user is excited, the advertising department can also display the ad at a visually stimulating time. For example, the advertising department can analyze the user's facial expressions and display the ad slowly if the user is relaxed. If the user is in a hurry, the ad will be displayed quickly. If the user is excited, the ad will be displayed at a visually stimulating time. By adjusting the timing of ad display according to the user's emotions, more effective ad display becomes possible. 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 advertising department may be performed using generative AI, for example, or without generative AI. For example, the advertising department can input user emotion data into a generative AI and have the AI ​​perform emotion estimation.

[0090] The advertising department can display the most relevant advertisements by considering the user's geographical location when displaying ads. For example, if the user is in a specific region, the advertising department can display ads related to that region. For example, if the user is traveling, the advertising department can display ads related to the travel destination. For example, if the user is in a specific facility (e.g., a shopping mall), the advertising department can display ads related to that facility. For example, if the advertising department is in a specific region, it can display ads related to that region. If the user is traveling, it can display ads related to the travel destination. If the user is in a specific facility, it can display ads related to that facility. By considering the user's geographical location, it is possible to display more relevant advertisements. Some or all of the above processing in the advertising department may be performed using, for example, a generative AI, or not using a generative AI. For example, the advertising department can input the user's geographical location data into a generative AI and have the generative AI select the most relevant advertisements.

[0091] The measurement unit can estimate the user's emotions and adjust the method of measuring the time spent based on the estimated user emotions. For example, if the user is relaxed, the measurement unit can measure a detailed time spent. For example, if the user is in a hurry, the measurement unit can also measure a concise time spent. For example, if the user is excited, the measurement unit can also measure the time spent with visually stimulating effects added. For example, the measurement unit analyzes the user's facial expressions and measures a detailed time spent if the user is relaxed. If the user is in a hurry, it measures a concise time spent. If the user is excited, it measures the time spent with visually stimulating effects added. By adjusting the method of measuring the time spent according to the user's emotions, it becomes possible to measure the time spent more accurately. 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 measurement unit may be performed using a generative AI, for example, or without a generative AI. For example, the measurement unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0092] The measurement unit can select the optimal measurement method by referring to the user's past stay history when measuring dwell time. For example, the measurement unit can measure detailed dwell time based on places where the user has stayed for a long time in the past. For example, the measurement unit can also prioritize measuring dwell time at places of interest based on the user's past stay history. For example, the measurement unit can analyze the user's past stay history and select the most effective measurement method. For example, the measurement unit can measure detailed dwell time based on places where the user has stayed for a long time in the past. For example, it can prioritize measuring dwell time at places of interest based on the user's past stay history. For example, it can analyze the user's past stay history and select the most effective measurement method. This makes it possible to measure dwell time more accurately by referring to the user's past stay history. Some or all of the above processing in the measurement unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the measurement unit can input the user's past stay history data into a generation AI and have the generation AI select the optimal measurement method.

[0093] The measurement unit can estimate the user's emotions and adjust the frequency of measuring dwell time based on the estimated user emotions. For example, if the user is relaxed, the measurement unit will measure dwell time frequently. For example, if the user is in a hurry, the measurement unit can also reduce the frequency of measuring dwell time. For example, if the user is excited, the measurement unit can also measure dwell time with visually stimulating effects added. For example, the measurement unit analyzes the user's facial expressions and measures dwell time frequently if the user is relaxed. If the user is in a hurry, it reduces the frequency of measuring dwell time. If the user is excited, it measures dwell time with visually stimulating effects added. By adjusting the frequency of measuring dwell time according to the user's emotions, more accurate measurement of dwell time becomes possible. 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 measurement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the measurement unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The measurement unit can select the optimal measurement method when measuring dwell time, taking into account the user's device information. For example, if the user is using a smartphone, the measurement unit will select a measurement method that takes into account the device's battery consumption. For example, if the user is using a tablet, the measurement unit can also select a measurement method optimized for a large screen. For example, if the user is using a smartwatch, the measurement unit can also select a simple and highly visible measurement method. By taking into account the user's device information, it becomes possible to measure dwell time more accurately. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input user device information data into a generative AI and have the generative AI select the optimal measurement method.

[0095] The measurement unit can select the optimal measurement method when measuring the time spent, taking into account the user's current activity status. For example, if the user is walking, the measurement unit will select a measurement method suitable for walking. For example, if the user is sitting, the measurement unit can also select a measurement method suitable for sitting. For example, if the user is exercising, the measurement unit can also select a measurement method suitable for exercise. For example, if the user is walking, the measurement unit will select a measurement method suitable for walking. If the user is sitting, the measurement unit will select a measurement method suitable for sitting. If the user is exercising, the measurement unit will select a measurement method suitable for exercise. This makes it possible to measure the time spent more accurately by taking into account the user's current activity status. Some or all of the above processing in the measurement unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the measurement unit can input the user's current activity status data into a generation AI and have the generation AI select the optimal measurement method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can refer to the user's past question history when receiving a user's question and automatically suggest similar questions. For example, if a user previously asked about "bank opening hours," the reception desk can refer to that history and automatically suggest information about "bank opening hours" when a similar question is entered again. Also, if a user previously asked about "how to make a restaurant reservation," the reception desk can use that history to suggest related information such as "restaurant menus" and "restaurant locations." This makes it possible to resolve questions more quickly and accurately by utilizing the user's past question history. Some or all of the above processing in 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 the user's past question history data into a generation AI and have the generation AI suggest similar questions.

[0098] The advertising department can estimate the user's emotions and adjust the content of advertisements based on those emotions. For example, if a user is stressed, advertisements for products or services with relaxing effects can be displayed. If a user is excited, advertisements related to entertainment or activities can be displayed. If a user is relaxed, advertisements related to travel or relaxation can be displayed. This maximizes the effectiveness of advertisements by providing content that matches the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the advertising department may be performed using generative AI, or not. For example, the advertising department can input user emotion data into a generative AI and have the generative AI adjust the advertisement content.

[0099] The measurement unit can adjust the measurement method based on the type of device the user is using when measuring the user's dwell time. For example, if the user is using a smartphone, the measurement frequency can be set lower to conserve battery power. If the user is using a tablet, a measurement method optimized for the large screen can be selected. If the user is using a smartwatch, a simple and highly visible measurement method can be selected. By providing the optimal measurement method according to the user's device, more accurate measurement of dwell time becomes possible. Some or all of the above processing in the measurement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the measurement unit can input user device information data into a generation AI and have the generation AI select the optimal measurement method.

[0100] The service provider can estimate the user's emotions and adjust the way it expresses its responses based on those emotions. For example, if the user is nervous, it can provide a simple and easily understandable response. If the user is relaxed, it can provide a more detailed response. If the user is in a hurry, it can provide a concise and to-the-point response. This allows the service provider to improve user satisfaction by providing the most appropriate response based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the service provider may be performed using generative AI, or it may be performed without generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way it expresses its responses.

[0101] The reception desk can provide relevant information when receiving user inquiries, taking into account the user's current geographical location. For example, if a user is in a specific region, inquiries related to that region can be prioritized. If a user is traveling, inquiries related to their travel destination can also be prioritized. Furthermore, if a user is in a specific facility (e.g., a hospital or bank), inquiries related to that facility can be prioritized. This allows for the reception desk to receive more relevant inquiries by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location data into a generative AI and have the generative AI perform the filtering of inquiries.

[0102] 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 nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can also provide a detailed analysis result. If the user is in a hurry, it can provide a concise analysis result that gets straight to the point. This allows for improved user satisfaction by providing the most appropriate analysis result according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0103] The answering unit can select the most appropriate answer by referring to the user's past question history when providing an answer. For example, if a user previously asked about "bank opening hours," the answering unit can refer to that history and automatically provide information about "bank opening hours" when a similar question is entered again. Also, if a user previously asked about "how to make a restaurant reservation," the answering unit can use that history to provide related information such as "restaurant menus" and "restaurant locations." This allows for faster and more accurate answers to be provided by utilizing the user's past question history. Some or all of the above processing in the answering unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the answering unit can input the user's past question history data into a generation AI and have the generation AI select the most appropriate answer.

[0104] The advertising department can estimate the user's emotions and adjust the timing of ad display based on those emotions. For example, if the user is relaxed, the ad can be displayed slowly. If the user is in a hurry, the ad can be displayed quickly. If the user is excited, the ad can be displayed at a visually stimulating moment. This maximizes the effectiveness of the ad by providing the optimal ad display timing according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the advertising department may be performed using generative AI, or not. For example, the advertising department can input user emotion data into a generative AI and have the generative AI adjust the timing of ad display.

[0105] The measurement unit can select the optimal measurement method when measuring the user's dwell time, taking into account the user's current activity status. For example, if the user is walking, it can select a measurement method suitable for walking. If the user is sitting, it can also select a measurement method suitable for sitting. Furthermore, if the user is exercising, it can also select a measurement method suitable for exercise. This allows for more accurate measurement of dwell time by considering the user's current activity status. Some or all of the above processing in the measurement unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the measurement unit can input the user's current activity status data into a generation AI and have the generation AI select the optimal measurement method.

[0106] The service provider can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, to-the-point response. If the user is relaxed, it can provide a detailed response. If the user is excited, it can provide a response with visually stimulating effects. This improves user satisfaction by providing the optimal response length according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the response.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives user inquiries. User inquiries include questions, inquiries, and feedback. The reception desk can accept inquiries in text, audio, and image formats. For example, text inquiries are accepted as is, audio inquiries are converted to text using speech recognition technology, and image inquiries are converted to text using image analysis technology before being accepted. Step 2: The analysis unit analyzes the questions received by the reception unit. The analysis is performed using generative AI and employs methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis techniques are used to analyze the content of the questions, data mining techniques are used to analyze the patterns of the questions, and machine learning algorithms are used to classify the questions. Step 3: The provisioning unit provides the answer based on the results analyzed by the analysis unit. The answer is provided in various formats, such as text, audio, and image. For example, the answer is generated in text format, the audio answer is generated using text-to-speech technology, and the image answer is generated using text-to-image technology.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, advertising unit, and measurement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user inquiries. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the inquiries using generated AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides answers based on the analysis results. The advertising unit is implemented by the display 40A of the smart device 14 and displays advertisements. The measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the user's dwell time. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, advertising unit, and measurement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's questions. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the questions using generated AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides answers based on the analysis results. The advertising unit is implemented by the display of the smart glasses 214 and displays advertisements. The measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and measures the user's dwell time. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, advertising unit, and measurement unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user inquiries. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the inquiries using generated AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides answers based on the analysis results. The advertising unit is implemented by the display of the headset terminal 314 and displays advertisements. The measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the user's dwell time. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, advertising unit, and measurement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user inquiries. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the inquiries using generated AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides answers based on the analysis results. The advertising unit is implemented by the display of the robot 414 and displays advertisements. The measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the user's stay time. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk to receive user inquiries, An analysis unit analyzes the questions received by the reception unit, The system includes a providing unit that provides an answer based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) It has an advertising section for displaying advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a measurement unit that measures the user's dwell time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, To provide appropriate answers to user questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We estimate the user's emotions and adjust the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of contact. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When a question is submitted, it is filtered based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a question, the system prioritizes receiving questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, adjust the order of analysis based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned supply unit is, When providing an answer, adjust the level of detail in the answer based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing answers, different answer algorithms are applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned supply unit is, When providing answers, we will prioritize responses based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing answers, adjust the order of the answers based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advertising department, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned advertising department, When displaying ads, the system selects the most suitable ad by referring to the user's past ad click history. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned advertising department, It estimates the user's emotions and adjusts the timing of ad display based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned advertising department, When displaying ads, the system takes into account the user's geographical location to show the most relevant ads. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned measuring unit is We estimate the user's emotions and adjust the method of measuring dwell time based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned measuring unit is When measuring dwell time, the system selects the optimal measurement method by referring to the user's past dwell history. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the frequency of measuring dwell time based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned measuring unit is When measuring dwell time, the optimal measurement method is selected by considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned measuring unit is When measuring dwell time, the optimal measurement method is selected considering the user's current activity status. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk to receive user inquiries, An analysis unit analyzes the questions received by the reception unit, The system includes a providing unit that provides an answer based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. It has an advertising section for displaying advertisements. The system according to feature 1.

3. It includes a measurement unit that measures the user's dwell time. The system according to feature 1.

4. The aforementioned supply unit is, To provide appropriate answers to user questions. The system according to feature 1.

5. The aforementioned reception unit is We estimate the user's emotions and adjust the timing of question submissions based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of contact. The system according to feature 1.

7. The aforementioned reception unit is When a question is submitted, it is filtered based on the user's current situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to accept based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving a question, the system prioritizes receiving questions that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system according to feature 1.

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