System

Abstract

An object of a system according to an embodiment is to extract an answerer's intent with high accuracy and provide feedback.SOLUTION: A system includes a generation AI, an interactive questionnaire execution unit, an answer analysis unit, and a feedback unit. The generating AI implements an interactive questionnaire. The interactive questionnaire execution part executes an interactive questionnaire. The answer analysis unit analyzes the answer content obtained by the interactive questionnaire execution unit. The feedback unit feeds back the result analyzed by the answer analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that the accuracy of questionnaire responses is low, making it difficult to accurately grasp the true intentions of respondents.

[0005] The system according to the embodiment aims to extract the true intention of the respondent with high accuracy and provide feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an interactive survey implementation unit, a response analysis unit, and a feedback unit. The generation AI implements an interactive survey. The interactive survey implementation unit implements the interactive survey. The response analysis unit analyzes the response content obtained by the interactive survey implementation unit. The feedback unit feeds back the results analyzed by the response analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can extract the true intention of the respondent with high accuracy and provide feedback. [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) The questionnaire system according to the embodiment of the present invention is a system that uses a generative AI to conduct an interactive questionnaire, extract the true intentions of respondents with high accuracy, and provide feedback. As a result, the questionnaire system can extract the true intentions of respondents with higher accuracy than conventional questionnaires and provide feedback.

[0029] A questionnaire system according to an embodiment includes a generation AI, an interactive questionnaire implementation unit, a response analysis unit, and a feedback unit. The generation AI includes an interactive questionnaire implementation unit that implements an interactive questionnaire. For example, the generation AI analyzes input from a user and generates appropriate questions. The generation AI can ask a question such as, "Please tell me why you chose this product." The generation AI also includes a response analysis unit that analyzes the content of the responses obtained from the respondents. For example, if the respondent answers, "This product is easy to use," the generation AI can further dig into the specific reasons why the respondent feels it is "easy to use." The generation AI also includes a feedback unit that feeds back the results of the analysis by the response analysis unit. For example, the generation AI can save the content of the responses as text data and analyze it later. This allows the questionnaire system according to an embodiment to accurately extract the respondent's true intentions and provide feedback.

[0030] The interactive survey implementation unit can refer to the respondent's past response history and generate consistent questions. For example, the generation AI in the interactive survey implementation unit retrieves the respondent's past response history from a database during a dialogue and generates consistent questions. For example, a respondent who previously placed importance on "ease of use" is asked about specific elements of usability. The generation AI in the interactive survey implementation unit also analyzes the respondent's past response history during a dialogue and generates questions that are consistent. For example, a respondent who previously placed importance on "design" is asked about specific improvements to the design. The generation AI in the interactive survey implementation unit also refers to the respondent's past response history during a dialogue and generates questions based on the respondent's interests. For example, a respondent who previously placed importance on "price" is asked detailed questions about price. This makes it possible to ask consistent questions based on the respondent's past response history.

[0031] The interactive survey implementation unit generates questions in multiple languages ​​simultaneously and can conduct an international survey. For example, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions in multiple languages ​​simultaneously and conducts an international survey. For example, questions are asked simultaneously in English, French, Chinese, etc. Furthermore, when the generation AI conducts an interactive survey, the interactive survey implementation unit automatically translates the questions according to the respondent's language setting and conducts an international survey. For example, if the respondent selects Spanish, the questions are asked in Spanish. Furthermore, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions in multiple languages ​​simultaneously and builds a system for conducting an international survey. For example, questions supervised by experts in each language are used. This makes it possible to generate questions in multiple languages ​​simultaneously and conduct an international survey.

[0032] The interactive survey implementation unit can generate questions that take into consideration the respondent's living environment and cultural background. For example, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions that take into consideration the respondent's living environment and cultural background. For example, respondents living in urban areas are asked questions about transportation. Furthermore, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions that take into consideration the respondent's cultural background. For example, questions are asked that are based on the customs and values ​​of a particular culture. Furthermore, the interactive survey implementation unit builds a system that generates questions that take into consideration the respondent's living environment and cultural background when the generation AI conducts an interactive survey. For example, questions that reflect the characteristics of each region are asked. This makes it possible to generate questions that take into consideration the respondent's living environment and cultural background.

[0033] When analyzing a user's input, the interactive survey implementation unit can understand the context of the input content and generate questions by referring to related background information. For example, when the generation AI analyzes a user's input, the interactive survey implementation unit understands the context of the input content and generates questions by referring to related background information. For example, if a user inputs "this product is expensive," the interactive survey implementation unit asks detailed questions about the price. Furthermore, the interactive survey implementation unit builds a system in which, when the generation AI analyzes a user's input, the interactive survey implementation unit understands the context of the input content and generates questions by referring to related background information. For example, questions are asked based on past purchase history. Furthermore, when the generation AI analyzes a user's input, the interactive survey implementation unit understands the context of the input content and generates questions by referring to related background information. For example, if a user inputs "easy to use," the interactive survey implementation unit asks about specific elements of usability. This makes it possible to understand the context of the user's input and generate questions by referring to related background information.

[0034] The interactive survey implementation unit can detect ambiguity in the input content when analyzing the user's input and generate follow-up questions for clarification. For example, when the generation AI analyzes the user's input, the interactive survey implementation unit detects ambiguity in the input content and generates follow-up questions for clarification. For example, if the user inputs "good," the interactive survey implementation unit asks what specifically is good about it. The interactive survey implementation unit also builds a system that detects ambiguity in the input content when the generation AI analyzes the user's input and generates follow-up questions for clarification. For example, it detects ambiguous expressions and asks detailed questions. The interactive survey implementation unit can also detect ambiguity in the input content when the generation AI analyzes the user's input and generate follow-up questions for clarification. For example, if the user inputs "easy to use," the interactive survey implementation unit asks about specific elements of usability. This makes it possible to detect ambiguity in the user's input and generate follow-up questions for clarification.

[0035] The interactive survey implementation unit can also analyze the user's voice input and image input and generate multimodal questions. In the interactive survey implementation unit, for example, the generation AI analyzes the user's voice input and generates appropriate questions from the voice data. For example, if the user voice-inputs, "What do you think of this product?", the system will ask detailed questions. In addition, the interactive survey implementation unit can analyze the user's image input and generate appropriate questions from the image data. For example, if the user uploads a photo of a product, the system will ask questions based on that photo. In addition, the interactive survey implementation unit can build a system in which the generation AI simultaneously analyzes the user's voice input and image input and generates multimodal questions. For example, questions are asked based on both voice and images. This makes it possible to analyze the user's voice input and image input and generate multimodal questions.

[0036] The interactive survey implementation unit can generate questions by referring to expertise in different industries and fields. For example, when the generation AI analyzes a user's input, the interactive survey implementation unit generates questions by referring to expertise in different industries and fields. For example, a health-related question is asked based on expertise in the medical field. The interactive survey implementation unit also builds a system in which, when the generation AI analyzes a user's input, the interactive survey implementation unit generates questions by referring to expertise in different industries and fields. For example, a technical question is asked based on expertise in the technology field. The interactive survey implementation unit also generates questions by referring to expertise in different industries and fields when the generation AI analyzes a user's input. For example, a market-related question is asked based on expertise in the marketing field. This makes it possible to generate questions by referring to expertise in different industries and fields.

[0037] The answer analysis unit refers to the respondent's past answer history and behavioral history, and is able to grasp the true intention with high accuracy. For example, when the generation AI analyzes the content of an answer, the answer analysis unit retrieves the respondent's past answer history from a database and grasps the true intention with high accuracy. For example, it analyzes in detail the answers of a respondent who has placed importance on "ease of use" in the past. In addition, when the generation AI analyzes the content of an answer, the answer analysis unit refers to the respondent's behavioral history and grasps the true intention with high accuracy. For example, it analyzes the content of an answer based on past purchase history and usage history. In addition, when the generation AI analyzes the content of an answer, the answer analysis unit refers to the respondent's past answer history and behavioral history, and builds a system that grasps the true intention with high accuracy. For example, it checks the consistency between past answers and current answers. This makes it possible to grasp the true intention with high accuracy by referring to the respondent's past answer history and behavioral history.

[0038] The answer analysis unit can extract potential needs and problems behind the answer content. For example, when the generation AI analyzes the answer content, the answer analysis unit extracts potential needs behind the answer content. For example, if the answer is "easy to use," the specific elements of that answer are analyzed. In addition, when the generation AI analyzes the answer content, the answer analysis unit extracts problems behind the answer content. For example, if the answer is "expensive," the specific points of dissatisfaction with the price are analyzed. In addition, the answer analysis unit builds a system that extracts potential needs and problems behind the answer content when the generation AI analyzes the answer content. For example, the answer content is analyzed in detail to discover hidden needs. This makes it possible to extract potential needs and problems behind the answer content.

[0039] The answer analysis unit compares answers from different cultures and regions, and can grasp the true meaning from a global perspective. For example, when the generation AI analyzes the content of an answer, the answer analysis unit compares answers from different cultures and regions, and can grasp the true meaning from a global perspective. For example, it analyzes answers from different cultures to the same question. Furthermore, when the generation AI analyzes the content of an answer, the answer analysis unit compares answers from different regions, and builds a system that grasps the true meaning from a global perspective. For example, it analyzes answer trends by region. Furthermore, when the generation AI analyzes the content of an answer, the answer analysis unit compares answers from different cultures and regions, and can grasp the true meaning from a global perspective. For example, it asks questions that take cultural background into consideration. This makes it possible to compare answers from different cultures and regions, and can grasp the true meaning from a global perspective.

[0040] The answer analysis unit visualizes the answer content, making it possible to grasp the true meaning in a format that is visually easy to understand. For example, the answer analysis unit uses a generation AI to visualize the answer content and grasp the true meaning in a format that is visually easy to understand. For example, the answer content is displayed in a graph or chart. The answer analysis unit also builds a system in which the generation AI visualizes the answer content and grasps the true meaning in a format that is visually easy to understand. For example, the answer content is displayed in a mind map. The answer analysis unit also visualizes the answer content and grasps the true meaning in a format that is visually easy to understand. For example, the answer content is displayed in an infographic. This makes it possible to visualize the answer content and grasp the true meaning in a format that is visually easy to understand.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The questionnaire system may further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit may, for example, analyze what websites the user has visited in the past and what products the user has purchased, and generate questions based on this. For example, if the user has purchased many electronic devices in the past, detailed questions about the electronic devices may be asked. The behavior analysis unit may also analyze the user's social media activity and generate questions based on this. For example, if the user frequently mentions a particular brand, questions about that brand may be asked. Furthermore, the behavior analysis unit may analyze the user's location information and generate questions specific to a region. For example, if the user frequently visits a particular region, questions about that region may be asked. This makes it possible to generate questions based on the user's behavioral history.

[0043] The questionnaire system may further include a health analysis unit that monitors the user's health condition. The health analysis unit may, for example, analyze data acquired from the user's wearable device and generate questions based on the data. For example, the health analysis unit may analyze the user's heart rate and sleep patterns and ask health-related questions. The health analysis unit may also analyze the user's food records and generate questions based on the data. For example, if the user frequently eats a particular food ingredient, the health analysis unit may ask questions about that food ingredient. The health analysis unit may also analyze the user's exercise history and generate questions based on the data. For example, if the user frequently performs a particular exercise, the health analysis unit may ask questions about that exercise. This allows questions to be generated based on the user's health condition.

[0044] The questionnaire system may further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit, for example, analyzes data on products purchased by the user in the past and generates questions based on this. For example, if the user has purchased many cosmetics in the past, detailed questions about the cosmetics may be asked. The purchase analysis unit may also analyze the user's purchasing patterns and generate questions based on this. For example, if the user frequently purchases products from a particular brand, questions about that brand may be asked. Furthermore, the purchase analysis unit may analyze the user's purchasing history and suggest new products based on this. For example, new products related to products purchased by the user in the past may be suggested. This makes it possible to generate questions based on the user's purchasing history.

[0045] The questionnaire system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit may, for example, analyze events and activities the user has participated in in the past and generate questions based on this. For example, if the user has participated in many music events in the past, detailed questions about music may be asked. The hobby analysis unit may also analyze the user's social media activity and generate questions based on this. For example, if the user frequently mentions a particular artist, questions about that artist may be asked. The hobby analysis unit may also analyze the user's reading history and generate questions based on this. For example, if the user frequently reads books of a particular genre, questions about that genre may be asked. This makes it possible to generate questions based on the user's hobbies and interests.

[0046] The questionnaire system may further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit may, for example, analyze online courses or learning programs that the user has taken in the past and generate questions based on this. For example, if the user has taken many programming courses in the past, detailed questions about programming may be asked. The learning analysis unit may also analyze the user's learning outcomes and generate questions based on this. For example, if the user has acquired a specific skill, questions related to that skill may be asked. Furthermore, the learning analysis unit may analyze the user's learning history and suggest new learning opportunities based on this. For example, it may suggest new courses related to courses the user has taken in the past. This makes it possible to generate questions based on the user's learning history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The interactive survey implementation unit uses the generation AI to conduct an interactive survey. For example, the generation AI analyzes input from the user and generates appropriate questions. Specifically, it can ask questions such as, "Please tell us why you chose this product." Step 2: The response analysis unit analyzes the responses obtained by the interactive questionnaire implementation unit. For example, if a respondent answers "because this product is easy to use," the unit can ask more detailed questions about the specific reasons why they feel the product is "easy to use." Step 3: The feedback unit feeds back the results of the analysis by the response analysis unit. For example, the generation AI can save the responses as text data and analyze them later. This allows the survey system to accurately extract the respondent's true intentions and provide feedback.

[0049] (Example 2) The questionnaire system according to the embodiment of the present invention is a system that uses a generative AI to conduct an interactive questionnaire, extract the true intentions of respondents with high accuracy, and provide feedback. As a result, the questionnaire system can extract the true intentions of respondents with higher accuracy than conventional questionnaires and provide feedback.

[0050] A questionnaire system according to an embodiment includes a generation AI, an interactive questionnaire implementation unit, a response analysis unit, and a feedback unit. The generation AI includes an interactive questionnaire implementation unit that implements an interactive questionnaire. For example, the generation AI analyzes input from a user and generates appropriate questions. The generation AI can ask a question such as, "Please tell me why you chose this product." The generation AI also includes a response analysis unit that analyzes the content of the responses obtained from the respondents. For example, if the respondent answers, "This product is easy to use," the generation AI can further dig into the specific reasons why the respondent feels it is "easy to use." The generation AI also includes a feedback unit that feeds back the results of the analysis by the response analysis unit. For example, the generation AI can save the content of the responses as text data and analyze it later. This allows the questionnaire system according to an embodiment to accurately extract the respondent's true intentions and provide feedback.

[0051] The interactive survey implementation unit can analyze the respondent's facial expressions and tone of voice, detect changes in emotion in real time, and adjust the content of questions accordingly. For example, the generation AI captures the respondent's facial expressions with a camera during the conversation and uses facial expression analysis technology to detect changes in emotion in real time. For example, if the respondent looks confused, the questions are simplified. The generation AI also collects the respondent's tone of voice with a microphone during the conversation and uses voice analysis technology to detect changes in emotion in real time. For example, if the respondent speaks in an excited tone, the generation AI asks more detailed questions. The generation AI also simultaneously analyzes the respondent's facial expressions and tone of voice during the conversation, comprehensively assessing changes in emotion and adjusting the content of questions accordingly. For example, if the respondent looks tired and speaks in a low tone, the generation AI shortens the questions. This makes it possible to adjust the content of questions according to the respondent's emotions.

[0052] The interactive survey implementation unit can refer to the respondent's past response history and generate consistent questions. For example, the generation AI in the interactive survey implementation unit retrieves the respondent's past response history from a database during a dialogue and generates consistent questions. For example, a respondent who previously placed importance on "ease of use" is asked about specific elements of usability. The generation AI in the interactive survey implementation unit also analyzes the respondent's past response history during a dialogue and generates questions that are consistent. For example, a respondent who previously placed importance on "design" is asked about specific improvements to the design. The generation AI in the interactive survey implementation unit also refers to the respondent's past response history during a dialogue and generates questions based on the respondent's interests. For example, a respondent who previously placed importance on "price" is asked detailed questions about price. This makes it possible to ask consistent questions based on the respondent's past response history.

[0053] The interactive survey implementation unit uses the emotion estimation function to generate questions according to the respondent's emotional state and can conduct a dialogue that elicits positive emotions. In the interactive survey implementation unit, for example, the generation AI uses the emotion estimation function to analyze the respondent's emotional state in real time and generate questions that elicit positive emotions. For example, if the respondent is relaxed, more detailed questions are asked. In addition, in the interactive survey implementation unit, the generation AI uses the emotion estimation function to generate questions according to the respondent's emotional state and conduct a dialogue that elicits positive emotions. For example, if the respondent is excited, questions that arouse interest are asked. In addition, in the interactive survey implementation unit, the generation AI uses the emotion estimation function to analyze the respondent's emotional state and generate questions to elicit positive emotions. For example, if the respondent is feeling anxious, questions that give a sense of security are asked. In this way, questions according to the respondent's emotional state can be generated and positive emotions can be elicited.

[0054] The interactive survey implementation unit generates questions in multiple languages ​​simultaneously and can conduct an international survey. For example, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions in multiple languages ​​simultaneously and conducts an international survey. For example, questions are asked simultaneously in English, French, Chinese, etc. Furthermore, when the generation AI conducts an interactive survey, the interactive survey implementation unit automatically translates the questions according to the respondent's language setting and conducts an international survey. For example, if the respondent selects Spanish, the questions are asked in Spanish. Furthermore, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions in multiple languages ​​simultaneously and builds a system for conducting an international survey. For example, questions supervised by experts in each language are used. This makes it possible to generate questions in multiple languages ​​simultaneously and conduct an international survey.

[0055] The interactive survey implementation unit can generate questions that take into consideration the respondent's living environment and cultural background. For example, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions that take into consideration the respondent's living environment and cultural background. For example, respondents living in urban areas are asked questions about transportation. Furthermore, when the generation AI conducts an interactive survey, the interactive survey implementation unit generates questions that take into consideration the respondent's cultural background. For example, questions are asked that are based on the customs and values ​​of a particular culture. Furthermore, the interactive survey implementation unit builds a system that generates questions that take into consideration the respondent's living environment and cultural background when the generation AI conducts an interactive survey. For example, questions that reflect the characteristics of each region are asked. This makes it possible to generate questions that take into consideration the respondent's living environment and cultural background.

[0056] The interactive survey implementation unit can use the emotion estimation function to monitor the emotions of respondents in real time during the interactive survey and provide feedback according to their emotions. In the interactive survey implementation unit, for example, the generation AI uses the emotion estimation function to monitor the emotions of respondents in real time during the interactive survey and provide feedback according to their emotions. For example, if the respondent is feeling anxious, feedback that gives a sense of security is provided. In addition, the interactive survey implementation unit builds a system in which the generation AI uses the emotion estimation function to monitor the emotions of respondents in real time during the interactive survey and provide feedback according to their emotions. For example, feedback that elicits positive emotions is provided. In addition, the interactive survey implementation unit can use the emotion estimation function to monitor the emotions of respondents in real time during the interactive survey and provide feedback according to their emotions. For example, if the respondent is excited, more detailed questions are asked. In this way, the emotions of respondents can be monitored in real time during the interactive survey and provide feedback according to their emotions.

[0057] When analyzing a user's input, the interactive survey implementation unit can understand the context of the input content and generate questions by referring to related background information. For example, when the generation AI analyzes a user's input, the interactive survey implementation unit understands the context of the input content and generates questions by referring to related background information. For example, if a user inputs "this product is expensive," the interactive survey implementation unit asks detailed questions about the price. Furthermore, the interactive survey implementation unit builds a system in which, when the generation AI analyzes a user's input, the interactive survey implementation unit understands the context of the input content and generates questions by referring to related background information. For example, questions are asked based on past purchase history. Furthermore, when the generation AI analyzes a user's input, the interactive survey implementation unit understands the context of the input content and generates questions by referring to related background information. For example, if a user inputs "easy to use," the interactive survey implementation unit asks about specific elements of usability. This makes it possible to understand the context of the user's input and generate questions by referring to related background information.

[0058] The interactive survey implementation unit can detect ambiguity in the input content when analyzing the user's input and generate follow-up questions for clarification. For example, when the generation AI analyzes the user's input, the interactive survey implementation unit detects ambiguity in the input content and generates follow-up questions for clarification. For example, if the user inputs "good," the interactive survey implementation unit asks what specifically is good about it. The interactive survey implementation unit also builds a system that detects ambiguity in the input content when the generation AI analyzes the user's input and generates follow-up questions for clarification. For example, it detects ambiguous expressions and asks detailed questions. The interactive survey implementation unit can also detect ambiguity in the input content when the generation AI analyzes the user's input and generate follow-up questions for clarification. For example, if the user inputs "easy to use," the interactive survey implementation unit asks about specific elements of usability. This makes it possible to detect ambiguity in the user's input and generate follow-up questions for clarification.

[0059] The interactive survey implementation unit can use the emotion estimation function to analyze the emotional response to the user's input and generate questions based on the emotion. In the interactive survey implementation unit, for example, the generation AI uses the emotion estimation function to analyze the emotional response to the user's input and generate questions based on the emotion. For example, if the user shows positive emotion, more detailed questions are asked. In addition, the interactive survey implementation unit builds a system in which the generation AI uses the emotion estimation function to analyze the emotional response to the user's input and generate questions based on the emotion. For example, if the user shows negative emotion, questions that provide a sense of security are asked. In addition, in the interactive survey implementation unit, the generation AI uses the emotion estimation function to analyze the emotional response to the user's input and generate questions based on the emotion. For example, if the user is excited, questions that pique the user's interest are asked. In this way, the emotional response to the user's input can be analyzed and questions based on the emotion can be generated.

[0060] The interactive survey implementation unit can also analyze the user's voice input and image input and generate multimodal questions. In the interactive survey implementation unit, for example, the generation AI analyzes the user's voice input and generates appropriate questions from the voice data. For example, if the user voice-inputs, "What do you think of this product?", the system will ask detailed questions. In addition, the interactive survey implementation unit can analyze the user's image input and generate appropriate questions from the image data. For example, if the user uploads a photo of a product, the system will ask questions based on that photo. In addition, the interactive survey implementation unit can build a system in which the generation AI simultaneously analyzes the user's voice input and image input and generates multimodal questions. For example, questions are asked based on both voice and images. This makes it possible to analyze the user's voice input and image input and generate multimodal questions.

[0061] The interactive survey implementation unit can generate questions by referring to expertise in different industries and fields. For example, when the generation AI analyzes a user's input, the interactive survey implementation unit generates questions by referring to expertise in different industries and fields. For example, a health-related question is asked based on expertise in the medical field. The interactive survey implementation unit also builds a system in which, when the generation AI analyzes a user's input, the interactive survey implementation unit generates questions by referring to expertise in different industries and fields. For example, a technical question is asked based on expertise in the technology field. The interactive survey implementation unit also generates questions by referring to expertise in different industries and fields when the generation AI analyzes a user's input. For example, a market-related question is asked based on expertise in the marketing field. This makes it possible to generate questions by referring to expertise in different industries and fields.

[0062] The interactive survey implementation unit can use the emotion estimation function to monitor emotional reactions to a user's input in real time and generate questions according to the emotion. In the interactive survey implementation unit, for example, the generation AI uses the emotion estimation function to monitor emotional reactions to a user's input in real time and generate questions according to the emotion. For example, if the user shows positive emotion, more detailed questions are asked. In addition, the interactive survey implementation unit builds a system in which the generation AI uses the emotion estimation function to monitor emotional reactions to a user's input in real time and generate questions according to the emotion. For example, if the user shows negative emotion, questions that provide a sense of security are asked. In addition, the interactive survey implementation unit can use the emotion estimation function to monitor emotional reactions to a user's input in real time and generate questions according to the emotion. For example, if the user is excited, questions that pique the user's interest are asked. In this way, it is possible to monitor emotional reactions to a user's input in real time and generate questions according to the emotion.

[0063] The answer analysis unit refers to the respondent's past answer history and behavioral history, and is able to grasp the true intention with high accuracy. For example, when the generation AI analyzes the content of an answer, the answer analysis unit retrieves the respondent's past answer history from a database and grasps the true intention with high accuracy. For example, it analyzes in detail the answers of a respondent who has placed importance on "ease of use" in the past. In addition, when the generation AI analyzes the content of an answer, the answer analysis unit refers to the respondent's behavioral history and grasps the true intention with high accuracy. For example, it analyzes the content of an answer based on past purchase history and usage history. In addition, when the generation AI analyzes the content of an answer, the answer analysis unit refers to the respondent's past answer history and behavioral history, and builds a system that grasps the true intention with high accuracy. For example, it checks the consistency between past answers and current answers. This makes it possible to grasp the true intention with high accuracy by referring to the respondent's past answer history and behavioral history.

[0064] The answer analysis unit can extract potential needs and problems behind the answer content. For example, when the generation AI analyzes the answer content, the answer analysis unit extracts potential needs behind the answer content. For example, if the answer is "easy to use," the specific elements of that answer are analyzed. In addition, when the generation AI analyzes the answer content, the answer analysis unit extracts problems behind the answer content. For example, if the answer is "expensive," the specific points of dissatisfaction with the price are analyzed. In addition, the answer analysis unit builds a system that extracts potential needs and problems behind the answer content when the generation AI analyzes the answer content. For example, the answer content is analyzed in detail to discover hidden needs. This makes it possible to extract potential needs and problems behind the answer content.

[0065] The answer analysis unit uses the emotion estimation function to analyze the emotional reaction to the answer content and can grasp the true meaning based on the emotion. For example, the answer analysis unit uses the emotion estimation function to analyze the emotional reaction to the answer content and grasp the true meaning based on the emotion. For example, it analyzes answer content that shows positive emotion in detail. Furthermore, the answer analysis unit builds a system in which the generation AI uses the emotion estimation function to analyze the emotional reaction to the answer content and grasp the true meaning based on the emotion. For example, it analyzes answer content that shows negative emotion in detail. Furthermore, the answer analysis unit uses the emotion estimation function to analyze the emotional reaction to the answer content and grasp the true meaning based on the emotion. For example, if the respondent is excited, it analyzes the cause of the excitement. In this way, it is possible to analyze the emotional reaction to the answer content and grasp the true meaning based on the emotion.

[0066] The answer analysis unit compares answers from different cultures and regions, and can grasp the true meaning from a global perspective. For example, when the generation AI analyzes the content of an answer, the answer analysis unit compares answers from different cultures and regions, and can grasp the true meaning from a global perspective. For example, it analyzes answers from different cultures to the same question. Furthermore, when the generation AI analyzes the content of an answer, the answer analysis unit compares answers from different regions, and builds a system that grasps the true meaning from a global perspective. For example, it analyzes answer trends by region. Furthermore, when the generation AI analyzes the content of an answer, the answer analysis unit compares answers from different cultures and regions, and can grasp the true meaning from a global perspective. For example, it asks questions that take cultural background into consideration. This makes it possible to compare answers from different cultures and regions, and can grasp the true meaning from a global perspective.

[0067] The answer analysis unit visualizes the answer content, making it possible to grasp the true meaning in a format that is visually easy to understand. For example, the answer analysis unit uses a generation AI to visualize the answer content and grasp the true meaning in a format that is visually easy to understand. For example, the answer content is displayed in a graph or chart. The answer analysis unit also builds a system in which the generation AI visualizes the answer content and grasps the true meaning in a format that is visually easy to understand. For example, the answer content is displayed in a mind map. The answer analysis unit also visualizes the answer content and grasps the true meaning in a format that is visually easy to understand. For example, the answer content is displayed in an infographic. This makes it possible to visualize the answer content and grasp the true meaning in a format that is visually easy to understand.

[0068] The answer analysis unit uses the emotion estimation function to monitor emotional reactions to the answer content in real time and can grasp the true meaning based on the emotions. For example, the answer analysis unit uses the emotion estimation function to monitor emotional reactions to the answer content in real time and grasp the true meaning based on the emotions. For example, it analyzes answer content that shows positive emotions in detail. Furthermore, the answer analysis unit builds a system in which the generation AI uses the emotion estimation function to monitor emotional reactions to the answer content in real time and grasp the true meaning based on the emotions. For example, it analyzes answer content that shows negative emotions in detail. Furthermore, the answer analysis unit uses the emotion estimation function to monitor emotional reactions to the answer content in real time and grasp the true meaning based on the emotions. For example, if the respondent is excited, it analyzes the cause of the excitement. In this way, it is possible to monitor emotional reactions to the answer content in real time and grasp the true meaning based on the emotions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The questionnaire system may further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit may, for example, analyze what websites the user has visited in the past and what products the user has purchased, and generate questions based on this. For example, if the user has purchased many electronic devices in the past, detailed questions about the electronic devices may be asked. The behavior analysis unit may also analyze the user's social media activity and generate questions based on this. For example, if the user frequently mentions a particular brand, questions about that brand may be asked. Furthermore, the behavior analysis unit may analyze the user's location information and generate questions specific to a region. For example, if the user frequently visits a particular region, questions about that region may be asked. This makes it possible to generate questions based on the user's behavioral history.

[0071] The questionnaire system may further include a health analysis unit that monitors the user's health condition. The health analysis unit may, for example, analyze data acquired from the user's wearable device and generate questions based on the data. For example, the health analysis unit may analyze the user's heart rate and sleep patterns and ask health-related questions. The health analysis unit may also analyze the user's food records and generate questions based on the data. For example, if the user frequently eats a particular food ingredient, the health analysis unit may ask questions about that food ingredient. The health analysis unit may also analyze the user's exercise history and generate questions based on the data. For example, if the user frequently performs a particular exercise, the health analysis unit may ask questions about that exercise. This allows questions to be generated based on the user's health condition.

[0072] The questionnaire system may further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit, for example, analyzes data on products purchased by the user in the past and generates questions based on this. For example, if the user has purchased many cosmetics in the past, detailed questions about the cosmetics may be asked. The purchase analysis unit may also analyze the user's purchasing patterns and generate questions based on this. For example, if the user frequently purchases products from a particular brand, questions about that brand may be asked. Furthermore, the purchase analysis unit may analyze the user's purchasing history and suggest new products based on this. For example, new products related to products purchased by the user in the past may be suggested. This makes it possible to generate questions based on the user's purchasing history.

[0073] The questionnaire system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit may, for example, analyze events and activities the user has participated in in the past and generate questions based on this. For example, if the user has participated in many music events in the past, detailed questions about music may be asked. The hobby analysis unit may also analyze the user's social media activity and generate questions based on this. For example, if the user frequently mentions a particular artist, questions about that artist may be asked. The hobby analysis unit may also analyze the user's reading history and generate questions based on this. For example, if the user frequently reads books of a particular genre, questions about that genre may be asked. This makes it possible to generate questions based on the user's hobbies and interests.

[0074] The questionnaire system may further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit may, for example, analyze online courses or learning programs that the user has taken in the past and generate questions based on this. For example, if the user has taken many programming courses in the past, detailed questions about programming may be asked. The learning analysis unit may also analyze the user's learning outcomes and generate questions based on this. For example, if the user has acquired a specific skill, questions related to that skill may be asked. Furthermore, the learning analysis unit may analyze the user's learning history and suggest new learning opportunities based on this. For example, it may suggest new courses related to courses the user has taken in the past. This makes it possible to generate questions based on the user's learning history.

[0075] The questionnaire system can further include a feedback analysis unit that estimates the user's emotions and provides feedback based on the estimated emotions. For example, if the user expresses a positive emotion, the feedback analysis unit provides feedback that reinforces the emotion. For example, if the user is satisfied, the feedback analysis unit provides feedback to further increase the satisfaction. Furthermore, if the user expresses a negative emotion, the feedback analysis unit can provide feedback to alleviate the emotion. For example, if the user is dissatisfied, the feedback analysis unit provides feedback to relieve the dissatisfaction. Furthermore, the feedback analysis unit can monitor the user's emotions in real time and provide feedback based on the monitoring. For example, if the user is excited, the feedback analysis unit provides feedback to maintain the excitement. In this way, feedback based on the user's emotions can be provided.

[0076] The questionnaire system can further include a question generation unit that estimates the user's emotions and generates questions based on the estimated emotions. For example, if the user is relaxed, the question generation unit generates questions to help the user maintain that state. For example, if the user is relaxed, the question generation unit asks questions that are easy to answer in a relaxed state. Furthermore, if the user is excited, the question generation unit can also generate questions to bring out that excitement. For example, if the user is excited, the question generation unit asks questions to further increase that excitement. Furthermore, the question generation unit can monitor the user's emotions in real time and generate questions based on this. For example, if the user is feeling anxious, the question generation unit asks questions to ease the anxiety. In this way, questions based on the user's emotions can be generated.

[0077] The questionnaire system may further include a progress adjustment unit that estimates the user's emotions and adjusts the progress of the questionnaire based on the estimated emotions. For example, the progress adjustment unit may reduce the number of questions in the questionnaire when the user is tired. For example, when the user is tired, only important questions are asked. The progress adjustment unit may also increase the number of questions in the questionnaire when the user is excited. For example, when the user is excited, detailed questions are asked. Furthermore, the progress adjustment unit may monitor the user's emotions in real time and adjust the progress of the questionnaire based on this. For example, when the user is relaxed, the pace of questions is adjusted to maintain that state. In this way, the progress of the questionnaire can be adjusted based on the user's emotions.

[0078] The questionnaire system may further include an end adjustment unit that estimates the user's emotions and adjusts the timing of ending the questionnaire based on the estimated emotions. The end adjustment unit may, for example, end the questionnaire early if the user is tired. For example, if the user is tired, only important questions may be asked and the questionnaire may end early. The end adjustment unit may also extend the questionnaire if the user is excited. For example, if the user is excited, detailed questions may be asked and the questionnaire may be extended. Furthermore, the end adjustment unit may monitor the user's emotions in real time and adjust the timing of ending the questionnaire based on the user's emotions. For example, if the user is relaxed, the survey may end at an appropriate time to maintain that state. In this way, the timing of ending the questionnaire can be adjusted based on the user's emotions.

[0079] The questionnaire system may further include a content customization unit that estimates the user's emotions and customizes the content of the questionnaire based on the estimated emotions. For example, when the user expresses a positive emotion, the content customization unit generates questions to reinforce the emotion. For example, when the user is satisfied, the content customization unit asks questions to further increase the satisfaction. Furthermore, when the user expresses a negative emotion, the content customization unit can generate questions to alleviate the emotion. For example, when the user is dissatisfied, the content customization unit asks questions to relieve the dissatisfaction. Furthermore, the content customization unit can monitor the user's emotions in real time and customize the content of the questionnaire based on the monitored emotions. For example, when the user is excited, the content customization unit asks questions to maintain the excitement. In this way, the content of the questionnaire can be customized based on the user's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The interactive survey implementation unit uses the generation AI to conduct an interactive survey. For example, the generation AI analyzes input from the user and generates appropriate questions. Specifically, it can ask questions such as, "Please tell us why you chose this product." Step 2: The response analysis unit analyzes the responses obtained by the interactive questionnaire implementation unit. For example, if a respondent answers "because this product is easy to use," the unit can ask more detailed questions about the specific reasons why they feel the product is "easy to use." Step 3: The feedback unit feeds back the results of the analysis by the response analysis unit. For example, the generation AI can save the responses as text data and analyze them later. This allows the survey system to accurately extract the respondent's true intentions and provide feedback.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 AI 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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 AI 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 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. Equipped with generative AI, The generated AI is an interactive questionnaire implementation unit that implements an interactive questionnaire; a response analysis unit that analyzes the content of the responses obtained by the interactive questionnaire implementation unit; a feedback unit that feeds back the results of the analysis by the answer analysis unit. A system characterized by:

2. The interactive questionnaire implementation unit Analyzes respondent's facial expressions and tone of voice to detect changes in emotion in real time and adjust questions accordingly 2. The system of claim 1.

3. The interactive questionnaire implementation unit Generate consistent questions by looking at the respondent's past response history 2. The system of claim 1.

4. The interactive questionnaire implementation unit Generate questions based on the respondent's emotional state and engage in dialogue that elicits positive emotions 2. The system of claim 1.

5. The interactive questionnaire implementation unit Generate questions in multiple languages ​​simultaneously and conduct international surveys 2. The system of claim 1.

Images