System

Abstract

An object of a system according to an embodiment is to efficiently conduct an interview with a persona and extract the sound of the interview.SOLUTION: A system includes an interview execution part, an answer analysis part, and a follow-up generation part. The interview execution unit performs an interview with the persona based on a preset question list. The answer analysis unit analyzes the answer of the persona acquired by the interview execution unit. The follow-up generation unit generates an appropriate follow-up question based on 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] With conventional technology, it was difficult to efficiently interview personas and extract their true feelings.

[0005] The system according to the embodiment aims to efficiently conduct interviews with personas and draw out their true feelings. [Means for solving the problem]

[0006] The system according to the embodiment includes an interview execution unit, a response analysis unit, and a follow-up generation unit. The interview execution unit conducts an interview with a persona based on a preset question list. The response analysis unit analyzes the responses of the persona acquired by the interview execution unit. The follow-up generation unit generates appropriate follow-up questions based on the results of the analysis by the response analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently conduct interviews with personas and draw out their true feelings. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 interview support system according to an embodiment of the present invention automates interviews with personas and uses a generation AI to elicit the personas' true thoughts and opinions. This allows the interview support system to efficiently elicit the personas' true thoughts and opinions.

[0029] An interview support system according to an embodiment includes an interview execution unit, a response analysis unit, and a follow-up generation unit. The interview execution unit conducts an interview with a persona based on a preset question list. For example, the interview execution unit uses a generation AI to understand the persona's responses using natural language processing technology and generate appropriate follow-up questions. The response analysis unit analyzes the persona's responses acquired by the interview execution unit. For example, the response analysis unit includes technology that enables the generation AI to analyze the persona's responses and elicit the true feelings and opinions behind them. The follow-up generation unit generates appropriate follow-up questions based on the results of the analysis by the response analysis unit. For example, the follow-up generation unit enables the generation AI to analyze the persona's emotions and tone and ask follow-up questions at appropriate times. This enables the interview support system according to an embodiment to efficiently elicit the persona's true feelings and opinions.

[0030] When analyzing the persona's answers, the answer analysis unit can introduce an algorithm that identifies the potential needs or issues behind the answers. For example, the answer analysis unit introduces an algorithm in which the generation AI analyzes the persona's answers and identifies the potential needs and issues. For example, if a persona answers, "I don't use this function very often," the unit asks a question such as, "Why do you feel that you don't use this function?" to draw out specific reasons. By identifying the persona's potential needs and issues, it is possible to draw out more specific true feelings.

[0031] The interview execution unit can ask consistent questions based on the persona's past response history. For example, the generation AI retrieves the persona's past response history from a database and references it during the interview. For example, if a persona previously responded that "this function is useful," the interview execution unit can ask consistent questions such as "How do you use this function?" By referencing the persona's past response history, consistent questions can be asked and deeper truths can be drawn out.

[0032] The interview execution unit can provide visual support by displaying relevant visuals or data in real time based on the persona's answers. In the interview execution unit, for example, the generation AI analyzes the persona's answers and displays relevant visuals in real time. For example, if the persona answers "I think it's good" to the question "What do you think of the new design?", the generation AI will display an image of that design. This makes it possible to provide visual support and improve the quality of the interview by displaying relevant visuals or data based on the persona's answers.

[0033] The interview execution unit can use an automatic translation function to conduct interviews with personas of different cultures or languages. For example, the generation AI uses the automatic translation function to conduct interviews with personas of different languages. For example, questions set in Japanese are translated into English, and an interview is conducted with an English-speaking persona. This makes it possible to collect diverse opinions by interviewing personas of different cultures and languages.

[0034] The interview execution unit can introduce an algorithm that conducts interviews based on a preset question list, dynamically updates the question list, and generates new questions according to the persona's answers. The interview execution unit, for example, introduces an algorithm that allows the generation AI to dynamically update the question list according to the persona's answers. For example, if the answer to the question, "What do you think about this feature?" is "I think it's useful," the generation AI generates a new question such as "What specifically did you find useful?" This makes it possible to dynamically update the question list according to the persona's answers, thereby achieving more effective interviews.

[0035] The interview execution unit can analyze the persona's response to the question list and identify the most effective question pattern. For example, the interview execution unit uses a generation AI to analyze the persona's response to the question list and identify the most effective question pattern. For example, the generation AI can identify questions that the persona found easy to answer and use similar questions in the next interview. In this way, by analyzing the persona's response, the most effective question pattern can be identified and reflected in the next interview.

[0036] The interview execution unit can provide related additional information and data based on the persona's answers to the list of questions. In the interview execution unit, for example, the generation AI provides related additional information based on the persona's answers. For example, if the answer to the question "What do you think about this feature?" is "I think it's useful," the generation AI provides a detailed explanation of the feature. This makes it possible to improve the quality of the interview by providing related additional information and data based on the persona's answers.

[0037] The interview execution unit can generate a list of questions customized for the personas in different industries or fields. For example, the generation AI in the interview execution unit generates a list of questions customized for personas in different industries. For example, technical questions are asked to personas in the IT industry, and medical-related questions are asked to personas in the medical industry. This allows for more appropriate interviews to be conducted by generating a list of questions customized for personas in different industries or fields.

[0038] The response analysis unit can integrate different data sources when analyzing the persona's responses. For example, when the generation AI analyzes the persona's responses, the response analysis unit integrates social media data. For example, if a persona answers, "I don't use this feature very often," the response analysis unit refers to social media comments and analyzes feedback from users who share similar opinions. Integrating different data sources enables more multifaceted analysis and a deeper understanding of the persona's true feelings and opinions.

[0039] The response analysis unit can compare the responses of the personas from different industries or fields to identify common true feelings and opinions. For example, the generation AI compares the responses of personas from different industries to identify common true feelings and opinions. For example, it compares the responses of personas from the IT industry and the medical industry to identify common needs and challenges. This makes it possible to identify common true feelings and opinions by comparing the responses of personas from different industries and fields, enabling analysis from a broader perspective.

[0040] The interview execution unit can continuously learn based on the results of the interview and reflect this in the next interview. For example, the interview execution unit uses a generation AI to learn effective questions based on the results of the interview. For example, the AI ​​can learn questions that the persona found easy to answer and use similar questions in the next interview. This allows the system to continuously learn based on the results of the interview and improve the quality of the next interview.

[0041] The interview execution unit can identify effective question patterns based on the results of the interview. For example, the interview execution unit uses a generation AI to identify effective question patterns based on the results of the interview. For example, the unit identifies question patterns that the persona felt were easy to answer, and uses a similar pattern in the next interview. In this way, by identifying effective question patterns based on the results of the interview, the quality of the next interview can be improved.

[0042] The interview execution unit can identify an effective follow-up method based on the results of the interview. For example, the interview execution unit uses the generation AI to identify an effective follow-up method based on the results of the interview. For example, the generation AI identifies a follow-up method that the persona felt was "easy to answer this follow-up question," and uses a similar method in the next interview. In this way, by identifying an effective follow-up method based on the results of the interview, the quality of the next interview can be improved.

[0043] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. For example, the interview execution unit uses a generation AI to learn effective questions based on the results of the interview. For example, the unit learns questions that the persona felt were easy to answer and uses similar questions in the next interview. This allows the quality of the next interview to be improved by learning effective questions and follow-up methods based on the results of the interview.

[0044] The interview execution unit can identify effective question patterns based on the results of the interview. For example, the interview execution unit uses a generation AI to identify effective question patterns based on the results of the interview. For example, the unit identifies question patterns that the persona felt were easy to answer, and uses a similar pattern in the next interview. In this way, by identifying effective question patterns based on the results of the interview, the quality of the next interview can be improved.

[0045] The interview execution unit can identify an effective follow-up method based on the results of the interview. For example, the interview execution unit uses the generation AI to identify an effective follow-up method based on the results of the interview. For example, the generation AI identifies a follow-up method that the persona felt was "easy to answer this follow-up question," and uses a similar method in the next interview. In this way, by identifying an effective follow-up method based on the results of the interview, the quality of the next interview can be improved.

[0046] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. For example, the interview execution unit uses a generation AI to learn effective questions based on the results of the interview. For example, the unit learns questions that the persona felt were easy to answer and uses similar questions in the next interview. This allows the quality of the next interview to be improved by learning effective questions and follow-up methods based on the results of the interview.

[0047] The interview execution unit can identify effective question patterns based on the results of the interview. For example, the interview execution unit uses a generation AI to identify effective question patterns based on the results of the interview. For example, the unit identifies question patterns that the persona felt were easy to answer, and uses a similar pattern in the next interview. In this way, by identifying effective question patterns based on the results of the interview, the quality of the next interview can be improved.

[0048] The interview execution unit can identify an effective follow-up method based on the results of the interview. For example, the interview execution unit uses the generation AI to identify an effective follow-up method based on the results of the interview. For example, the generation AI identifies a follow-up method that the persona felt was "easy to answer this follow-up question," and uses a similar method in the next interview. In this way, by identifying an effective follow-up method based on the results of the interview, the quality of the next interview can be improved.

[0049] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. In the interview execution unit, for example, the generation AI learns effective questions based on the results of the interview. For example, it learns questions that the persona felt were easy to answer, and uses similar questions in the next interview. The generation AI also learns effective follow-up methods based on the results of the interview. For example, it learns follow-up methods that the persona felt were easy to answer, and uses similar methods in the next interview. In this way, by learning effective questions and follow-up methods based on the results of the interview, it is possible to improve the quality of the next interview.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The interview execution unit can provide visual support by displaying relevant visuals or data in real time based on the persona's answers. For example, the generation AI analyzes the persona's answers and displays relevant visuals in real time. For example, if the persona answers "I think it's good" to the question "What do you think of the new design?", the generation AI will display an image of that design. This allows visual support to be provided by displaying relevant visuals or data based on the persona's answers, improving the quality of the interview.

[0052] The interview execution unit can use an automatic translation function to conduct interviews with personas of different cultures or languages. For example, the generation AI uses the automatic translation function to conduct interviews with personas of different languages. For example, questions set in Japanese can be translated into English and interviewed with personas who speak English. This makes it possible to collect diverse opinions by interviewing personas of different cultures and languages.

[0053] The interview execution unit can ask consistent questions based on the persona's past response history. For example, the generation AI can retrieve the persona's past response history from a database and refer to it during the interview. For example, if a persona previously responded that "this function is useful," it can ask a consistent question such as "How do you use that function?" By referencing the persona's past response history, it is possible to ask consistent questions and draw out deeper, true thoughts.

[0054] The interview execution unit can analyze the persona's response to the list of questions and identify the most effective question pattern. For example, the generation AI can analyze the persona's response to the list of questions and identify the most effective question pattern. For example, it can identify questions that the persona felt were easy to answer and use similar questions in the next interview. In this way, by analyzing the persona's response, it is possible to identify the most effective question pattern and reflect it in the next interview.

[0055] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. For example, the generation AI learns effective questions based on the results of the interview. For example, it learns questions that the persona felt were easy to answer, and uses similar questions in the next interview. The generation AI also learns effective follow-up methods based on the results of the interview. For example, it learns follow-up methods that the persona felt were easy to answer, and uses similar methods in the next interview. In this way, by learning effective questions and follow-up methods based on the results of the interview, it is possible to improve the quality of the next interview.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The interview execution unit conducts an interview with the persona based on a pre-defined list of questions. For example, the interview execution unit uses a generation AI to understand the persona's answers using natural language processing technology and generate appropriate follow-up questions. Step 2: The response analysis unit analyzes the persona's responses obtained by the interview execution unit. For example, the response analysis unit is equipped with technology that enables the generation AI to analyze the persona's responses and extract the true feelings and opinions behind them. Step 3: The follow-up generation unit generates appropriate follow-up questions based on the results analyzed by the answer analysis unit. For example, the follow-up generation unit uses a generation AI to analyze the persona's emotions and tone and ask follow-up questions at the appropriate time.

[0058] (Example 2) The interview support system according to an embodiment of the present invention automates interviews with personas and uses a generation AI to elicit the personas' true thoughts and opinions. This allows the interview support system to efficiently elicit the personas' true thoughts and opinions.

[0059] An interview support system according to an embodiment includes an interview execution unit, a response analysis unit, and a follow-up generation unit. The interview execution unit conducts an interview with a persona based on a preset question list. For example, the interview execution unit uses a generation AI to understand the persona's responses using natural language processing technology and generate appropriate follow-up questions. The response analysis unit analyzes the persona's responses acquired by the interview execution unit. For example, the response analysis unit includes technology that enables the generation AI to analyze the persona's responses and elicit the true feelings and opinions behind them. The follow-up generation unit generates appropriate follow-up questions based on the results of the analysis by the response analysis unit. For example, the follow-up generation unit enables the generation AI to analyze the persona's emotions and tone and ask follow-up questions at appropriate times. This enables the interview support system according to an embodiment to efficiently elicit the persona's true feelings and opinions.

[0060] The follow-up generation unit can generate follow-up questions based on the emotional nuances of the persona's answers. For example, the generation AI analyzes the emotional nuances of the persona's answers and generates appropriate follow-up questions. For example, if a persona answers, "I don't use this function very often," the generation AI asks a question such as, "Why do you feel that you don't use that function?" to elicit specific reasons. In this way, by generating follow-up questions that correspond to the persona's emotions, it is possible to elicit deeper, true thoughts.

[0061] When analyzing the persona's answers, the answer analysis unit can introduce an algorithm that identifies the potential needs or issues behind the answers. For example, the answer analysis unit introduces an algorithm in which the generation AI analyzes the persona's answers and identifies the potential needs and issues. For example, if a persona answers, "I don't use this function very often," the unit asks a question such as, "Why do you feel that you don't use this function?" to draw out specific reasons. By identifying the persona's potential needs and issues, it is possible to draw out more specific true feelings.

[0062] The interview execution unit can analyze the persona's facial expressions or tone of voice in real time and dynamically generate questions according to their emotions. For example, the generation AI captures the persona's facial expressions with a camera during the interview and performs emotional analysis in real time. For example, if the persona smiles, the generation AI generates a positive follow-up question such as "Tell me more about that." This allows for more effective interviews by dynamically generating questions according to the persona's emotions.

[0063] The interview execution unit can ask consistent questions based on the persona's past response history. For example, the generation AI retrieves the persona's past response history from a database and references it during the interview. For example, if a persona previously responded that "this function is useful," the interview execution unit can ask consistent questions such as "How do you use this function?" By referencing the persona's past response history, consistent questions can be asked and deeper truths can be drawn out.

[0064] The interview execution unit can provide visual support by displaying relevant visuals or data in real time based on the persona's answers. In the interview execution unit, for example, the generation AI analyzes the persona's answers and displays relevant visuals in real time. For example, if the persona answers "I think it's good" to the question "What do you think of the new design?", the generation AI will display an image of that design. This makes it possible to provide visual support and improve the quality of the interview by displaying relevant visuals or data based on the persona's answers.

[0065] The interview execution unit can use an automatic translation function to conduct interviews with personas of different cultures or languages. For example, the generation AI uses the automatic translation function to conduct interviews with personas of different languages. For example, questions set in Japanese are translated into English, and an interview is conducted with an English-speaking persona. This makes it possible to collect diverse opinions by interviewing personas of different cultures and languages.

[0066] The interview execution unit can use the emotion estimation function to adjust the progress of the interview in real time according to the emotions of the persona, thereby eliciting positive emotions. For example, the generation AI in the interview execution unit uses the emotion estimation function to adjust the progress of the interview in real time according to the emotions of the persona. For example, if the persona is nervous, questions are asked to help them relax. In this way, by adjusting the progress of the interview according to the persona's emotions, positive emotions can be elicited and better interview results can be obtained.

[0067] The interview execution unit can introduce an algorithm that conducts interviews based on a preset question list, dynamically updates the question list, and generates new questions according to the persona's answers. The interview execution unit, for example, introduces an algorithm that allows the generation AI to dynamically update the question list according to the persona's answers. For example, if the answer to the question, "What do you think about this feature?" is "I think it's useful," the generation AI generates a new question such as "What specifically did you find useful?" This makes it possible to dynamically update the question list according to the persona's answers, thereby achieving more effective interviews.

[0068] The interview execution unit can analyze the persona's response to the question list and identify the most effective question pattern. For example, the interview execution unit uses a generation AI to analyze the persona's response to the question list and identify the most effective question pattern. For example, the generation AI can identify questions that the persona found easy to answer and use similar questions in the next interview. In this way, by analyzing the persona's response, the most effective question pattern can be identified and reflected in the next interview.

[0069] The interview execution unit uses an emotion estimation function to dynamically adjust the question list according to the persona's emotions, thereby drawing out deeper, true thoughts. For example, the generation AI in the interview execution unit uses the emotion estimation function to dynamically adjust the question list according to the persona's emotions. For example, if the persona is nervous, questions to help them relax are added. In this way, by dynamically adjusting the question list according to the persona's emotions, deeper, true thoughts can be drawn out.

[0070] The interview execution unit can provide related additional information and data based on the persona's answers to the list of questions. In the interview execution unit, for example, the generation AI provides related additional information based on the persona's answers. For example, if the answer to the question "What do you think about this feature?" is "I think it's useful," the generation AI provides a detailed explanation of the feature. This makes it possible to improve the quality of the interview by providing related additional information and data based on the persona's answers.

[0071] The interview execution unit can generate a list of questions customized for the personas in different industries or fields. For example, the generation AI in the interview execution unit generates a list of questions customized for personas in different industries. For example, technical questions are asked to personas in the IT industry, and medical-related questions are asked to personas in the medical industry. This allows for more appropriate interviews to be conducted by generating a list of questions customized for personas in different industries or fields.

[0072] The interview execution unit can use an emotion estimation function to adjust the question list in real time according to the persona's emotions and draw out positive emotions. For example, the generation AI in the interview execution unit uses the emotion estimation function to adjust the question list in real time according to the persona's emotions. For example, if the persona is nervous, questions to help them relax are added. In this way, by adjusting the question list in real time according to the persona's emotions, positive emotions can be drawn out and better interview results can be obtained.

[0073] The response analysis unit analyzes the persona's response based on the emotional aspects and can extract true feelings and opinions based on emotions. For example, the response analysis unit uses a generation AI to analyze the emotional aspects of the persona's response and extract true feelings and opinions based on emotions. For example, for a persona who answers, "This feature is great," it asks a follow-up question such as, "What specifically did you find great about it?" In this way, by analyzing the persona's emotional aspects, it is possible to extract deeper true feelings and opinions.

[0074] The answer analysis unit uses the emotion estimation function to perform an analysis according to the persona's emotions and draw out deeper true feelings. For example, the generation AI uses the emotion estimation function to perform an analysis according to the persona's emotions and draw out deeper true feelings. For example, if the persona is nervous, it asks questions to relax them. In this way, by performing an analysis according to the persona's emotions, deeper true feelings can be drawn out.

[0075] The response analysis unit can integrate different data sources when analyzing the persona's responses. For example, when the generation AI analyzes the persona's responses, the response analysis unit integrates social media data. For example, if a persona answers, "I don't use this feature very often," the response analysis unit refers to social media comments and analyzes feedback from users who share similar opinions. Integrating different data sources enables more multifaceted analysis and a deeper understanding of the persona's true feelings and opinions.

[0076] The response analysis unit can compare the responses of the personas from different industries or fields to identify common true feelings and opinions. For example, the generation AI compares the responses of personas from different industries to identify common true feelings and opinions. For example, it compares the responses of personas from the IT industry and the medical industry to identify common needs and challenges. This makes it possible to identify common true feelings and opinions by comparing the responses of personas from different industries and fields, enabling analysis from a broader perspective.

[0077] The answer analysis unit uses an emotion estimation function to perform analysis in real time according to the persona's emotions, and can elicit positive emotions. For example, the generation AI uses the emotion estimation function to perform analysis in real time according to the persona's emotions. For example, if the persona is nervous, it asks questions to relax them. In this way, by performing analysis in real time according to the persona's emotions, positive emotions can be elicited and better interview results can be obtained.

[0078] The interview execution unit can continuously learn based on the results of the interview and reflect this in the next interview. For example, the interview execution unit uses a generation AI to learn effective questions based on the results of the interview. For example, the AI ​​can learn questions that the persona found easy to answer and use similar questions in the next interview. This allows the system to continuously learn based on the results of the interview and improve the quality of the next interview.

[0079] The interview execution unit can identify effective question patterns based on the results of the interview. For example, the interview execution unit uses a generation AI to identify effective question patterns based on the results of the interview. For example, the unit identifies question patterns that the persona felt were easy to answer, and uses a similar pattern in the next interview. In this way, by identifying effective question patterns based on the results of the interview, the quality of the next interview can be improved.

[0080] The interview execution unit can identify an effective follow-up method based on the results of the interview. For example, the interview execution unit uses the generation AI to identify an effective follow-up method based on the results of the interview. For example, the generation AI identifies a follow-up method that the persona felt was "easy to answer this follow-up question," and uses a similar method in the next interview. In this way, by identifying an effective follow-up method based on the results of the interview, the quality of the next interview can be improved.

[0081] The interview execution unit uses an emotion estimation function to perform analysis in real time according to the persona's emotions, and can elicit positive emotions. For example, the interview execution unit uses the emotion estimation function to perform analysis in real time according to the persona's emotions. For example, if the persona is nervous, it asks questions to relax them. In this way, by performing analysis in real time according to the persona's emotions, positive emotions can be elicited and better interview results can be obtained.

[0082] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. For example, the interview execution unit uses a generation AI to learn effective questions based on the results of the interview. For example, the unit learns questions that the persona felt were easy to answer and uses similar questions in the next interview. This allows the quality of the next interview to be improved by learning effective questions and follow-up methods based on the results of the interview.

[0083] The interview execution unit can identify effective question patterns based on the results of the interview. For example, the interview execution unit uses a generation AI to identify effective question patterns based on the results of the interview. For example, the unit identifies question patterns that the persona felt were easy to answer, and uses a similar pattern in the next interview. In this way, by identifying effective question patterns based on the results of the interview, the quality of the next interview can be improved.

[0084] The interview execution unit can identify an effective follow-up method based on the results of the interview. For example, the interview execution unit uses the generation AI to identify an effective follow-up method based on the results of the interview. For example, the generation AI identifies a follow-up method that the persona felt was "easy to answer this follow-up question," and uses a similar method in the next interview. In this way, by identifying an effective follow-up method based on the results of the interview, the quality of the next interview can be improved.

[0085] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. For example, the interview execution unit uses a generation AI to learn effective questions based on the results of the interview. For example, the unit learns questions that the persona felt were easy to answer and uses similar questions in the next interview. This allows the quality of the next interview to be improved by learning effective questions and follow-up methods based on the results of the interview.

[0086] The interview execution unit can identify effective question patterns based on the results of the interview. For example, the interview execution unit uses a generation AI to identify effective question patterns based on the results of the interview. For example, the unit identifies question patterns that the persona felt were easy to answer, and uses a similar pattern in the next interview. In this way, by identifying effective question patterns based on the results of the interview, the quality of the next interview can be improved.

[0087] The interview execution unit can identify an effective follow-up method based on the results of the interview. For example, the interview execution unit uses the generation AI to identify an effective follow-up method based on the results of the interview. For example, the generation AI identifies a follow-up method that the persona felt was "easy to answer this follow-up question," and uses a similar method in the next interview. In this way, by identifying an effective follow-up method based on the results of the interview, the quality of the next interview can be improved.

[0088] The interview execution unit uses an emotion estimation function to perform analysis in real time according to the persona's emotions, and can elicit positive emotions. For example, the interview execution unit uses the emotion estimation function to perform analysis in real time according to the persona's emotions. For example, if the persona is nervous, it asks questions to relax them. In this way, by performing analysis in real time according to the persona's emotions, positive emotions can be elicited and better interview results can be obtained.

[0089] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. In the interview execution unit, for example, the generation AI learns effective questions based on the results of the interview. For example, it learns questions that the persona felt were easy to answer, and uses similar questions in the next interview. The generation AI also learns effective follow-up methods based on the results of the interview. For example, it learns follow-up methods that the persona felt were easy to answer, and uses similar methods in the next interview. In this way, by learning effective questions and follow-up methods based on the results of the interview, it is possible to improve the quality of the next interview.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The interview execution unit can provide visual support by displaying relevant visuals or data in real time based on the persona's answers. For example, the generation AI analyzes the persona's answers and displays relevant visuals in real time. For example, if the persona answers "I think it's good" to the question "What do you think of the new design?", the generation AI will display an image of that design. This allows visual support to be provided by displaying relevant visuals or data based on the persona's answers, improving the quality of the interview.

[0092] The interview execution unit can use an automatic translation function to conduct interviews with personas of different cultures or languages. For example, the generation AI uses the automatic translation function to conduct interviews with personas of different languages. For example, questions set in Japanese can be translated into English and interviewed with personas who speak English. This makes it possible to collect diverse opinions by interviewing personas of different cultures and languages.

[0093] The interview execution unit can ask consistent questions based on the persona's past response history. For example, the generation AI can retrieve the persona's past response history from a database and refer to it during the interview. For example, if a persona previously responded that "this function is useful," it can ask a consistent question such as "How do you use that function?" By referencing the persona's past response history, it is possible to ask consistent questions and draw out deeper, true thoughts.

[0094] The interview execution unit can analyze the persona's response to the list of questions and identify the most effective question pattern. For example, the generation AI can analyze the persona's response to the list of questions and identify the most effective question pattern. For example, it can identify questions that the persona felt were easy to answer and use similar questions in the next interview. In this way, by analyzing the persona's response, it is possible to identify the most effective question pattern and reflect it in the next interview.

[0095] The interview execution unit can learn effective questions and follow-up methods based on the results of the interview. For example, the generation AI learns effective questions based on the results of the interview. For example, it learns questions that the persona felt were easy to answer, and uses similar questions in the next interview. The generation AI also learns effective follow-up methods based on the results of the interview. For example, it learns follow-up methods that the persona felt were easy to answer, and uses similar methods in the next interview. In this way, by learning effective questions and follow-up methods based on the results of the interview, it is possible to improve the quality of the next interview.

[0096] The interview execution unit uses the emotion estimation function to adjust the progress of the interview in real time according to the persona's emotions, thereby eliciting positive emotions. For example, the generation AI uses the emotion estimation function to adjust the progress of the interview in real time according to the persona's emotions. For example, if the persona is nervous, it asks questions to relax them. In this way, by adjusting the progress of the interview according to the persona's emotions, positive emotions can be elicited and better interview results can be obtained.

[0097] The interview execution unit uses an emotion estimation function to dynamically adjust the question list according to the persona's emotions, thereby drawing out deeper, true thoughts. For example, the generation AI uses the emotion estimation function to dynamically adjust the question list according to the persona's emotions. For example, if the persona is nervous, questions to help them relax are added. In this way, by dynamically adjusting the question list according to the persona's emotions, deeper, true thoughts can be drawn out.

[0098] The response analysis unit analyzes the persona's response based on the emotional aspects, and can extract true feelings and opinions based on emotions. For example, the generation AI analyzes the emotional aspects of the persona's response and extracts true feelings and opinions based on emotions. For example, for a persona who answers, "This feature is great," it asks a follow-up question such as, "What specifically did you find great about it?" In this way, by analyzing the persona's emotional aspects, it is possible to extract deeper true feelings and opinions.

[0099] The response analysis unit uses the emotion estimation function to perform an analysis according to the persona's emotions, thereby drawing out deeper true feelings. For example, the generation AI uses the emotion estimation function to perform an analysis according to the persona's emotions and draw out deeper true feelings. For example, if the persona is nervous, it asks questions to relax them. This allows for analysis according to the persona's emotions to draw out deeper true feelings.

[0100] The answer analysis unit uses an emotion estimation function to perform analysis in real time according to the persona's emotions, thereby eliciting positive emotions. For example, the generation AI uses the emotion estimation function to perform analysis in real time according to the persona's emotions. For example, if the persona is nervous, it asks questions to help them relax. In this way, by performing analysis in real time according to the persona's emotions, positive emotions can be elicited, resulting in better interview results.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The interview execution unit conducts an interview with the persona based on a pre-defined list of questions. For example, the interview execution unit uses a generation AI to understand the persona's answers using natural language processing technology and generate appropriate follow-up questions. Step 2: The response analysis unit analyzes the persona's responses obtained by the interview execution unit. For example, the response analysis unit is equipped with technology that enables the generation AI to analyze the persona's responses and extract the true feelings and opinions behind them. Step 3: The follow-up generation unit generates appropriate follow-up questions based on the results analyzed by the answer analysis unit. For example, the follow-up generation unit uses a generation AI to analyze the persona's emotions and tone and ask follow-up questions at the appropriate time.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. an interview execution unit that conducts interviews with personas based on a pre-set list of questions; a response analysis unit that analyzes the responses of the persona acquired by the interview execution unit; a follow-up question generation unit that generates an appropriate follow-up question based on the result of the analysis by the answer analysis unit. A system characterized by:

2. The follow-up generation unit Generate follow-up questions based on the emotional nuances of the persona's answers 2. The system of claim 1.

3. The interview execution unit: The persona's facial expressions or tone of voice are analyzed in real time, and questions are dynamically generated according to their emotions.

2. The system of claim 1.

4. The interview execution unit: Conduct interviews based on a pre-defined list of questions, dynamically update the list of questions, and implement algorithms that generate new questions based on the persona's answers.

2. The system of claim 1.

5. The answer analysis unit Analyze the persona's responses based on their emotional aspects to extract their true feelings and opinions.

2. The system of claim 1.

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