system

Abstract

The system according to the embodiment aims to reduce the burden of communication and reduce misunderstandings and mistakes. [Solution] A system according to an embodiment includes a question receiving unit, a question generation unit, an answer range setting unit, and an answer generation unit. The question receiving unit receives questions that the user wants to ask. The question generation unit analyzes the content received by the question receiving unit and generates a question. The answer range setting unit sets a range based on user information. The answer generation unit uses AI to answer questions generated by the question generation unit on behalf of the user.

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 can lead to miscommunication, misunderstandings, failures, anxiety, and stress.

[0005] The system according to the embodiment aims to reduce the burden of communication and reduce misunderstandings and mistakes. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, a question generation unit, an answer range setting unit, and an answer generation unit. The question receiving unit receives questions that the user wants to ask. The question generation unit analyzes the content received by the question receiving unit and generates a question. The answer range setting unit sets a range based on user information. The answer generation unit uses AI to answer questions generated by the question generation unit on behalf of the user. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden of communication and reduce misunderstandings and mistakes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication support system according to an embodiment of the present invention utilizes AI to reduce the burden of communication. In this system, a user communicates what they want to ask to an AI, which analyzes the content and generates a question to ask the other party by filling in any gaps. The user also communicates the extent to which the AI ​​is willing to answer the question using their own information, and the AI ​​answers the question on their behalf. For example, the communication support system allows the user to input what they want to ask. For example, the user can input the question in text format or by voice input. Next, the communication support system uses AI to analyze the content of the user's question and generate a question by filling in any gaps. For example, the AI ​​receives a prompt such as "Please summarize the main points of this question," extracts the main points of the question, and creates a question. Next, the communication support system sets the extent to which the user's information is willing to answer the question, and the AI ​​answers the question on their behalf within that range. For example, the AI ​​generates an appropriate answer based on the user's information and provides it to the other party. Furthermore, the communication support system can provide relevant advertising and promotional information when answering on the user's behalf. This allows the user to obtain the necessary information while also learning about new products and services. This reduces the burden of communication for the user. For example, it can reduce the stress and anxiety users feel when communicating with their superiors and allow them to exchange information more efficiently.It can also create new advertising markets and marketing opportunities by providing relevant advertising and promotional information.

[0029] A communication support system according to an embodiment includes a question receiving unit, a question generating unit, an answer range setting unit, and an answer generating unit. The question receiving unit receives a question from a user. The user's question may include, but is not limited to, work-related questions or personal inquiries. The question receiving unit may receive a question input by the user in text format. The question receiving unit may also receive a question using voice input. For example, a voice recognition technology may be used to convert the user's voice into text and receive the text as a question. The question generating unit may use AI to analyze the content received by the question receiving unit and generate a question. The question generating unit may use natural language processing technology to analyze the content of the user's question. The question generating unit may also use a machine learning algorithm to generate a question by compensating for any omissions or omissions in the question. For example, an AI may receive a prompt such as "Please summarize the main points of this question," extract the main points of the question, and create a question. The answer range setting unit sets a range based on user information. The answer range setting unit sets a range of answers that are acceptable based on, for example, the user's personal information and behavioral history. The answer range setting unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "please do not disclose this information." The answer generation unit uses AI to answer questions generated by the question generation unit. For example, the answer generation unit generates appropriate answers based on user information. The answer generation unit can also provide related advertisements and promotional information. For example, the AI ​​provides information on related products and services along with answers to user questions. This enables the communication support system according to the embodiment to efficiently accept, analyze, generate questions, and generate answers based on user requests.

[0030] The question generation unit can analyze what the user wants to ask and generate a question. The question generation unit analyzes the content of the user's question using, for example, natural language processing technology. For example, the question generation unit extracts the main points of the user's question using a text analysis algorithm. The question generation unit can also generate a question by supplementing the question with a machine learning algorithm. For example, the question generation unit generates a question by adding relevant information based on the content of the user's question. The question generation unit can also use a generation AI to summarize the content of the user's question and generate a concise question. For example, the generation AI receives a prompt such as "Please summarize the main points of this question," and extracts the main points of the question to create a question. In this way, a more appropriate question can be generated by analyzing what the user wants to ask and supplementing the questions.

[0031] The answer generation unit allows the AI ​​to answer questions on behalf of the user based on the user's information. The answer generation unit generates answers to questions based on, for example, the user's personal information and behavioral history. For example, the answer generation unit generates appropriate answers based on the user's past behavioral history. The answer generation unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "do not disclose." The answer generation unit can also use the generation AI to generate appropriate answers based on the user's information. For example, the generation AI generates answers to the user's questions and provides them to the other party. In this way, the AI ​​answers on behalf of the user based on the range of information that is acceptable for the answer, thereby efficiently generating answers while properly managing the user's information.

[0032] When accepting a question, the question acceptance unit can select an appropriate acceptance method by referring to the user's past question history. For example, the question acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The question acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The question acceptance unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the question acceptance unit selects the optimal question acceptance method by saving and analyzing the user's past question history in a database. In this way, the optimal question acceptance method can be selected by referring to the user's past question history.

[0033] The question receiving unit can filter questions based on the user's current situation and areas of interest when receiving questions. For example, the question receiving unit preferentially receives questions related to a project the user is currently working on. The question receiving unit can also automatically filter related questions based on the user's areas of interest. The question receiving unit can also select an appropriate question receiving method depending on the user's current situation (for example, in a meeting or on the move). For example, the question receiving unit collects the user's location information and activity status using a sensor to understand the current situation. The question receiving unit also stores the user's areas of interest in a database and analyzes them to filter related questions. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be received.

[0034] When accepting a question, the question acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the question acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question using text, the question acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the question acceptance unit can also accept the question using image recognition technology. For example, the question acceptance unit selects the optimal acceptance means depending on the user's input method. This enables more efficient question acceptance by selecting the optimal acceptance means depending on the user's input method.

[0035] When accepting questions, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the question acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the question acceptance unit can also prioritize accepting questions related to the user's destination. Furthermore, if the user is overseas, the question acceptance unit can also prioritize accepting questions related to the country or area. For example, the question acceptance unit can obtain the user's location information using GPS data or a location information service and filter out relevant questions. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized.

[0036] The question accepting unit can analyze the user's social media activity when accepting a question and accept related questions. For example, the question accepting unit preferentially accepts questions related to topics in which the user has shown interest on social media. The question accepting unit can also analyze the content of the user's social media posts and accept related questions. The question accepting unit can also accept related questions by referring to the activities of the user's friends on social media. For example, the question accepting unit stores the user's social media activity in a database and analyzes it to filter related questions. In this way, related questions can be accepted by analyzing the user's social media activity.

[0037] The question reception unit can customize an appropriate reception method by reflecting the user's past feedback when receiving a question. The question reception unit can, for example, suggest an optimal question reception method based on feedback provided by the user in the past. The question reception unit can also preferentially receive specific question formats based on the user's past feedback. The question reception unit can also analyze the user's past feedback and customize the reception method. For example, the question reception unit selects an optimal question reception method by storing and analyzing the user's feedback in a database. In this way, the optimal question reception method can be customized by reflecting the user's past feedback.

[0038] The question generation unit can adjust the level of detail of the question based on the importance of the question when generating the question. For example, the question generation unit includes a detailed explanation for a question with a high level of importance. The question generation unit can also include a concise explanation for a question with a low level of importance. The question generation unit can also appropriately add necessary information depending on the importance of the question. For example, the question generation unit analyzes the content of the user's question and adjusts the level of detail of the question using an algorithm that evaluates the importance. In this way, by adjusting the level of detail of the question based on the importance of the question, a more appropriate question can be generated.

[0039] When generating a question, the question generation unit can apply different question generation algorithms depending on the category of the question. For example, the question generation unit applies a specialized algorithm to technical questions. The question generation unit can also apply a general-purpose algorithm to general questions. The question generation unit can also apply a specific support algorithm to questions related to customer support. For example, the question generation unit analyzes the category of the question and selects an appropriate algorithm. In this way, more appropriate questions can be generated by applying different question generation algorithms depending on the category of the question.

[0040] When generating a question, the question generation unit can improve the accuracy of the question by referring to the user's past question results. The question generation unit improves the accuracy of the question, for example, based on answers the user has received in the past. The question generation unit can also select the optimal question format from the user's past question results. The question generation unit can also analyze the user's past question history to improve the accuracy of the question. For example, the question generation unit improves the accuracy of the question by saving the user's past question results in a database and analyzing them. In this way, the accuracy of the question is improved by referring to the user's past question results.

[0041] When generating questions, the question generation unit can determine the priority of questions based on the time of submission of the questions. For example, the question generation unit generates questions with a high degree of urgency with priority. The question generation unit can also generate questions with a high priority that are due to be submitted soon with priority. The question generation unit can also determine the optimal order of question generation depending on the time of submission of the questions. For example, the question generation unit analyzes the content of a user's question and determines the priority of questions using an algorithm that evaluates the time of submission. In this way, by determining the priority of questions based on the time of submission of the question, more appropriate questions can be generated.

[0042] The question generation unit can adjust the order of questions based on the relevance of the questions when generating questions. For example, the question generation unit generates highly relevant questions with priority. The question generation unit can also determine the optimal order of questions based on the relevance of the questions. The question generation unit can also analyze the relevance of questions and propose an efficient order of generating questions. For example, the question generation unit analyzes the content of a user's question and adjusts the order of questions using an algorithm that evaluates the relevance. This allows for more efficient question generation by adjusting the order of questions based on the relevance of the questions.

[0043] When generating a question, the question generation unit can adjust the use of technical terms in the question according to the user's level of expertise. For example, if the user has technical knowledge, the question generation unit generates a question that uses a lot of technical terms. Furthermore, if the user has general knowledge, the question generation unit can also generate a question that avoids technical terms. Furthermore, the question generation unit can select an optimal question expression according to the user's level of expertise. For example, the question generation unit adjusts the use of technical terms in the question using an algorithm that evaluates the user's level of expertise. In this way, by adjusting the use of technical terms in the question according to the user's level of expertise, more appropriate questions can be generated.

[0044] When setting the answer range, the answer range setting unit can select an appropriate setting method by referring to the user's past answer history. The answer range setting unit sets the optimal answer range based on, for example, answers provided by the user in the past. The answer range setting unit can also preferentially set a specific answer format based on the user's past answer history. The answer range setting unit can also analyze the user's past answer history and optimize the answer range. For example, the answer range setting unit selects the optimal answer range setting method by saving and analyzing the user's past answer history in a database. In this way, the optimal answer range setting method can be selected by referring to the user's past answer history.

[0045] When setting the answer range, the answer range setting unit can perform filtering based on the user's current situation and areas of interest. For example, the answer range setting unit prioritizes setting an answer range related to a project the user is currently working on. The answer range setting unit can also automatically filter related answer ranges based on the user's areas of interest. The answer range setting unit can also set an appropriate answer range depending on the user's current situation (for example, in a meeting or on the move). For example, the answer range setting unit collects the user's location information and activity status using a sensor to grasp the current situation. The answer range setting unit also filters out related answer ranges by saving the user's areas of interest in a database and analyzing them. In this way, by filtering the answer range based on the user's current situation and areas of interest, a more relevant answer range can be set.

[0046] When setting the answer range, the answer range setting unit can select an appropriate setting means depending on the user's input method. For example, when the user sets the answer range by voice, the answer range setting unit sets it using voice recognition technology. Furthermore, when the user sets the answer range by text, the answer range setting unit can also set it using text analysis technology. Furthermore, when the user sets the answer range using an image, the answer range setting unit can also set it using image recognition technology. For example, the answer range setting unit selects the optimal setting means depending on the user's input method. This enables more efficient answer range setting by selecting the optimal setting means depending on the user's input method.

[0047] When setting the answer range, the answer range setting unit can prioritize a highly relevant range by taking into account the user's geographical location information. For example, if the user is in a specific area, the answer range setting unit can prioritize an answer range related to that area. Furthermore, if the user is traveling, the answer range setting unit can also prioritize an answer range related to the user's destination. Furthermore, if the user is overseas, the answer range setting unit can also prioritize an answer range related to that country or area. For example, the answer range setting unit obtains the user's location information using GPS data or a location information service and filters the relevant answer ranges. In this way, it is possible to prioritize a highly relevant answer range by taking into account the user's geographical location information.

[0048] When setting the answer range, the answer range setting unit can analyze the user's social media activity and set the relevant range. For example, the answer range setting unit prioritizes setting the answer range related to topics in which the user has shown interest on social media. The answer range setting unit can also analyze the content of the user's posts on social media and set the relevant answer range. The answer range setting unit can also set the relevant answer range by referring to the activities of the user's friends on social media. For example, the answer range setting unit filters the relevant answer range by storing and analyzing the user's social media activity in a database. In this way, the relevant answer range can be set by analyzing the user's social media activity.

[0049] The answer range setting unit can customize an appropriate setting method by reflecting the user's past feedback when setting the answer range. The answer range setting unit sets an optimal answer range based on, for example, feedback provided by the user in the past. The answer range setting unit can also prioritize a specific answer format based on the user's past feedback. The answer range setting unit can also analyze the user's past feedback and customize the setting method. For example, the answer range setting unit selects an optimal answer range setting method by saving and analyzing the user's feedback in a database. In this way, the optimal answer range setting method can be customized by reflecting the user's past feedback.

[0050] The answer generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the answer generation unit includes a detailed explanation for a question with a high level of importance. The answer generation unit can also include a concise explanation for a question with a low level of importance. The answer generation unit can also appropriately add necessary information depending on the importance of the question. For example, the answer generation unit analyzes the content of the user's question and adjusts the level of detail of the answer using an algorithm that evaluates the importance. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be generated.

[0051] When generating an answer, the answer generation unit can apply different answer generation algorithms depending on the category of the question. For example, the answer generation unit applies a specialized algorithm to technical questions. The answer generation unit can also apply a general-purpose algorithm to general questions. The answer generation unit can also apply a specific support algorithm to questions regarding customer support. For example, the answer generation unit analyzes the category of the question and selects an appropriate algorithm. In this way, by applying different answer generation algorithms depending on the category of the question, a more appropriate answer can be generated.

[0052] When generating an answer, the answer generation unit can improve the accuracy of the answer by referring to the user's past answer results. The answer generation unit improves the accuracy of the answer, for example, based on answers the user has received in the past. The answer generation unit can also select the optimal answer format from the user's past answer results. The answer generation unit can also analyze the user's past answer history to improve the accuracy of the answer. For example, the answer generation unit improves the accuracy of the answer by saving the user's past answer results in a database and analyzing them. In this way, the accuracy of the answer is improved by referring to the user's past answer results.

[0053] When generating answers, the answer generation unit can determine the priority of answers based on the time of submission of the question. For example, the answer generation unit generates answers with priority to questions with high urgency. The answer generation unit can also generate answers with priority to questions that will be submitted soon. The answer generation unit can also determine the optimal order of answer generation depending on the time of submission of the questions. For example, the answer generation unit analyzes the content of the user's question and determines the priority of answers using an algorithm that evaluates the time of submission. In this way, by determining the priority of answers based on the time of submission of the question, more appropriate answers can be generated.

[0054] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit preferentially generates answers to questions with high relevance. The answer generation unit can also determine the optimal order of answers according to the relevance of the questions. The answer generation unit can also analyze the relevance of the questions and propose an efficient order of answer generation. For example, the answer generation unit analyzes the content of the user's question and adjusts the order of answers using an algorithm that evaluates the relevance. This allows for more efficient answer generation by adjusting the order of answers based on the relevance of the questions.

[0055] When generating an answer, the answer generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user has technical knowledge, the answer generation unit generates an answer that uses a lot of technical terms. Furthermore, if the user has general knowledge, the answer generation unit can also generate an answer that avoids technical terms. Furthermore, the answer generation unit can select an optimal answer expression according to the user's level of expertise. For example, the answer generation unit adjusts the use of technical terms in the answer using an algorithm that evaluates the user's level of expertise. In this way, by adjusting the use of technical terms in the answer according to the user's level of expertise, a more appropriate answer can be generated.

[0056] The answer generation unit can provide relevant advertising or promotional information when the AI ​​answers a question on behalf of the user. For example, the answer generation unit provides information on related products and services along with the answer to the user's question. The answer generation unit can also select appropriate advertising or promotional information based on the user's interests. For example, the answer generation unit analyzes the content of the user's question and provides advertising or promotional information using an algorithm that displays relevant advertisements. This can create new advertising markets and marketing opportunities by providing related advertising or promotional information along with the answer to the question.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The question reception unit can select an appropriate reception method by referring to the user's past question history. For example, the question reception unit can automatically display questions that the user has frequently asked in the past as candidates. The question reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The question reception unit can also predict and suggest questions that will be asked during a specific time period based on the user's past question history. For example, the question reception unit selects the optimal question reception method by saving and analyzing the user's past question history in a database. In this way, the optimal question reception method can be selected by referring to the user's past question history.

[0059] The answer generation unit allows the AI ​​to answer questions on behalf of the user based on the user's information. For example, the answer generation unit generates answers to questions based on the user's personal information and behavioral history. For example, the answer generation unit generates appropriate answers based on the user's past behavioral history. The answer generation unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "do not disclose." The answer generation unit can also use the generation AI to generate appropriate answers based on the user's information. For example, the generation AI generates answers to the user's questions and provides them to the other party. In this way, the AI ​​answers on behalf of the user based on the range of information that is acceptable for the answer, allowing answers to be generated efficiently while properly managing the user's information.

[0060] When accepting a question, the question acceptance unit can select an appropriate acceptance method by referring to the user's past question history. For example, the question acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The question acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The question acceptance unit can also predict and suggest questions that will be asked during a specific time period based on the user's past question history. For example, the question acceptance unit selects the optimal question acceptance method by saving and analyzing the user's past question history in a database. In this way, the optimal question acceptance method can be selected by referring to the user's past question history.

[0061] When accepting questions, the question acceptance unit can filter questions based on the user's current situation and areas of interest. For example, questions related to a project the user is currently working on are preferentially accepted. The question acceptance unit can also automatically filter related questions based on the user's areas of interest. The question acceptance unit can also select an appropriate question acceptance method depending on the user's current situation (for example, in a meeting or on the move). For example, the question acceptance unit collects the user's location information and activity status using a sensor to understand the current situation. The question acceptance unit also stores the user's areas of interest in a database and analyzes them to filter out related questions. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be accepted.

[0062] When accepting a question, the question acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user inputs a question by voice, the question acceptance unit can accept the question using voice recognition technology. Furthermore, if the user inputs a question using text, the question acceptance unit can also accept the question using text analysis technology. Furthermore, if the user inputs a question using an image, the question acceptance unit can also accept the question using image recognition technology. For example, the question acceptance unit selects the optimal acceptance means depending on the user's input method. This allows for more efficient question acceptance by selecting the optimal acceptance means depending on the user's input method.

[0063] When accepting questions, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, it can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the question acceptance unit can prioritize accepting questions related to the user's destination. Furthermore, if the user is overseas, the question acceptance unit can prioritize accepting questions related to that country or area. For example, the question acceptance unit can obtain the user's location information using GPS data or a location information service and filter out relevant questions. In this way, it is possible to prioritize accepting highly relevant questions by taking into account the user's geographical location information.

[0064] When accepting a question, the question acceptance unit can analyze the user's social media activity and accept related questions. For example, it can preferentially accept questions related to topics in which the user has shown interest on social media. The question acceptance unit can also analyze the content of the user's social media posts and accept related questions. The question acceptance unit can also accept related questions by referring to the activity of the user's friends on social media. For example, the question acceptance unit stores the user's social media activity in a database and analyzes it to filter related questions. In this way, it is possible to accept related questions by analyzing the user's social media activity.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The question receiving unit receives a question from the user. The question the user wants to ask may include, but is not limited to, a question about work or a personal question. For example, the question receiving unit receives a question input by the user in text format. The question receiving unit can also receive a question using voice input. For example, using voice recognition technology, the user's voice is converted into text and accepted as a question. Step 2: The question generation unit uses AI to analyze the content received by the question reception unit and generate a question. The question generation unit uses, for example, natural language processing technology to analyze the content of the user's question. The question generation unit can also use a machine learning algorithm to generate a question by compensating for any omissions or deficiencies in the question. For example, the AI ​​receives a prompt such as "Please summarize the main points of this question," and extracts the main points of the question to create a question. Step 3: The answer range setting unit sets the range based on the user's information. The answer range setting unit sets the range of answers that can be answered based on, for example, the user's personal information or behavioral history. The answer range setting unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "Do not disclose this information." Step 4: In the answer generation unit, the AI ​​answers the questions generated by the question generation unit on behalf of the user. For example, the answer generation unit generates an appropriate answer based on the user's information. The answer generation unit can also provide related advertisements and promotional information. For example, the AI ​​provides information on related products and services along with the answer to the user's question.

[0067] (Example 2) A communication support system according to an embodiment of the present invention utilizes AI to reduce the burden of communication. In this system, a user communicates what they want to ask to an AI, which analyzes the content and generates a question to ask the other party by filling in any gaps. The user also communicates the extent to which the AI ​​is willing to answer the question using their own information, and the AI ​​answers the question on their behalf. For example, the communication support system allows the user to input what they want to ask. For example, the user can input the question in text format or by voice input. Next, the communication support system uses AI to analyze the content of the user's question and generate a question by filling in any gaps. For example, the AI ​​receives a prompt such as "Please summarize the main points of this question," extracts the main points of the question, and creates a question. Next, the communication support system sets the extent to which the user's information is willing to answer the question, and the AI ​​answers the question on their behalf within that range. For example, the AI ​​generates an appropriate answer based on the user's information and provides it to the other party. Furthermore, the communication support system can provide relevant advertising and promotional information when answering on the user's behalf. This allows the user to obtain the necessary information while also learning about new products and services. This reduces the burden of communication for the user. For example, it can reduce the stress and anxiety users feel when communicating with their superiors and allow them to exchange information more efficiently.It can also create new advertising markets and marketing opportunities by providing relevant advertising and promotional information.

[0068] A communication support system according to an embodiment includes a question receiving unit, a question generating unit, an answer range setting unit, and an answer generating unit. The question receiving unit receives a question from a user. The user's question may include, but is not limited to, work-related questions or personal inquiries. The question receiving unit may receive a question input by the user in text format. The question receiving unit may also receive a question using voice input. For example, a voice recognition technology may be used to convert the user's voice into text and receive the text as a question. The question generating unit may use AI to analyze the content received by the question receiving unit and generate a question. The question generating unit may use natural language processing technology to analyze the content of the user's question. The question generating unit may also use a machine learning algorithm to generate a question by compensating for any omissions or omissions in the question. For example, an AI may receive a prompt such as "Please summarize the main points of this question," extract the main points of the question, and create a question. The answer range setting unit sets a range based on user information. The answer range setting unit sets a range of answers that are acceptable based on, for example, the user's personal information and behavioral history. The answer range setting unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "please do not disclose this information." The answer generation unit uses AI to answer questions generated by the question generation unit. For example, the answer generation unit generates appropriate answers based on user information. The answer generation unit can also provide related advertisements and promotional information. For example, the AI ​​provides information on related products and services along with answers to user questions. This enables the communication support system according to the embodiment to efficiently accept, analyze, generate questions, and generate answers based on user requests.

[0069] The question generation unit can analyze what the user wants to ask and generate a question. The question generation unit analyzes the content of the user's question using, for example, natural language processing technology. For example, the question generation unit extracts the main points of the user's question using a text analysis algorithm. The question generation unit can also generate a question by supplementing the question with a machine learning algorithm. For example, the question generation unit generates a question by adding relevant information based on the content of the user's question. The question generation unit can also use a generation AI to summarize the content of the user's question and generate a concise question. For example, the generation AI receives a prompt such as "Please summarize the main points of this question," and extracts the main points of the question to create a question. In this way, a more appropriate question can be generated by analyzing what the user wants to ask and supplementing the questions.

[0070] The answer generation unit allows the AI ​​to answer questions on behalf of the user based on the user's information. The answer generation unit generates answers to questions based on, for example, the user's personal information and behavioral history. For example, the answer generation unit generates appropriate answers based on the user's past behavioral history. The answer generation unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "do not disclose." The answer generation unit can also use the generation AI to generate appropriate answers based on the user's information. For example, the generation AI generates answers to the user's questions and provides them to the other party. In this way, the AI ​​answers on behalf of the user based on the range of information that is acceptable for the answer, thereby efficiently generating answers while properly managing the user's information.

[0071] The question accepting unit can estimate a user's emotions and adjust the method for accepting questions based on the user's emotions. For example, if the user is feeling stressed, the question accepting unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the question accepting unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the question accepting unit can prioritize voice input to enable the user to quickly enter a question. For example, the question accepting unit can capture a user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. Furthermore, the question accepting unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the emotion score can be calculated by analyzing the tone and speed of the voice. Furthermore, the question accepting unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the emotion score can be calculated based on fluctuations in heart rate. This allows for more appropriate question acceptance by adjusting the method for accepting questions based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] When accepting a question, the question acceptance unit can select an appropriate acceptance method by referring to the user's past question history. For example, the question acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The question acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The question acceptance unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. For example, the question acceptance unit selects the optimal question acceptance method by saving and analyzing the user's past question history in a database. In this way, the optimal question acceptance method can be selected by referring to the user's past question history.

[0073] The question receiving unit can filter questions based on the user's current situation and areas of interest when receiving questions. For example, the question receiving unit preferentially receives questions related to a project the user is currently working on. The question receiving unit can also automatically filter related questions based on the user's areas of interest. The question receiving unit can also select an appropriate question receiving method depending on the user's current situation (for example, in a meeting or on the move). For example, the question receiving unit collects the user's location information and activity status using a sensor to understand the current situation. The question receiving unit also stores the user's areas of interest in a database and analyzes them to filter related questions. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be received.

[0074] When accepting a question, the question acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the question acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question using text, the question acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the question acceptance unit can also accept the question using image recognition technology. For example, the question acceptance unit selects the optimal acceptance means depending on the user's input method. This enables more efficient question acceptance by selecting the optimal acceptance means depending on the user's input method.

[0075] The question receiving unit can estimate the user's emotions and determine the priority of questions to be received based on the user's emotions. For example, when the user is nervous, the question receiving unit can prioritize questions with high importance. Furthermore, when the user is relaxed, the question receiving unit can prioritize questions with detailed answers. Furthermore, when the user is in a hurry, the question receiving unit can prioritize questions that require a quick answer. For example, the question receiving unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. Furthermore, the question receiving unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score can be calculated by analyzing the tone and speed of the voice. Furthermore, the question receiving unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on fluctuations in heart rate. This allows for more appropriate question reception by determining the priority of questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] When accepting questions, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the question acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the question acceptance unit can also prioritize accepting questions related to the user's destination. Furthermore, if the user is overseas, the question acceptance unit can also prioritize accepting questions related to the country or area. For example, the question acceptance unit can obtain the user's location information using GPS data or a location information service and filter out relevant questions. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized.

[0077] The question accepting unit can analyze the user's social media activity when accepting a question and accept related questions. For example, the question accepting unit preferentially accepts questions related to topics in which the user has shown interest on social media. The question accepting unit can also analyze the content of the user's social media posts and accept related questions. The question accepting unit can also accept related questions by referring to the activities of the user's friends on social media. For example, the question accepting unit stores the user's social media activity in a database and analyzes it to filter related questions. In this way, related questions can be accepted by analyzing the user's social media activity.

[0078] The question reception unit can customize an appropriate reception method by reflecting the user's past feedback when receiving a question. The question reception unit can, for example, suggest an optimal question reception method based on feedback provided by the user in the past. The question reception unit can also preferentially receive specific question formats based on the user's past feedback. The question reception unit can also analyze the user's past feedback and customize the reception method. For example, the question reception unit selects an optimal question reception method by storing and analyzing the user's feedback in a database. In this way, the optimal question reception method can be customized by reflecting the user's past feedback.

[0079] The question generation unit can estimate the user's emotions and adjust the way the question is phrased based on the user's emotions. For example, if the user is nervous, the question generation unit uses a simple and clear way of phrase. Furthermore, if the user is relaxed, the question generation unit can use a detailed and polite way of phrase. Furthermore, if the user is in a hurry, the question generation unit can use a concise and quick way of phrase. For example, the question generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on changes in facial expression. Furthermore, the question generation unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score is calculated by analyzing the tone and speed of the voice. Furthermore, the question generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on fluctuations in heart rate. Thus, by adjusting the way the question is phrased based on the user's emotions, more appropriate questions can be generated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The question generation unit can adjust the level of detail of the question based on the importance of the question when generating the question. For example, the question generation unit includes a detailed explanation for a question with a high level of importance. The question generation unit can also include a concise explanation for a question with a low level of importance. The question generation unit can also appropriately add necessary information depending on the importance of the question. For example, the question generation unit analyzes the content of the user's question and adjusts the level of detail of the question using an algorithm that evaluates the importance. In this way, by adjusting the level of detail of the question based on the importance of the question, a more appropriate question can be generated.

[0081] When generating a question, the question generation unit can apply different question generation algorithms depending on the category of the question. For example, the question generation unit applies a specialized algorithm to technical questions. The question generation unit can also apply a general-purpose algorithm to general questions. The question generation unit can also apply a specific support algorithm to questions related to customer support. For example, the question generation unit analyzes the category of the question and selects an appropriate algorithm. In this way, more appropriate questions can be generated by applying different question generation algorithms depending on the category of the question.

[0082] When generating a question, the question generation unit can improve the accuracy of the question by referring to the user's past question results. The question generation unit improves the accuracy of the question, for example, based on answers the user has received in the past. The question generation unit can also select the optimal question format from the user's past question results. The question generation unit can also analyze the user's past question history to improve the accuracy of the question. For example, the question generation unit improves the accuracy of the question by saving the user's past question results in a database and analyzing them. In this way, the accuracy of the question is improved by referring to the user's past question results.

[0083] The question generation unit can estimate the user's emotions and adjust the length of the questions based on the user's emotions. For example, if the user is nervous, the question generation unit generates short, to-the-point questions. Furthermore, if the user is relaxed, the question generation unit can generate longer questions with detailed explanations. Furthermore, if the user is in a hurry, the question generation unit can generate concise, quick questions. For example, the question generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. For example, the emotion score is calculated based on changes in facial expressions. Furthermore, the question generation unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the emotion score is calculated by analyzing the tone and speed of the voice. Furthermore, the question generation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the emotion score is calculated based on fluctuations in heart rate. Thus, by adjusting the length of the questions according to the user's emotions, more appropriate questions can be generated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] When generating questions, the question generation unit can determine the priority of questions based on the time of submission of the questions. For example, the question generation unit generates questions with a high degree of urgency with priority. The question generation unit can also generate questions with a high priority that are due to be submitted soon with priority. The question generation unit can also determine the optimal order of question generation depending on the time of submission of the questions. For example, the question generation unit analyzes the content of a user's question and determines the priority of questions using an algorithm that evaluates the time of submission. In this way, by determining the priority of questions based on the time of submission of the question, more appropriate questions can be generated.

[0085] The question generation unit can adjust the order of questions based on the relevance of the questions when generating questions. For example, the question generation unit generates highly relevant questions with priority. The question generation unit can also determine the optimal order of questions based on the relevance of the questions. The question generation unit can also analyze the relevance of questions and propose an efficient order of generating questions. For example, the question generation unit analyzes the content of a user's question and adjusts the order of questions using an algorithm that evaluates the relevance. This allows for more efficient question generation by adjusting the order of questions based on the relevance of the questions.

[0086] When generating a question, the question generation unit can adjust the use of technical terms in the question according to the user's level of expertise. For example, if the user has technical knowledge, the question generation unit generates a question that uses a lot of technical terms. Furthermore, if the user has general knowledge, the question generation unit can also generate a question that avoids technical terms. Furthermore, the question generation unit can select an optimal question expression according to the user's level of expertise. For example, the question generation unit adjusts the use of technical terms in the question using an algorithm that evaluates the user's level of expertise. In this way, by adjusting the use of technical terms in the question according to the user's level of expertise, more appropriate questions can be generated.

[0087] The answer range setting unit can estimate the user's emotions and adjust the answer range setting method based on the user's emotions. For example, if the user is nervous, the answer range setting unit can set a simple and clear answer range. Furthermore, if the user is relaxed, the answer range setting unit can also set a detailed answer range. Furthermore, if the user is in a hurry, the answer range setting unit can also set a range that allows for quick answers. For example, the answer range setting unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. Furthermore, the answer range setting unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score can be calculated by analyzing the tone and speed of the voice. Furthermore, the answer range setting unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on heart rate fluctuations. Thus, by adjusting the answer range setting method according to the user's emotions, a more appropriate answer range can be set. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] When setting the answer range, the answer range setting unit can select an appropriate setting method by referring to the user's past answer history. The answer range setting unit sets the optimal answer range based on, for example, answers provided by the user in the past. The answer range setting unit can also preferentially set a specific answer format based on the user's past answer history. The answer range setting unit can also analyze the user's past answer history and optimize the answer range. For example, the answer range setting unit selects the optimal answer range setting method by saving and analyzing the user's past answer history in a database. In this way, the optimal answer range setting method can be selected by referring to the user's past answer history.

[0089] When setting the answer range, the answer range setting unit can perform filtering based on the user's current situation and areas of interest. For example, the answer range setting unit prioritizes setting an answer range related to a project the user is currently working on. The answer range setting unit can also automatically filter related answer ranges based on the user's areas of interest. The answer range setting unit can also set an appropriate answer range depending on the user's current situation (for example, in a meeting or on the move). For example, the answer range setting unit collects the user's location information and activity status using a sensor to grasp the current situation. The answer range setting unit also filters out related answer ranges by saving the user's areas of interest in a database and analyzing them. In this way, by filtering the answer range based on the user's current situation and areas of interest, a more relevant answer range can be set.

[0090] When setting the answer range, the answer range setting unit can select an appropriate setting means depending on the user's input method. For example, when the user sets the answer range by voice, the answer range setting unit sets it using voice recognition technology. Furthermore, when the user sets the answer range by text, the answer range setting unit can also set it using text analysis technology. Furthermore, when the user sets the answer range using an image, the answer range setting unit can also set it using image recognition technology. For example, the answer range setting unit selects the optimal setting means depending on the user's input method. This enables more efficient answer range setting by selecting the optimal setting means depending on the user's input method.

[0091] The answer range setting unit can estimate the user's emotions and determine the priority of answer ranges to be set based on the user's emotions. For example, if the user is nervous, the answer range setting unit can prioritize answer ranges with high importance. Furthermore, if the user is relaxed, the answer range setting unit can prioritize detailed answer ranges. Furthermore, if the user is in a hurry, the answer range setting unit can prioritize ranges that can be answered quickly. For example, the answer range setting unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. Furthermore, the answer range setting unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the voice can be analyzed to calculate the emotion score. Furthermore, the answer range setting unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on fluctuations in heart rate. In this way, the priority of answer ranges can be determined according to the user's emotions, thereby setting a more appropriate answer range. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] When setting the answer range, the answer range setting unit can prioritize a highly relevant range by taking into account the user's geographical location information. For example, if the user is in a specific area, the answer range setting unit can prioritize an answer range related to that area. Furthermore, if the user is traveling, the answer range setting unit can also prioritize an answer range related to the user's destination. Furthermore, if the user is overseas, the answer range setting unit can also prioritize an answer range related to that country or area. For example, the answer range setting unit obtains the user's location information using GPS data or a location information service and filters the relevant answer ranges. In this way, it is possible to prioritize a highly relevant answer range by taking into account the user's geographical location information.

[0093] When setting the answer range, the answer range setting unit can analyze the user's social media activity and set the relevant range. For example, the answer range setting unit prioritizes setting the answer range related to topics in which the user has shown interest on social media. The answer range setting unit can also analyze the content of the user's posts on social media and set the relevant answer range. The answer range setting unit can also set the relevant answer range by referring to the activities of the user's friends on social media. For example, the answer range setting unit filters the relevant answer range by storing and analyzing the user's social media activity in a database. In this way, the relevant answer range can be set by analyzing the user's social media activity.

[0094] The answer range setting unit can customize an appropriate setting method by reflecting the user's past feedback when setting the answer range. The answer range setting unit sets an optimal answer range based on, for example, feedback provided by the user in the past. The answer range setting unit can also prioritize a specific answer format based on the user's past feedback. The answer range setting unit can also analyze the user's past feedback and customize the setting method. For example, the answer range setting unit selects an optimal answer range setting method by saving and analyzing the user's feedback in a database. In this way, the optimal answer range setting method can be customized by reflecting the user's past feedback.

[0095] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the user's emotions. For example, if the user is nervous, the answer generation unit uses a simple and clear expression. Furthermore, if the user is relaxed, the answer generation unit can use a detailed and polite expression. Furthermore, if the user is in a hurry, the answer generation unit can use a concise and quick expression. For example, the answer generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. The answer generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score can be calculated by analyzing the tone and speed of the voice. The answer generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on fluctuations in heart rate. This allows the way the answer is expressed to be adjusted according to the user's emotions, thereby generating 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The answer generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the answer generation unit includes a detailed explanation for a question with a high level of importance. The answer generation unit can also include a concise explanation for a question with a low level of importance. The answer generation unit can also appropriately add necessary information depending on the importance of the question. For example, the answer generation unit analyzes the content of the user's question and adjusts the level of detail of the answer using an algorithm that evaluates the importance. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be generated.

[0097] When generating an answer, the answer generation unit can apply different answer generation algorithms depending on the category of the question. For example, the answer generation unit applies a specialized algorithm to technical questions. The answer generation unit can also apply a general-purpose algorithm to general questions. The answer generation unit can also apply a specific support algorithm to questions regarding customer support. For example, the answer generation unit analyzes the category of the question and selects an appropriate algorithm. In this way, by applying different answer generation algorithms depending on the category of the question, a more appropriate answer can be generated.

[0098] When generating an answer, the answer generation unit can improve the accuracy of the answer by referring to the user's past answer results. The answer generation unit improves the accuracy of the answer, for example, based on answers the user has received in the past. The answer generation unit can also select the optimal answer format from the user's past answer results. The answer generation unit can also analyze the user's past answer history to improve the accuracy of the answer. For example, the answer generation unit improves the accuracy of the answer by saving the user's past answer results in a database and analyzing them. In this way, the accuracy of the answer is improved by referring to the user's past answer results.

[0099] The answer generation unit can estimate the user's emotions and adjust the length of the answer based on the user's emotions. For example, if the user is nervous, the answer generation unit generates a short, to-the-point answer. Furthermore, if the user is relaxed, the answer generation unit can generate a longer answer with detailed explanations. Furthermore, if the user is in a hurry, the answer generation unit can generate a concise, quick answer. For example, the answer generation unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. For example, the emotion score is calculated based on changes in facial expression. The answer generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score is calculated by analyzing the tone and speed of the voice. The answer generation unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score is calculated based on heart rate fluctuations. By adjusting the length of the answer according to the user's emotions, a more appropriate answer can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] When generating answers, the answer generation unit can determine the priority of answers based on the time of submission of the question. For example, the answer generation unit generates answers with priority to questions with high urgency. The answer generation unit can also generate answers with priority to questions that will be submitted soon. The answer generation unit can also determine the optimal order of answer generation depending on the time of submission of the questions. For example, the answer generation unit analyzes the content of the user's question and determines the priority of answers using an algorithm that evaluates the time of submission. In this way, by determining the priority of answers based on the time of submission of the question, more appropriate answers can be generated.

[0101] The answer generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the answer generation unit preferentially generates answers to questions with high relevance. The answer generation unit can also determine the optimal order of answers according to the relevance of the questions. The answer generation unit can also analyze the relevance of the questions and propose an efficient order of answer generation. For example, the answer generation unit analyzes the content of the user's question and adjusts the order of answers using an algorithm that evaluates the relevance. This allows for more efficient answer generation by adjusting the order of answers based on the relevance of the questions.

[0102] When generating an answer, the answer generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user has technical knowledge, the answer generation unit generates an answer that uses a lot of technical terms. Furthermore, if the user has general knowledge, the answer generation unit can also generate an answer that avoids technical terms. Furthermore, the answer generation unit can select an optimal answer expression according to the user's level of expertise. For example, the answer generation unit adjusts the use of technical terms in the answer using an algorithm that evaluates the user's level of expertise. In this way, by adjusting the use of technical terms in the answer according to the user's level of expertise, a more appropriate answer can be generated.

[0103] The answer generation unit can provide relevant advertising or promotional information when the AI ​​answers a question on behalf of the user. For example, the answer generation unit provides information on related products and services along with the answer to the user's question. The answer generation unit can also select appropriate advertising or promotional information based on the user's interests. For example, the answer generation unit analyzes the content of the user's question and provides advertising or promotional information using an algorithm that displays relevant advertisements. This can create new advertising markets and marketing opportunities by providing related advertising or promotional information along with the answer to the question. === Hard Collateral 1-1 === Each of the multiple elements including the question reception unit, question generation unit, answer range setting unit, and answer generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the question reception unit receives questions from the user using the reception device 38 of the smart device 14. The question generation unit is realized by the specification processing unit 290 of the data processing device 12 and generates questions using AI. The answer range setting unit sets a range based on user information using the specification processing unit 290 of the data processing device 12. The answer generation unit has AI answer questions on behalf of the user using the specification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the question receiving unit, question generating unit, answer range setting unit, and answer generating unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the question receiving unit receives a question that the user wants to ask using the microphone 238 of the smart glasses 214. The question generating unit is realized by the specific processing unit 290 of the data processing device 12 and generates a question using AI. The answer range setting unit sets a range based on user information by the specific processing unit 290 of the data processing device 12. The answer generating unit has AI answer questions on behalf of the user by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the question receiving unit, question generating unit, answer range setting unit, and answer generating unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the question receiving unit receives questions that the user wants to ask using the microphone 238 of the headset type terminal 314. The question generating unit is realized by the identification processing unit 290 of the data processing device 12, and generates questions using AI. The answer range setting unit sets a range based on user information by the identification processing unit 290 of the data processing device 12. The answer generating unit has AI answer questions on behalf of the user, as determined by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the question receiving unit, question generating unit, answer range setting unit, and answer generating unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the question receiving unit receives questions that the user wants to ask using the microphone 238 of the robot 414. The question generating unit is realized by the identification processing unit 290 of the data processing device 12, and generates questions using AI. The answer range setting unit sets a range based on user information by the identification processing unit 290 of the data processing device 12. The answer generating unit has AI answer questions on behalf of the user by the identification processing unit 290 of the data processing device 12.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The question reception unit can select an appropriate reception method by referring to the user's past question history. For example, the question reception unit can automatically display questions that the user has frequently asked in the past as candidates. The question reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The question reception unit can also predict and suggest questions that will be asked during a specific time period based on the user's past question history. For example, the question reception unit selects the optimal question reception method by saving and analyzing the user's past question history in a database. In this way, the optimal question reception method can be selected by referring to the user's past question history.

[0106] The question generation unit can estimate the user's emotions and adjust the way the question is phrased based on the user's emotions. For example, if the user is nervous, the question generation unit can use a simple and clear way of phrase. If the user is relaxed, the question generation unit can use a detailed and polite way of phrase. If the user is in a hurry, the question generation unit can use a concise and quick way of phrase. For example, the question generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. The question generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score can be calculated by analyzing the tone and speed of the voice. The question generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on fluctuations in heart rate. In this way, the way the question is phrased can be adjusted according to the user's emotions, thereby generating more appropriate questions.

[0107] The answer generation unit allows the AI ​​to answer questions on behalf of the user based on the user's information. For example, the answer generation unit generates answers to questions based on the user's personal information and behavioral history. For example, the answer generation unit generates appropriate answers based on the user's past behavioral history. The answer generation unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "do not disclose." The answer generation unit can also use the generation AI to generate appropriate answers based on the user's information. For example, the generation AI generates answers to the user's questions and provides them to the other party. In this way, the AI ​​answers on behalf of the user based on the range of information that is acceptable for the answer, allowing answers to be generated efficiently while properly managing the user's information.

[0108] The question accepting unit can estimate a user's emotions and adjust the method for accepting questions based on the user's emotions. For example, if the user is feeling stressed, the question accepting unit can provide a simple interface and minimize input steps. If the user is relaxed, the question accepting unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the question accepting unit can prioritize voice input to enable quick question entry. For example, the question accepting unit can capture a user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, the question accepting unit can calculate an emotion score based on changes in facial expression. The question accepting unit can also record the user's voice and estimate the user's emotions using voice analysis technology. For example, the question accepting unit can analyze the tone and speed of the voice and calculate an emotion score. The question accepting unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the question accepting unit can calculate an emotion score based on fluctuations in heart rate. This allows the method for accepting questions to be adjusted according to the user's emotions, enabling more appropriate question acceptance.

[0109] When accepting a question, the question acceptance unit can select an appropriate acceptance method by referring to the user's past question history. For example, the question acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The question acceptance unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The question acceptance unit can also predict and suggest questions that will be asked during a specific time period based on the user's past question history. For example, the question acceptance unit selects the optimal question acceptance method by saving and analyzing the user's past question history in a database. In this way, the optimal question acceptance method can be selected by referring to the user's past question history.

[0110] When accepting questions, the question acceptance unit can filter questions based on the user's current situation and areas of interest. For example, questions related to a project the user is currently working on are preferentially accepted. The question acceptance unit can also automatically filter related questions based on the user's areas of interest. The question acceptance unit can also select an appropriate question acceptance method depending on the user's current situation (for example, in a meeting or on the move). For example, the question acceptance unit collects the user's location information and activity status using a sensor to understand the current situation. The question acceptance unit also stores the user's areas of interest in a database and analyzes them to filter out related questions. In this way, by filtering questions based on the user's current situation and areas of interest, more relevant questions can be accepted.

[0111] When accepting a question, the question acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user inputs a question by voice, the question acceptance unit can accept the question using voice recognition technology. Furthermore, if the user inputs a question using text, the question acceptance unit can also accept the question using text analysis technology. Furthermore, if the user inputs a question using an image, the question acceptance unit can also accept the question using image recognition technology. For example, the question acceptance unit selects the optimal acceptance means depending on the user's input method. This allows for more efficient question acceptance by selecting the optimal acceptance means depending on the user's input method.

[0112] The question receiving unit can estimate the user's emotions and determine the priority of questions to be received based on the user's emotions. For example, if the user is nervous, the question receiving unit can prioritize questions of high importance. Furthermore, if the user is relaxed, the question receiving unit can prioritize questions that require a quick answer. Furthermore, if the user is in a hurry, the question receiving unit can prioritize questions that require a quick answer. For example, the question receiving unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. Furthermore, the question receiving unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the emotion score can be calculated by analyzing the tone and speed of the voice. Furthermore, the question receiving unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on fluctuations in heart rate. This allows for more appropriate question reception by prioritizing questions according to the user's emotions.

[0113] When accepting questions, the question acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, it can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the question acceptance unit can prioritize accepting questions related to the user's destination. Furthermore, if the user is overseas, the question acceptance unit can prioritize accepting questions related to that country or area. For example, the question acceptance unit can obtain the user's location information using GPS data or a location information service and filter out relevant questions. In this way, it is possible to prioritize accepting highly relevant questions by taking into account the user's geographical location information.

[0114] When accepting a question, the question acceptance unit can analyze the user's social media activity and accept related questions. For example, it can preferentially accept questions related to topics in which the user has shown interest on social media. The question acceptance unit can also analyze the content of the user's social media posts and accept related questions. The question acceptance unit can also accept related questions by referring to the activity of the user's friends on social media. For example, the question acceptance unit stores the user's social media activity in a database and analyzes it to filter related questions. In this way, it is possible to accept related questions by analyzing the user's social media activity.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The question receiving unit receives a question from the user. The question the user wants to ask may include, but is not limited to, a question about work or a personal question. For example, the question receiving unit receives a question input by the user in text format. The question receiving unit can also receive a question using voice input. For example, using voice recognition technology, the user's voice is converted into text and accepted as a question. Step 2: The question generation unit uses AI to analyze the content received by the question reception unit and generate a question. The question generation unit uses, for example, natural language processing technology to analyze the content of the user's question. The question generation unit can also use a machine learning algorithm to generate a question by compensating for any omissions or deficiencies in the question. For example, the AI ​​receives a prompt such as "Please summarize the main points of this question," and extracts the main points of the question to create a question. Step 3: The answer range setting unit sets the range based on the user's information. The answer range setting unit sets the range of answers that can be answered based on, for example, the user's personal information or behavioral history. The answer range setting unit can also exclude specific information to protect the user's privacy. For example, it excludes information that the user has specified as "Do not disclose this information." Step 4: In the answer generation unit, the AI ​​answers the questions generated by the question generation unit on behalf of the user. For example, the answer generation unit generates an appropriate answer based on the user's information. The answer generation unit can also provide related advertisements and promotional information. For example, the AI ​​provides information on related products and services along with the answer to the user's question.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 question receiving unit that receives questions from users; a question generation unit that analyzes the content received by the question reception unit and generates a question; an answer range setting unit that sets a range based on user information; an answer generation unit that uses AI to answer questions generated by the question generation unit; Equipped with A system characterized by:

2. The question generation unit Analyze what the user wants to ask and generate questions 2. The system of claim 1.

3. The answer generation unit AI answers questions based on user information 2. The system of claim 1.

4. The question receiving unit Estimate the user's emotions and adjust the way questions are received based on the user's emotions 2. The system of claim 1.

5. The question receiving unit When accepting a question, the system selects the appropriate method of acceptance by referring to the user's past question history.

2. The system of claim 1.

6. The question receiving unit Filter questions based on the user's current situation or interests 2. The system of claim 1.

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

2. The system of claim 1.

8. The question receiving unit Estimate user emotions and prioritize questions based on user emotions 2. The system of claim 1.

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