system

Abstract

The system according to the embodiment aims to improve the efficiency of responses to inquiries from users. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a user. The analysis unit analyzes the content of the inquiry received by the reception unit. The generation unit generates an answer based on the content analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that responses to user inquiries are dispersed, making it difficult to respond efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of responses to inquiries from users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a user. The analysis unit analyzes the content of the inquiry received by the reception unit. The generation unit generates an answer based on the content analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of responses to inquiries from users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An inquiry platform according to an embodiment of the present invention is a system that unifies the contact point for inquiries across an entire group and allows anyone to make inquiries anytime, anywhere via chat. In this system, users submit inquiries via chat, and AI analyzes the inquiry content and provides appropriate answers. For example, a user inputs a question in text format, and AI analyzes the question, generates an answer, and provides it to the user via chat. This allows users to obtain quick and accurate answers. Furthermore, by analyzing the inquiry content, AI can grasp the trends and frequency of inquiries and provide data for creating FAQs for frequently asked questions. This reduces the burden of responding to inquiries and improves work efficiency. This allows the inquiry platform to reduce the time group employees spend in a "trouble" state and improve work efficiency. For example, productivity can be improved by instantly resolving problems that arise during work. Furthermore, the burden of responding to inquiries can be reduced by creating FAQs for frequently asked questions and allowing users to solve them themselves.

[0029] The inquiry platform according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a user. The inquiry from the user may include, but is not limited to, text, voice, or image. The reception unit may receive, for example, a question input by the user in text format. The reception unit may also receive voice input or image input. The analysis unit analyzes the inquiry received by the reception unit. The analysis may be performed using, for example, but is not limited to, natural language processing technology. For example, the analysis unit may analyze text using morphological analysis. The analysis unit may also analyze sentence structure using grammatical analysis. The analysis unit may also understand the meaning of a sentence using semantic analysis. The generation unit generates an answer based on the content analyzed by the analysis unit. The answer may be generated in, for example, text format, but is not limited to, the example. For example, the generation unit may generate text using natural language generation technology. The generation unit may also generate an answer in voice format using speech synthesis technology. The generation unit may also generate an answer in image format using image generation technology. The providing unit provides the answer generated by the generating unit. The answer is provided, for example, in a chat format, but is not limited to this example. For example, the providing unit provides the answer using real-time chat. The providing unit can also provide the answer using bot chat. The providing unit can also provide the answer using email or notification. This allows the inquiry platform according to the embodiment to efficiently accept and analyze inquiries from users, generate and provide answers.

[0030] The analysis unit can understand the intent of the question and generate an appropriate answer using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit analyzes text using, for example, morphological analysis. For example, morphological analysis divides text into words and identifies the part of speech of each word. The analysis unit can also analyze the structure of a sentence using grammatical analysis. For example, grammatical analysis analyzes the structure of a sentence and identifies elements of the sentence, such as a subject, predicate, and object. The analysis unit can also understand the meaning of a sentence using semantic analysis. For example, semantic analysis analyzes the meaning of a sentence and understands its intent. As a result, the analysis unit can accurately understand the intent of a question and generate an appropriate answer by using natural language processing technology.

[0031] The providing unit can provide the generated answer to the user in a chat format. Examples of chat formats include, but are not limited to, real-time chat and bot chat. The providing unit can provide the answer using, for example, real-time chat. For example, real-time chat is a format in which the user and the system exchange messages in real time. The providing unit can also provide the answer using bot chat. For example, bot chat is a format in which the system automatically generates a message and provides it to the user. The providing unit can also provide the answer using email or notification. For example, the providing unit can send the generated answer to the user by email. The providing unit can also provide the generated answer to the user as a notification. In this way, the providing unit can provide the generated answer to the user in a chat format, allowing the user to quickly obtain the answer.

[0032] The analysis unit can grasp the trends and frequency of inquiries and provide data for creating FAQs for frequently asked questions. Understanding trends includes, but is not limited to, frequency analysis and trend analysis. The analysis unit can grasp the trends of inquiries using, for example, frequency analysis. For example, frequency analysis analyzes how frequently a specific inquiry occurs. The analysis unit can also grasp the trends of inquiries using trend analysis. For example, trend analysis analyzes the occurrence pattern of inquiries and understands changes over time. Measurement of frequency includes, but is not limited to, the number of occurrences, time units, etc. The analysis unit can measure the frequency of inquiries using, for example, the number of occurrences. For example, the number of occurrences measures how many times a specific inquiry occurs within a certain period of time. The analysis unit can also measure the frequency of inquiries using time units. For example, the time units measure how often a specific inquiry occurs in a specific time period. This allows the analysis unit to grasp the trends and frequency of inquiries and provide data for creating FAQs for frequently asked questions.

[0033] The providing unit can provide FAQs to allow users to solve their own problems. FAQs include, but are not limited to, question and answer pairs and categorization. The providing unit can provide FAQs using, for example, question and answer pairs. For example, question and answer pairs are a format that provides specific answers to specific questions. The providing unit can also provide FAQs using categorization. For example, categorization is a format that classifies questions into specific categories so that users can easily find the information they need. In this way, the providing unit can provide FAQs to allow users to solve their own problems, thereby reducing the burden of responding to inquiries.

[0034] The reception unit may enable a user to make an inquiry. The inquiry may include, but is not limited to, for example, text, voice, or image. The reception unit may, for example, accept a question input by the user in text format. For example, the user may input a question in text format and transmit it to the system. The reception unit may also accept voice input. For example, the user may input a question in voice format and transmit it to the system. The reception unit may also accept image input. For example, the user may input a question in image format and transmit it to the system. This allows the reception unit to enable a user to make an inquiry anytime, anywhere, and to quickly resolve problems.

[0035] The reception unit can analyze the user's past inquiry history and select a reception method. The inquiry history includes, for example, past inquiry content, date and time, response results, etc., but is not limited to these examples. The reception unit, for example, analyzes the past inquiry content and selects a reception method. For example, the reception unit automatically displays inquiries frequently made by the user in the past as candidates. The reception unit can also analyze the date and time of past inquiries and select a reception method. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also analyze past response results and select a reception method. For example, the reception unit predicts and suggests an inquiry method to be used in a specific time period based on the user's past inquiry history. This allows the reception unit to analyze the user's past inquiry history and select the optimal reception method, enabling efficient response. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal reception method.

[0036] When receiving an inquiry, the reception unit may perform filtering based on the user's current situation and areas of interest. The current situation may include, but is not limited to, the user's location information and device information. The reception unit may perform filtering based on, for example, the user's location information. For example, the reception unit may preferentially receive inquiries related to the user's current work. The reception unit may also perform filtering based on the user's device information. For example, the reception unit may preferentially receive inquiries related to the device the user is using. Areas of interest may include, but are not limited to, past search history and browsing history. The reception unit may perform filtering based on the user's past search history. For example, the reception unit may filter related inquiries based on the user's areas of interest. The reception unit may also perform filtering based on the user's past browsing history. For example, the reception unit may suggest optimal inquiry content depending on the user's current situation (time zone, location, etc.). This allows the reception unit to perform filtering based on the user's current situation and areas of interest, thereby enabling a more appropriate response to the inquiry. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's location information data to the generation AI and have the generation AI perform filtering.

[0037] The reception unit can select a reception means depending on the user's input method when receiving an inquiry. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when a user makes a voice inquiry, the reception unit uses voice recognition technology to receive the inquiry. For example, the voice recognition technology converts the user's voice into text and inputs it into the system. Furthermore, when a user makes a text inquiry, the reception unit can preferentially receive the text input. For example, the user inputs a question in text format and sends it to the system. Furthermore, when a user makes an inquiry using an image, the reception unit can also use image recognition technology to receive the inquiry. For example, the image recognition technology analyzes the image sent by the user and understands the inquiry content. This allows the reception unit to select the optimal reception means depending on the user's input method when receiving the inquiry, thereby enabling efficient inquiry reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's voice data into a generation AI and have the generation AI perform voice recognition.

[0038] When accepting an inquiry, the reception unit can prioritize receiving inquiries that are highly relevant by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the reception unit prioritizes receiving inquiries related to that area. For example, when the user is in a specific area, the reception unit prioritizes receiving inquiries related to that area. Furthermore, when the user is traveling, the reception unit can suggest optimal inquiry content based on the user's current location. For example, when the user is traveling, the reception unit suggests optimal inquiry content based on the user's current location. Furthermore, when the user is in a specific facility, the reception unit can prioritize receiving inquiries related to that facility. For example, when the user is in a specific facility, the reception unit prioritizes receiving inquiries related to that facility. As a result, when accepting inquiries, the reception unit prioritizes receiving inquiries that are highly relevant by taking into account the user's geographical location information, thereby enabling the reception unit to prioritize responding to highly relevant inquiries. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant inquiries.

[0039] The reception unit may analyze the user's social media activity when receiving an inquiry and receive related inquiries. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit may preferentially receive related inquiries based on, for example, content mentioned by the user on social media. For example, the reception unit may preferentially receive related inquiries based on the content mentioned by the user on social media. The reception unit may also analyze the user's social media activity and suggest related inquiries. For example, the reception unit may analyze the user's social media activity and suggest related inquiries. The reception unit may also receive related inquiries by referring to the activities of the user's friends on social media. For example, the reception unit may receive related inquiries by referring to the activities of the user's friends on social media. In this way, the reception unit may analyze the user's social media activity when receiving an inquiry and receive related inquiries, thereby efficiently responding to related inquiries. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI select related inquiries.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an inquiry. Feedback includes, but is not limited to, survey results, user comments, and the like. The reception unit can, for example, propose an optimal reception method based on feedback previously provided by the user. For example, the reception unit proposes an optimal reception method based on feedback previously provided by the user. The reception unit can also preferentially select a specific reception method based on the user's past feedback. For example, the reception unit preferentially selects a specific reception method based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. As a result, the reception unit can provide an optimal reception method by customizing the reception method by reflecting the user's past feedback when receiving an inquiry. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and cause the generation AI to customize the reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the query. The importance includes, but is not limited to, for example, urgency and scope of impact. The analysis unit adjusts the level of detail of the analysis based on, for example, urgency. For example, the analysis unit performs a detailed analysis for queries with high importance. The analysis unit can also adjust the level of detail of the analysis based on the scope of impact. For example, the analysis unit performs a concise analysis for queries with low importance. The analysis unit can also adjust the priority of the analysis based on the importance. For example, the analysis unit prioritizes analysis for queries with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the query. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input query importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. Examples of categories include, but are not limited to, technical categories and business categories. For example, the analysis unit applies a specialized analysis algorithm to technical categories. For example, the analysis unit applies a specialized analysis algorithm to technical inquiries. The analysis unit can also apply a simple analysis algorithm to business categories. For example, the analysis unit applies a simple analysis algorithm to general inquiries. The analysis unit can also select and apply an optimal analysis algorithm for each category. For example, the analysis unit selects and applies an optimal analysis algorithm for each category. This enables the analysis unit to apply an optimal analysis algorithm depending on the category of the inquiry, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input inquiry category data to a generation AI and have the generation AI select an analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Analysis results include, for example, past analysis content, date and time, and results, but are not limited to these examples. The analysis unit can improve the accuracy of the analysis by referring to, for example, the past analysis content. For example, the analysis unit performs highly accurate analysis of similar queries based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the date and time of past analysis. For example, the analysis unit analyzes the user's past analysis results and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. As a result, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission time of the inquiry. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit determines the analysis priority based on, for example, the submission date and time. For example, the analysis unit prioritizes analysis for urgent inquiries. The analysis unit can also determine the analysis priority based on the submission frequency. For example, the analysis unit determines the analysis priority for normal inquiries based on the submission time. The analysis unit can also adjust the analysis schedule based on the submission time. For example, the analysis unit adjusts the analysis schedule based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the query. Relevance includes, but is not limited to, similarity of content and related keywords. The analysis unit adjusts the order of analysis based on, for example, similarity of content. For example, the analysis unit prioritizes analysis of highly relevant queries. The analysis unit can also adjust the order of analysis based on related keywords. For example, the analysis unit postpones analysis of less relevant queries. The analysis unit can also adjust the order of analysis based on relevance. For example, the analysis unit adjusts the order of analysis based on relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the query. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of technical terminology include, but are not limited to, credentials and past inquiries. The analysis unit can adjust the use of technical terms in the analysis based on, for example, credentials. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. The analysis unit can also adjust the use of technical terms in the analysis based on past inquiries. For example, if the user does not have technical expertise, the analysis unit can provide analysis results in simpler language. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are presented according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the inquiry. The importance includes, but is not limited to, for example, urgency and scope of impact. The generation unit adjusts the level of detail of the answer based on, for example, urgency. For example, the generation unit provides a detailed answer to an inquiry with a high importance. The generation unit can also adjust the level of detail of the answer based on the scope of impact. For example, the generation unit provides a concise answer to an inquiry with a low importance. The generation unit can also adjust the priority of the answer according to the importance. For example, the generation unit generates answers preferentially for inquiries with a high importance. This allows the generation unit to adjust the level of detail of the answer based on the importance of the inquiry, thereby enabling efficient answers. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input inquiry importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0048] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the inquiry. Examples of categories include, but are not limited to, technical categories and business categories. For example, the generation unit applies a specialized generation algorithm to technical categories. For example, the generation unit can apply a specialized generation algorithm to technical inquiries. The generation unit can also apply a simple generation algorithm to business categories. For example, the generation unit can apply a simple generation algorithm to general inquiries. The generation unit can also select and apply an optimal generation algorithm for each category. For example, the generation unit selects and applies an optimal generation algorithm for each category. This enables the generation unit to apply an optimal generation algorithm depending on the category of the inquiry, thereby enabling an efficient answer. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input inquiry category data to the generation AI and cause the generation AI to select a generation algorithm.

[0049] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The answer results include, for example, past answer content, date and time, and result, but are not limited to these examples. The generation unit improves the accuracy of the answer by referring to the past answer content. For example, the generation unit provides highly accurate answers to similar inquiries based on the user's past answer results. The generation unit can also improve the accuracy of the answer by referring to the date and time of the past answer. For example, the generation unit analyzes the user's past answer results and optimizes the generation algorithm. The generation unit can also improve the accuracy of the answer by referring to the past answer results. For example, the generation unit improves the accuracy of the answer by referring to the user's past answer results. As a result, the generation unit improves the accuracy of the answer by referring to the user's past answer results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0050] When generating answers, the generation unit can determine the priority of answers based on the submission time of the inquiry. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The generation unit determines the priority of answers based on, for example, the submission date and time. For example, the generation unit generates answers with priority for urgent inquiries. The generation unit can also determine the priority of answers based on the submission frequency. For example, the generation unit determines the priority of answers for normal inquiries based on the submission time. The generation unit can also adjust the response schedule according to the submission time. For example, the generation unit adjusts the response schedule according to the submission time. This enables the generation unit to determine the priority of answers based on the submission time of the inquiry, thereby enabling efficient responses. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input submission time data into the generation AI and have the generation AI determine the priority of answers.

[0051] When generating an answer, the generation unit can adjust the order of answers based on the relevance of the inquiry. Relevance includes, but is not limited to, similarity of content and related keywords. The generation unit adjusts the order of answers based on similarity of content, for example. For example, the generation unit prioritizes generating answers for inquiries with high relevance. The generation unit can also adjust the order of answers based on related keywords. For example, the generation unit postpones generating answers for inquiries with low relevance. The generation unit can also adjust the order of answers based on relevance. For example, the generation unit adjusts the order of answers based on relevance. This enables the generation unit to adjust the order of answers based on the relevance of the inquiry, thereby enabling efficient answers. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0052] When generating an answer, the generation unit can adjust the use of technical terms in the answer based on the user's level of expertise. Examples of the level of expertise include, but are not limited to, qualifications and past inquiries. The generation unit can adjust the use of technical terms in the answer based on qualifications. For example, if the user has specialized knowledge, the generation unit provides an answer that uses a lot of technical terms. The generation unit can also adjust the use of technical terms in the answer based on past inquiries. For example, if the user does not have specialized knowledge, the generation unit can provide an answer in simple language. The generation unit can also adjust the way the answer is expressed based on the user's level of expertise. For example, the generation unit adjusts the way the answer is expressed based on the user's level of expertise. This allows the generation unit to provide an answer that is easier to understand by adjusting the use of technical terms in the answer based on the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] When providing an answer, the providing unit can select a delivery method by referring to the user's past inquiry history. The inquiry history includes, for example, past inquiry content, date and time, response results, etc., but is not limited to these examples. The providing unit selects a delivery method by referring to, for example, the content of the past inquiry. For example, the providing unit selects an optimal delivery method based on delivery methods used by the user in the past. The providing unit can also select a delivery method by referring to the date and time of past inquiries. For example, the providing unit suggests an optimal delivery method based on the user's past inquiry history. The providing unit can also select a delivery method by referring to past response results. For example, the providing unit analyzes the user's past inquiry history and optimizes the delivery method. This allows the providing unit to select an optimal delivery method by referring to the user's past inquiry history, enabling efficient answer provision. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past inquiry history data into the generation AI and have the generation AI select a delivery method.

[0054] When providing an answer, the providing unit can customize the content to be provided based on the user's current situation. The current situation includes, but is not limited to, the user's location information and device information. The providing unit customizes the content to be provided based on, for example, the user's location information. For example, the providing unit prioritizes providing answers related to the user's current task. The providing unit can also customize the content to be provided based on the user's device information. For example, the providing unit suggests optimal content to be provided based on the user's current situation (time of day, location, etc.). The providing unit can also customize the content to be provided taking into account the user's current situation. For example, the providing unit customizes the content to be provided taking into account the user's current situation. In this way, the providing unit can provide a more appropriate answer by customizing the content to be provided based on the user's current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's location information data to the generation AI and cause the generation AI to customize the content to be provided.

[0055] The providing unit can improve the answer providing method by reflecting user feedback when providing an answer. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit improves the answer providing method based on, for example, user feedback. For example, the providing unit improves the answer providing method based on user feedback. The providing unit can also preferentially select a specific answer providing method based on user feedback. For example, the providing unit preferentially selects a specific answer providing method based on user feedback. The providing unit can also customize the answer providing interface by reflecting user feedback. For example, the providing unit customizes the answer providing interface by reflecting user feedback. This allows the providing unit to improve the answer providing method by reflecting user feedback when providing an answer, thereby providing a more appropriate answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the answer providing method.

[0056] The providing unit can select a providing method by taking into consideration the user's geographical location information when providing an answer. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the providing unit prioritizes providing answers related to that area. For example, when the user is in a specific area, the providing unit prioritizes providing answers related to that area. Furthermore, when the user is traveling, the providing unit can also suggest optimal content to be provided based on the user's current location. For example, when the user is traveling, the providing unit suggests optimal content to be provided based on the user's current location. Furthermore, when the user is in a specific facility, the providing unit can also prioritize providing answers related to that facility. For example, when the user is in a specific facility, the providing unit prioritizes providing answers related to that facility. Thus, when providing an answer, the providing unit can select a providing method by taking into consideration the user's geographical location information, thereby providing a highly relevant answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and have the generating AI select the providing method.

[0057] The providing unit can analyze the user's social media activity and suggest a means of providing an answer when providing an answer. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit can, for example, prioritize providing relevant answers based on content mentioned by the user on social media. For example, the providing unit can prioritize providing relevant answers based on content mentioned by the user on social media. The providing unit can also analyze the user's social media activity and suggest relevant answer content. For example, the providing unit can analyze the user's social media activity and suggest relevant answer content. The providing unit can also provide relevant answers by referring to the activity of the user's friends on social media. For example, the providing unit can provide relevant answers by referring to the activity of the user's friends on social media. In this way, the providing unit can efficiently provide relevant answers by analyzing the user's social media activity and suggesting a means of providing an answer when providing an answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into the generating AI and have the generating AI suggest a means of providing the data.

[0058] The providing unit can customize the answer providing method by reflecting the user's past feedback when providing an answer. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit can, for example, propose an optimal answer providing method based on feedback previously provided by the user. For example, the providing unit proposes an optimal answer providing method based on feedback previously provided by the user. The providing unit can also preferentially select a specific answer providing method based on the user's past feedback. For example, the providing unit preferentially selects a specific answer providing method based on the user's past feedback. The providing unit can also customize the answer providing interface by reflecting the user's feedback. For example, the providing unit customizes the answer providing interface by reflecting the user's feedback. As a result, the providing unit can provide an optimal answer providing method by customizing the answer providing method by reflecting the user's past feedback when providing an answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the answer providing method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past inquiry history and understand what kind of inquiries the user has made in the past. For example, it can automatically display inquiries that the user has made frequently in the past as candidates. The reception unit can also analyze the date and time of past inquiries, and if the user tends to make inquiries during a specific time period, it can respond to the inquiry in accordance with that time period. Furthermore, the reception unit can analyze the results of past inquiries and accumulate data for providing answers that satisfy the user. This allows the reception unit to utilize the user's past inquiry history to provide a more appropriate response.

[0061] The providing unit can adjust the answer providing method based on the user's current situation. For example, if the user is on the move, the answer can be provided in voice format. If the user is in a desktop environment, the answer can be provided in detailed text format. Furthermore, the providing unit can select the optimal answer providing method based on the user's device information. For example, if the user is using a smartphone, the answer can be provided in the form of a simple message, and if the user is using a tablet, the answer can be provided using an interactive graphical interface. This allows the providing unit to select the optimal answer providing method depending on the user's current situation.

[0062] The analysis unit can refer to relevant external databases based on the content of the inquiry to provide more accurate analysis results. For example, for technical inquiries, a specialized technical database can be referenced. For business-related inquiries, an industry trend database can be referenced. Furthermore, the analysis unit can automatically select relevant external databases based on the user's past inquiry history. This allows the analysis unit to utilize external databases to provide more accurate and reliable analysis results.

[0063] The reception unit can customize the inquiry reception method based on the user's current situation and areas of interest. For example, if the user is interested in a specific project, inquiries related to that project can be received with priority. Also, if the user tends to make inquiries during a specific time period, the reception unit can respond to inquiries according to that time period. Furthermore, the reception unit can automatically suggest related inquiries based on the user's past search history and browsing history. This allows the reception unit to provide the optimal inquiry reception method according to the user's current situation and areas of interest.

[0064] The providing unit can improve the answer providing method by reflecting the user's past feedback. For example, the providing unit can suggest the optimal answer providing method based on the user's past feedback. The providing unit can also preferentially select a specific answer providing method based on the user's feedback. Furthermore, the providing unit can customize the providing interface by reflecting the user's feedback. In this way, the providing unit can utilize the user's past feedback to provide a more appropriate answer providing method.

[0065] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, based on the content of a user's past inquiries and the analysis results thereof, the analysis unit can perform highly accurate analysis of similar inquiries. The analysis unit can also analyze past analysis results and optimize the analysis algorithm. Furthermore, the analysis unit can determine the priority of analysis by referring to the user's past analysis results. This allows the analysis unit to utilize the user's past analysis results to perform more accurate and efficient analysis.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives an inquiry from a user. The inquiry from the user may include, but is not limited to, text, voice, or image. For example, the reception unit receives a question input by the user in text format. The reception unit can also receive voice input or image input. Step 2: The analysis unit analyzes the inquiry received by the reception unit. The analysis is performed using, for example, natural language processing technology, but is not limited to this example. For example, the analysis unit analyzes the text using morphological analysis. The analysis unit can also analyze the structure of the sentence using grammatical analysis. The analysis unit can also understand the meaning of the sentence using semantic analysis. Step 3: The generation unit generates an answer based on the content analyzed by the analysis unit. The answer is generated in text format, for example, but is not limited to this example. For example, the generation unit generates text using natural language generation technology. The generation unit can also generate an answer in audio format using speech synthesis technology. The generation unit can also generate an answer in image format using image generation technology. Step 4: The providing unit provides the answer generated by the generating unit. The answer is provided, for example, in a chat format, but is not limited to this example. For example, the providing unit provides the answer using real-time chat. The providing unit can also provide the answer using bot chat. The providing unit can also provide the answer using email or notification.

[0068] (Example 2) An inquiry platform according to an embodiment of the present invention is a system that unifies the contact point for inquiries across an entire group and allows anyone to make inquiries anytime, anywhere via chat. In this system, users submit inquiries via chat, and AI analyzes the inquiry content and provides appropriate answers. For example, a user inputs a question in text format, and AI analyzes the question, generates an answer, and provides it to the user via chat. This allows users to obtain quick and accurate answers. Furthermore, by analyzing the inquiry content, AI can grasp the trends and frequency of inquiries and provide data for creating FAQs for frequently asked questions. This reduces the burden of responding to inquiries and improves work efficiency. This allows the inquiry platform to reduce the time group employees spend in a "trouble" state and improve work efficiency. For example, productivity can be improved by instantly resolving problems that arise during work. Furthermore, the burden of responding to inquiries can be reduced by creating FAQs for frequently asked questions and allowing users to solve them themselves.

[0069] The inquiry platform according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a user. The inquiry from the user may include, but is not limited to, text, voice, or image. The reception unit may receive, for example, a question input by the user in text format. The reception unit may also receive voice input or image input. The analysis unit analyzes the inquiry received by the reception unit. The analysis may be performed using, for example, but is not limited to, natural language processing technology. For example, the analysis unit may analyze text using morphological analysis. The analysis unit may also analyze sentence structure using grammatical analysis. The analysis unit may also understand the meaning of a sentence using semantic analysis. The generation unit generates an answer based on the content analyzed by the analysis unit. The answer may be generated in, for example, text format, but is not limited to, the example. For example, the generation unit may generate text using natural language generation technology. The generation unit may also generate an answer in voice format using speech synthesis technology. The generation unit may also generate an answer in image format using image generation technology. The providing unit provides the answer generated by the generating unit. The answer is provided, for example, in a chat format, but is not limited to this example. For example, the providing unit provides the answer using real-time chat. The providing unit can also provide the answer using bot chat. The providing unit can also provide the answer using email or notification. This allows the inquiry platform according to the embodiment to efficiently accept and analyze inquiries from users, generate and provide answers.

[0070] The analysis unit can understand the intent of the question and generate an appropriate answer using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit analyzes text using, for example, morphological analysis. For example, morphological analysis divides text into words and identifies the part of speech of each word. The analysis unit can also analyze the structure of a sentence using grammatical analysis. For example, grammatical analysis analyzes the structure of a sentence and identifies elements of the sentence, such as a subject, predicate, and object. The analysis unit can also understand the meaning of a sentence using semantic analysis. For example, semantic analysis analyzes the meaning of a sentence and understands its intent. As a result, the analysis unit can accurately understand the intent of a question and generate an appropriate answer by using natural language processing technology.

[0071] The providing unit can provide the generated answer to the user in a chat format. Examples of chat formats include, but are not limited to, real-time chat and bot chat. The providing unit can provide the answer using, for example, real-time chat. For example, real-time chat is a format in which the user and the system exchange messages in real time. The providing unit can also provide the answer using bot chat. For example, bot chat is a format in which the system automatically generates a message and provides it to the user. The providing unit can also provide the answer using email or notification. For example, the providing unit can send the generated answer to the user by email. The providing unit can also provide the generated answer to the user as a notification. In this way, the providing unit can provide the generated answer to the user in a chat format, allowing the user to quickly obtain the answer.

[0072] The analysis unit can grasp the trends and frequency of inquiries and provide data for creating FAQs for frequently asked questions. Understanding trends includes, but is not limited to, frequency analysis and trend analysis. The analysis unit can grasp the trends of inquiries using, for example, frequency analysis. For example, frequency analysis analyzes how frequently a specific inquiry occurs. The analysis unit can also grasp the trends of inquiries using trend analysis. For example, trend analysis analyzes the occurrence pattern of inquiries and understands changes over time. Measurement of frequency includes, but is not limited to, the number of occurrences, time units, etc. The analysis unit can measure the frequency of inquiries using, for example, the number of occurrences. For example, the number of occurrences measures how many times a specific inquiry occurs within a certain period of time. The analysis unit can also measure the frequency of inquiries using time units. For example, the time units measure how often a specific inquiry occurs in a specific time period. This allows the analysis unit to grasp the trends and frequency of inquiries and provide data for creating FAQs for frequently asked questions.

[0073] The providing unit can provide FAQs to allow users to solve their own problems. FAQs include, but are not limited to, question and answer pairs and categorization. The providing unit can provide FAQs using, for example, question and answer pairs. For example, question and answer pairs are a format that provides specific answers to specific questions. The providing unit can also provide FAQs using categorization. For example, categorization is a format that classifies questions into specific categories so that users can easily find the information they need. In this way, the providing unit can provide FAQs to allow users to solve their own problems, thereby reducing the burden of responding to inquiries.

[0074] The reception unit may enable a user to make an inquiry. The inquiry may include, but is not limited to, for example, text, voice, or image. The reception unit may, for example, accept a question input by the user in text format. For example, the user may input a question in text format and transmit it to the system. The reception unit may also accept voice input. For example, the user may input a question in voice format and transmit it to the system. The reception unit may also accept image input. For example, the user may input a question in image format and transmit it to the system. This allows the reception unit to enable a user to make an inquiry anytime, anywhere, and to quickly resolve problems.

[0075] The reception unit can estimate the user's emotion and adjust the inquiry reception method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The reception unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The reception unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the reception unit to estimate the user's emotion and adjust the inquiry reception method based on the estimated user's emotion, thereby enabling a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The reception unit can analyze the user's past inquiry history and select a reception method. The inquiry history includes, for example, past inquiry content, date and time, response results, etc., but is not limited to these examples. The reception unit, for example, analyzes the past inquiry content and selects a reception method. For example, the reception unit automatically displays inquiries frequently made by the user in the past as candidates. The reception unit can also analyze the date and time of past inquiries and select a reception method. For example, the reception unit prioritizes and suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also analyze past response results and select a reception method. For example, the reception unit predicts and suggests an inquiry method to be used in a specific time period based on the user's past inquiry history. This allows the reception unit to analyze the user's past inquiry history and select the optimal reception method, enabling efficient response. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal reception method.

[0077] When receiving an inquiry, the reception unit may perform filtering based on the user's current situation and areas of interest. The current situation may include, but is not limited to, the user's location information and device information. The reception unit may perform filtering based on, for example, the user's location information. For example, the reception unit may preferentially receive inquiries related to the user's current work. The reception unit may also perform filtering based on the user's device information. For example, the reception unit may preferentially receive inquiries related to the device the user is using. Areas of interest may include, but are not limited to, past search history and browsing history. The reception unit may perform filtering based on the user's past search history. For example, the reception unit may filter related inquiries based on the user's areas of interest. The reception unit may also perform filtering based on the user's past browsing history. For example, the reception unit may suggest optimal inquiry content depending on the user's current situation (time zone, location, etc.). This allows the reception unit to perform filtering based on the user's current situation and areas of interest, thereby enabling a more appropriate response to the inquiry. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's location information data to the generation AI and have the generation AI perform filtering.

[0078] The reception unit can select a reception means depending on the user's input method when receiving an inquiry. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when a user makes a voice inquiry, the reception unit uses voice recognition technology to receive the inquiry. For example, the voice recognition technology converts the user's voice into text and inputs it into the system. Furthermore, when a user makes a text inquiry, the reception unit can preferentially receive the text input. For example, the user inputs a question in text format and sends it to the system. Furthermore, when a user makes an inquiry using an image, the reception unit can also use image recognition technology to receive the inquiry. For example, the image recognition technology analyzes the image sent by the user and understands the inquiry content. This allows the reception unit to select the optimal reception means depending on the user's input method when receiving the inquiry, thereby enabling efficient inquiry reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's voice data into a generation AI and have the generation AI perform voice recognition.

[0079] The reception unit can estimate a user's emotion and determine the priority of inquiries to be received based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The reception unit can estimate a user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes a user's facial expression to estimate the emotion. The reception unit can also estimate a user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate a user's emotion using text analysis. For example, text analysis analyzes a user's text message to estimate the emotion. This allows the reception unit to estimate a user's emotion and determine the priority of inquiries to be received based on the estimated user's emotion, thereby enabling quick response to inquiries with high urgency. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0080] When accepting an inquiry, the reception unit can prioritize receiving inquiries that are highly relevant by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the reception unit prioritizes receiving inquiries related to that area. For example, when the user is in a specific area, the reception unit prioritizes receiving inquiries related to that area. Furthermore, when the user is traveling, the reception unit can suggest optimal inquiry content based on the user's current location. For example, when the user is traveling, the reception unit suggests optimal inquiry content based on the user's current location. Furthermore, when the user is in a specific facility, the reception unit can prioritize receiving inquiries related to that facility. For example, when the user is in a specific facility, the reception unit prioritizes receiving inquiries related to that facility. As a result, when accepting inquiries, the reception unit prioritizes receiving inquiries that are highly relevant by taking into account the user's geographical location information, thereby enabling the reception unit to prioritize responding to highly relevant inquiries. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant inquiries.

[0081] The reception unit may analyze the user's social media activity when receiving an inquiry and receive related inquiries. Social media activity may include, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit may preferentially receive related inquiries based on, for example, content mentioned by the user on social media. For example, the reception unit may preferentially receive related inquiries based on the content mentioned by the user on social media. The reception unit may also analyze the user's social media activity and suggest related inquiries. For example, the reception unit may analyze the user's social media activity and suggest related inquiries. The reception unit may also receive related inquiries by referring to the activities of the user's friends on social media. For example, the reception unit may receive related inquiries by referring to the activities of the user's friends on social media. In this way, the reception unit may analyze the user's social media activity when receiving an inquiry and receive related inquiries, thereby efficiently responding to related inquiries. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI select related inquiries.

[0082] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an inquiry. Feedback includes, but is not limited to, survey results, user comments, and the like. The reception unit can, for example, propose an optimal reception method based on feedback previously provided by the user. For example, the reception unit proposes an optimal reception method based on feedback previously provided by the user. The reception unit can also preferentially select a specific reception method based on the user's past feedback. For example, the reception unit preferentially selects a specific reception method based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. As a result, the reception unit can provide an optimal reception method by customizing the reception method by reflecting the user's past feedback when receiving an inquiry. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and cause the generation AI to customize the reception method.

[0083] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The analysis unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The analysis unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the analysis unit to estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the query. The importance includes, but is not limited to, for example, urgency and scope of impact. The analysis unit adjusts the level of detail of the analysis based on, for example, urgency. For example, the analysis unit performs a detailed analysis for queries with high importance. The analysis unit can also adjust the level of detail of the analysis based on the scope of impact. For example, the analysis unit performs a concise analysis for queries with low importance. The analysis unit can also adjust the priority of the analysis based on the importance. For example, the analysis unit prioritizes analysis for queries with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the query. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input query importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. Examples of categories include, but are not limited to, technical categories and business categories. For example, the analysis unit applies a specialized analysis algorithm to technical categories. For example, the analysis unit applies a specialized analysis algorithm to technical inquiries. The analysis unit can also apply a simple analysis algorithm to business categories. For example, the analysis unit applies a simple analysis algorithm to general inquiries. The analysis unit can also select and apply an optimal analysis algorithm for each category. For example, the analysis unit selects and applies an optimal analysis algorithm for each category. This enables the analysis unit to apply an optimal analysis algorithm depending on the category of the inquiry, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input inquiry category data to a generation AI and have the generation AI select an analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Analysis results include, for example, past analysis content, date and time, and results, but are not limited to these examples. The analysis unit can improve the accuracy of the analysis by referring to, for example, the past analysis content. For example, the analysis unit performs highly accurate analysis of similar queries based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the date and time of past analysis. For example, the analysis unit analyzes the user's past analysis results and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. As a result, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The analysis unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The analysis unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the analysis unit to estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0088] During analysis, the analysis unit can determine the analysis priority based on the submission time of the inquiry. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit determines the analysis priority based on, for example, the submission date and time. For example, the analysis unit prioritizes analysis for urgent inquiries. The analysis unit can also determine the analysis priority based on the submission frequency. For example, the analysis unit determines the analysis priority for normal inquiries based on the submission time. The analysis unit can also adjust the analysis schedule based on the submission time. For example, the analysis unit adjusts the analysis schedule based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data to the generation AI and have the generation AI determine the analysis priority.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the query. Relevance includes, but is not limited to, similarity of content and related keywords. The analysis unit adjusts the order of analysis based on, for example, similarity of content. For example, the analysis unit prioritizes analysis of highly relevant queries. The analysis unit can also adjust the order of analysis based on related keywords. For example, the analysis unit postpones analysis of less relevant queries. The analysis unit can also adjust the order of analysis based on relevance. For example, the analysis unit adjusts the order of analysis based on relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the query. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of technical terminology include, but are not limited to, credentials and past inquiries. The analysis unit can adjust the use of technical terms in the analysis based on, for example, credentials. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. The analysis unit can also adjust the use of technical terms in the analysis based on past inquiries. For example, if the user does not have technical expertise, the analysis unit can provide analysis results in simpler language. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are presented according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0091] The generation unit can estimate the user's emotion and adjust the answer generation method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The generation unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the generation unit to estimate the user's emotion and adjust the answer generation method based on the estimated user's emotion, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0092] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the inquiry. The importance includes, but is not limited to, for example, urgency and scope of impact. The generation unit adjusts the level of detail of the answer based on, for example, urgency. For example, the generation unit provides a detailed answer to an inquiry with a high importance. The generation unit can also adjust the level of detail of the answer based on the scope of impact. For example, the generation unit provides a concise answer to an inquiry with a low importance. The generation unit can also adjust the priority of the answer according to the importance. For example, the generation unit generates answers preferentially for inquiries with a high importance. This allows the generation unit to adjust the level of detail of the answer based on the importance of the inquiry, thereby enabling efficient answers. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input inquiry importance data to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0093] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the inquiry. Examples of categories include, but are not limited to, technical categories and business categories. For example, the generation unit applies a specialized generation algorithm to technical categories. For example, the generation unit can apply a specialized generation algorithm to technical inquiries. The generation unit can also apply a simple generation algorithm to business categories. For example, the generation unit can apply a simple generation algorithm to general inquiries. The generation unit can also select and apply an optimal generation algorithm for each category. For example, the generation unit selects and applies an optimal generation algorithm for each category. This enables the generation unit to apply an optimal generation algorithm depending on the category of the inquiry, thereby enabling an efficient answer. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input inquiry category data to the generation AI and cause the generation AI to select a generation algorithm.

[0094] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The answer results include, for example, past answer content, date and time, and result, but are not limited to these examples. The generation unit improves the accuracy of the answer by referring to the past answer content. For example, the generation unit provides highly accurate answers to similar inquiries based on the user's past answer results. The generation unit can also improve the accuracy of the answer by referring to the date and time of the past answer. For example, the generation unit analyzes the user's past answer results and optimizes the generation algorithm. The generation unit can also improve the accuracy of the answer by referring to the past answer results. For example, the generation unit improves the accuracy of the answer by referring to the user's past answer results. As a result, the generation unit improves the accuracy of the answer by referring to the user's past answer results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0095] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The generation unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the generation unit to estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion, thereby providing a more appropriate answer. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0096] When generating answers, the generation unit can determine the priority of answers based on the submission time of the inquiry. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The generation unit determines the priority of answers based on, for example, the submission date and time. For example, the generation unit generates answers with priority for urgent inquiries. The generation unit can also determine the priority of answers based on the submission frequency. For example, the generation unit determines the priority of answers for normal inquiries based on the submission time. The generation unit can also adjust the response schedule according to the submission time. For example, the generation unit adjusts the response schedule according to the submission time. This enables the generation unit to determine the priority of answers based on the submission time of the inquiry, thereby enabling efficient responses. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input submission time data into the generation AI and have the generation AI determine the priority of answers.

[0097] When generating an answer, the generation unit can adjust the order of answers based on the relevance of the inquiry. Relevance includes, but is not limited to, similarity of content and related keywords. The generation unit adjusts the order of answers based on similarity of content, for example. For example, the generation unit prioritizes generating answers for inquiries with high relevance. The generation unit can also adjust the order of answers based on related keywords. For example, the generation unit postpones generating answers for inquiries with low relevance. The generation unit can also adjust the order of answers based on relevance. For example, the generation unit adjusts the order of answers based on relevance. This enables the generation unit to adjust the order of answers based on the relevance of the inquiry, thereby enabling efficient answers. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0098] When generating an answer, the generation unit can adjust the use of technical terms in the answer based on the user's level of expertise. Examples of the level of expertise include, but are not limited to, qualifications and past inquiries. The generation unit can adjust the use of technical terms in the answer based on qualifications. For example, if the user has specialized knowledge, the generation unit provides an answer that uses a lot of technical terms. The generation unit can also adjust the use of technical terms in the answer based on past inquiries. For example, if the user does not have specialized knowledge, the generation unit can provide an answer in simple language. The generation unit can also adjust the way the answer is expressed based on the user's level of expertise. For example, the generation unit adjusts the way the answer is expressed based on the user's level of expertise. This allows the generation unit to provide an answer that is easier to understand by adjusting the use of technical terms in the answer based on the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The providing unit can estimate the user's emotion and adjust the method of providing an answer based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The providing unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The providing unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The providing unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the providing unit to estimate the user's emotion and adjust the method of providing an answer based on the estimated user's emotion, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0100] When providing an answer, the providing unit can select a delivery method by referring to the user's past inquiry history. The inquiry history includes, for example, past inquiry content, date and time, response results, etc., but is not limited to these examples. The providing unit selects a delivery method by referring to, for example, the content of the past inquiry. For example, the providing unit selects an optimal delivery method based on delivery methods used by the user in the past. The providing unit can also select a delivery method by referring to the date and time of past inquiries. For example, the providing unit suggests an optimal delivery method based on the user's past inquiry history. The providing unit can also select a delivery method by referring to past response results. For example, the providing unit analyzes the user's past inquiry history and optimizes the delivery method. This allows the providing unit to select an optimal delivery method by referring to the user's past inquiry history, enabling efficient answer provision. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's past inquiry history data into the generation AI and have the generation AI select a delivery method.

[0101] When providing an answer, the providing unit can customize the content to be provided based on the user's current situation. The current situation includes, but is not limited to, the user's location information and device information. The providing unit customizes the content to be provided based on, for example, the user's location information. For example, the providing unit prioritizes providing answers related to the user's current task. The providing unit can also customize the content to be provided based on the user's device information. For example, the providing unit suggests optimal content to be provided based on the user's current situation (time of day, location, etc.). The providing unit can also customize the content to be provided taking into account the user's current situation. For example, the providing unit customizes the content to be provided taking into account the user's current situation. In this way, the providing unit can provide a more appropriate answer by customizing the content to be provided based on the user's current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's location information data to the generation AI and cause the generation AI to customize the content to be provided.

[0102] The providing unit can improve the answer providing method by reflecting user feedback when providing an answer. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit improves the answer providing method based on, for example, user feedback. For example, the providing unit improves the answer providing method based on user feedback. The providing unit can also preferentially select a specific answer providing method based on user feedback. For example, the providing unit preferentially selects a specific answer providing method based on user feedback. The providing unit can also customize the answer providing interface by reflecting user feedback. For example, the providing unit customizes the answer providing interface by reflecting user feedback. This allows the providing unit to improve the answer providing method by reflecting user feedback when providing an answer, thereby providing a more appropriate answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the answer providing method.

[0103] The providing unit can estimate the user's emotion and determine the priority of answer provision based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. The providing unit can estimate the user's emotion using, for example, facial expression analysis. For example, facial expression analysis analyzes the user's facial expression to estimate the emotion. The providing unit can also estimate the user's emotion using voice analysis. For example, voice analysis analyzes the tone and speed of the user's voice to estimate the emotion. The providing unit can also estimate the user's emotion using text analysis. For example, text analysis analyzes the user's text message to estimate the emotion. This allows the providing unit to estimate the user's emotion and determine the priority of answer provision based on the estimated user's emotion, thereby enabling quick response to inquiries with high urgency. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0104] The providing unit can select a providing method by taking into consideration the user's geographical location information when providing an answer. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the providing unit prioritizes providing answers related to that area. For example, when the user is in a specific area, the providing unit prioritizes providing answers related to that area. Furthermore, when the user is traveling, the providing unit can also suggest optimal content to be provided based on the user's current location. For example, when the user is traveling, the providing unit suggests optimal content to be provided based on the user's current location. Furthermore, when the user is in a specific facility, the providing unit can also prioritize providing answers related to that facility. For example, when the user is in a specific facility, the providing unit prioritizes providing answers related to that facility. Thus, when providing an answer, the providing unit can select a providing method by taking into consideration the user's geographical location information, thereby providing a highly relevant answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and have the generating AI select the providing method.

[0105] The providing unit can analyze the user's social media activity and suggest a means of providing an answer when providing an answer. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The providing unit can, for example, prioritize providing relevant answers based on content mentioned by the user on social media. For example, the providing unit can prioritize providing relevant answers based on content mentioned by the user on social media. The providing unit can also analyze the user's social media activity and suggest relevant answer content. For example, the providing unit can analyze the user's social media activity and suggest relevant answer content. The providing unit can also provide relevant answers by referring to the activity of the user's friends on social media. For example, the providing unit can provide relevant answers by referring to the activity of the user's friends on social media. In this way, the providing unit can efficiently provide relevant answers by analyzing the user's social media activity and suggesting a means of providing an answer when providing an answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into the generating AI and have the generating AI suggest a means of providing the data.

[0106] The providing unit can customize the answer providing method by reflecting the user's past feedback when providing an answer. Examples of feedback include, but are not limited to, survey results and user comments. The providing unit can, for example, propose an optimal answer providing method based on feedback previously provided by the user. For example, the providing unit proposes an optimal answer providing method based on feedback previously provided by the user. The providing unit can also preferentially select a specific answer providing method based on the user's past feedback. For example, the providing unit preferentially selects a specific answer providing method based on the user's past feedback. The providing unit can also customize the answer providing interface by reflecting the user's feedback. For example, the providing unit customizes the answer providing interface by reflecting the user's feedback. As a result, the providing unit can provide an optimal answer providing method by customizing the answer providing method by reflecting the user's past feedback when providing an answer. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the answer providing method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives inquiries from users in the form of text, voice, images, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response in text or voice format based on the analyzed content. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the response in chat format. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives inquiries from a user in the form of text, voice, images, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response in text or voice format based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides a response in chat format. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives inquiries from users in the form of text, voice, images, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response in text or voice format based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides a response in chat format. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives inquiries from users in the form of text, voice, images, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry content using natural language processing technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response in text or voice format based on the analyzed content. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides a response in chat format.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past inquiry history and understand what kind of inquiries the user has made in the past. For example, it can automatically display inquiries that the user has made frequently in the past as candidates. The reception unit can also analyze the date and time of past inquiries, and if the user tends to make inquiries during a specific time period, it can respond to the inquiry in accordance with that time period. Furthermore, the reception unit can analyze the results of past inquiries and accumulate data for providing answers that satisfy the user. This allows the reception unit to utilize the user's past inquiry history to provide a more appropriate response.

[0109] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling anxious or angry, the analysis unit prioritizes analyzing that inquiry. The analysis unit can also adjust the level of detail of the analysis according to the user's emotions. For example, if the user feels urgent, the analysis unit performs a detailed analysis and provides a quick answer. Furthermore, the analysis unit can adjust the way the analysis results are presented based on the user's emotions. For example, if the user is calm, the analysis unit provides a detailed technical explanation, and if the user is impatient, the analysis unit provides a concise and easy-to-understand explanation. This allows the analysis unit to perform a more appropriate analysis taking the user's emotions into consideration.

[0110] The providing unit can adjust the answer providing method based on the user's current situation. For example, if the user is on the move, the answer can be provided in voice format. If the user is in a desktop environment, the answer can be provided in detailed text format. Furthermore, the providing unit can select the optimal answer providing method based on the user's device information. For example, if the user is using a smartphone, the answer can be provided in the form of a simple message, and if the user is using a tablet, the answer can be provided using an interactive graphical interface. This allows the providing unit to select the optimal answer providing method depending on the user's current situation.

[0111] The analysis unit can refer to relevant external databases based on the content of the inquiry to provide more accurate analysis results. For example, for technical inquiries, a specialized technical database can be referenced. For business-related inquiries, an industry trend database can be referenced. Furthermore, the analysis unit can automatically select relevant external databases based on the user's past inquiry history. This allows the analysis unit to utilize external databases to provide more accurate and reliable analysis results.

[0112] The providing unit can estimate the user's emotions and adjust the way in which the answer is provided based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide an answer in a gentle tone. Also, if the user is feeling angry, the providing unit can provide a quick and concise answer. Furthermore, the providing unit can adjust the level of detail of the answer depending on the user's emotions. For example, if the user is calm, the providing unit can provide a detailed technical explanation, and if the user is impatient, the providing unit can provide a concise and easy-to-understand explanation. This allows the providing unit to provide a more appropriate answer by taking the user's emotions into consideration.

[0113] The reception unit can customize the inquiry reception method based on the user's current situation and areas of interest. For example, if the user is interested in a specific project, inquiries related to that project can be received with priority. Also, if the user tends to make inquiries during a specific time period, the reception unit can respond to inquiries according to that time period. Furthermore, the reception unit can automatically suggest related inquiries based on the user's past search history and browsing history. This allows the reception unit to provide the optimal inquiry reception method according to the user's current situation and areas of interest.

[0114] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide analysis results in a gentle tone. If the user is feeling angry, the analysis unit can provide quick and concise analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's emotions. For example, if the user is calm, the analysis unit can provide a detailed technical explanation, and if the user is impatient, the analysis unit can provide a concise and easy-to-understand explanation. This allows the analysis unit to provide more appropriate analysis results by taking the user's emotions into consideration.

[0115] The providing unit can improve the answer providing method by reflecting the user's past feedback. For example, the providing unit can suggest the optimal answer providing method based on the user's past feedback. The providing unit can also preferentially select a specific answer providing method based on the user's feedback. Furthermore, the providing unit can customize the providing interface by reflecting the user's feedback. In this way, the providing unit can utilize the user's past feedback to provide a more appropriate answer providing method.

[0116] The reception unit can estimate the user's emotions and adjust the inquiry reception method based on the estimated user's emotions. For example, if the user feels anxious, the reception unit can respond in a gentle tone. If the user feels angry, the reception unit can respond quickly and concisely. Furthermore, the reception unit can adjust the priority of inquiries according to the user's emotions. For example, if the user feels urgent, the reception unit can receive the inquiry with priority. In this way, the reception unit can provide a more appropriate inquiry reception method taking the user's emotions into consideration.

[0117] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, based on the content of a user's past inquiries and the analysis results thereof, the analysis unit can perform highly accurate analysis of similar inquiries. The analysis unit can also analyze past analysis results and optimize the analysis algorithm. Furthermore, the analysis unit can determine the priority of analysis by referring to the user's past analysis results. This allows the analysis unit to utilize the user's past analysis results to perform more accurate and efficient analysis.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives an inquiry from a user. The inquiry from the user may include, but is not limited to, text, voice, or image. For example, the reception unit receives a question input by the user in text format. The reception unit can also receive voice input or image input. Step 2: The analysis unit analyzes the inquiry received by the reception unit. The analysis is performed using, for example, natural language processing technology, but is not limited to this example. For example, the analysis unit analyzes the text using morphological analysis. The analysis unit can also analyze the structure of the sentence using grammatical analysis. The analysis unit can also understand the meaning of the sentence using semantic analysis. Step 3: The generation unit generates an answer based on the content analyzed by the analysis unit. The answer is generated in text format, for example, but is not limited to this example. For example, the generation unit generates text using natural language generation technology. The generation unit can also generate an answer in audio format using speech synthesis technology. The generation unit can also generate an answer in image format using image generation technology. Step 4: The providing unit provides the answer generated by the generating unit. The answer is provided, for example, in a chat format, but is not limited to this example. For example, the providing unit provides the answer using real-time chat. The providing unit can also provide the answer using bot chat. The providing unit can also provide the answer using email or notification.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives inquiries from users; an analysis unit that analyzes the inquiry content received by the reception unit; a generation unit that generates an answer based on the content analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit. A system characterized by:

2. The analysis unit Uses natural language processing technology to understand the intent of the question and generate an appropriate answer 2. The system of claim 1.

3. The providing unit Provide generated answers to users in chat format 2. The system of claim 1.

4. The analysis unit Understand the trends and frequency of inquiries and provide data to create FAQs for common questions 2. The system of claim 1.

5. The providing unit Provide FAQs to help users solve their own problems 2. The system of claim 1.

6. The reception unit Allow users to make inquiries 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the way inquiries are received based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past inquiry history and select the reception method 2. The system of claim 1.

9. The reception unit Filtering inquiries based on the user's current situation and interests 2. The system of claim 1.

10. The reception unit When accepting an inquiry, select the acceptance method according to the user's input method.

2. The system of claim 1.

Images