System

Abstract

An object of the system according to the embodiment is to provide a meaningful output and a correct suggestion in simple research.SOLUTION: A system according to an embodiment includes an investigation target information acquiring unit, a generated AI analyzing unit, and a suggestion providing unit. The investigation target information acquisition unit acquires investigation target information. The generated AI analyzing unit analyzes the investigation target information acquired by the investigation target information acquiring unit. The suggestion providing unit provides a suggestion on the basis of the result analyzed by the generated AI 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 is difficult to obtain meaningful output or correct suggestions from simple research, and there is room for improvement.

[0005] The system according to the embodiment aims to provide meaningful output and correct suggestions in simple research. [Means for solving the problem]

[0006] The system according to the embodiment includes a survey target information acquisition unit, a generation AI analysis unit, and a suggestion providing unit. The survey target information acquisition unit acquires survey target information. The generation AI analysis unit analyzes the survey target information acquired by the survey target information acquisition unit. The suggestion providing unit provides suggestions based on the results of the analysis by the generation AI analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide meaningful output and correct suggestions in simple research. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The research system according to an embodiment of the present invention automates simple research in the form of a popularity poll, and uses a generation AI to dig deeper into the survey results and provide suggestions for names, catchphrases, designs, etc. This allows the research system to replace simple quantitative surveys and improve the quality of the surveys.

[0029] A research system according to an embodiment includes a survey target information acquisition unit, a generation AI analysis unit, and a suggestion providing unit. The survey target information acquisition unit acquires survey target information. For example, the survey target information acquisition unit collects user behavior data. The survey target information acquisition unit can also acquire survey results. The survey target information acquisition unit can also collect social media posts. The generation AI analysis unit analyzes the survey target information acquired by the survey target information acquisition unit. For example, the generation AI analysis unit analyzes the survey target information using data mining technology. The generation AI analysis unit can also analyze the survey target information using statistical analysis technology. The generation AI analysis unit can also analyze the survey target information using a machine learning algorithm. The suggestion providing unit provides suggestions based on the results of the analysis by the generation AI analysis unit. For example, the suggestion providing unit provides suggestions in the form of a report. The suggestion providing unit can also provide suggestions in a dashboard display. The suggestion providing unit can also provide suggestions in the form of a notification. This allows the research system to automate simple research in the form of a popularity poll and provide marketing-meaningful output. For example, a research system can quickly provide suggestions for names, catchphrases, and designs for specific products or services. Research systems can also automate surveys, reducing man-hours and shortening the time to first responses. Furthermore, research systems can reduce the number of safe ideas surveyed and improve the quality of surveys.

[0030] The generation AI analysis unit can analyze a user's past voting history and generate personalized survey results that reflect the user's preferences. The generation AI analysis unit, for example, analyzes a user's past voting history and generates personalized survey results that reflect the preferences of each individual user. For example, new survey results are customized based on products and services that a specific user has previously given high ratings to. The generation AI analysis unit also provides personalized survey results based on a user's past voting history. For example, it generates survey results for new products and services related to categories that the user has previously shown interest in. The generation AI analysis unit also analyzes a user's past voting history and generates survey results that reflect the preferences of each individual user. For example, it customizes new survey results based on products and services that the user has previously given high ratings to. This makes it possible to provide personalized survey results based on the preferences of each individual user.

[0031] The generative AI analysis unit can analyze social media trends in real time and provide survey results that reflect the latest trends. The generative AI analysis unit, for example, analyzes social media trends in real time and provides survey results that reflect the latest trends. For example, it analyzes hashtags on Twitter and Instagram and generates survey results on popular topics and products. The generative AI analysis unit also analyzes social media trends in real time and provides survey results that reflect the latest trends. For example, it analyzes posts on Facebook and TikTok and generates survey results on products and services that are of high interest to users. The generative AI analysis unit also analyzes social media trends in real time and provides survey results that reflect the latest trends. For example, it analyzes hashtags on Twitter and Instagram and generates survey results on popular topics and products. This makes it possible to provide survey results that reflect the latest trends.

[0032] The generation AI analysis unit can use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. The generation AI analysis unit can, for example, use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. For example, it can analyze images of products uploaded by users and generate investigation results related to the products. The generation AI analysis unit can also use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. For example, it can analyze images of events uploaded by users and generate investigation results related to the events. The generation AI analysis unit can also use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. For example, it can analyze images of products uploaded by users and generate investigation results related to the products. This makes it possible to automatically extract related investigation subjects from images uploaded by users and conduct investigations.

[0033] The generative AI analysis unit can use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. The generative AI analysis unit can, for example, use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. For example, it can analyze the features of a product described by the user in the voice and provide survey results related to that product. The generative AI analysis unit can also use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. For example, it can analyze the details of an event described by the user in the voice and provide survey results related to that event. The generative AI analysis unit can also use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. For example, it can analyze the features of a product described by the user in the voice and provide survey results related to that product. In this way, the generative AI analysis unit can analyze the survey target from the user's voice input and generate survey results.

[0034] The generative AI analysis unit can analyze user attributes (age, gender, region, etc.) in detail to reveal trends for each attribute. The generative AI analysis unit can, for example, analyze user attributes (age, gender, region, etc.) in detail to reveal trends for each attribute. For example, it can analyze trends in products and services preferred by users of a particular age group or gender. The generative AI analysis unit can also reveal trends for each attribute based on user attribute data. For example, it can analyze trends in popular products and services by region to help with marketing strategies. The generative AI analysis unit can also analyze user attributes (age, gender, region, etc.) in detail to reveal trends for each attribute. For example, it can analyze trends in products and services preferred by users of a particular age group or gender. This can reveal trends for each user attribute.

[0035] The generative AI analysis unit can compare past survey results with current survey results and analyze changes over time. The generative AI analysis unit, for example, compares past survey results with current survey results and analyzes changes over time. For example, it analyzes how the popularity of a particular product or service has changed over time. The generative AI analysis unit also compares past survey results with current survey results and analyzes changes over time. For example, it analyzes how particular trends and consumer preferences have changed over time. The generative AI analysis unit also compares past survey results with current survey results and analyzes changes over time. For example, it analyzes how the popularity of a particular product or service has changed over time. This makes it possible to compare past and current survey results and analyze changes over time.

[0036] The generative AI analysis unit can analyze survey results in different languages ​​to reveal trends from an international perspective. The generative AI analysis unit, for example, analyzes survey results in different languages ​​to reveal trends from an international perspective. For example, it analyzes survey results in English, French, Chinese, etc. and compares the preferences of consumers in each country. The generative AI analysis unit also analyzes survey results in different languages ​​to reveal trends from an international perspective. For example, it analyzes the characteristics of products and services preferred by consumers in each country and develops a global marketing strategy. The generative AI analysis unit also analyzes survey results in different languages ​​to reveal trends from an international perspective. For example, it analyzes survey results in English, French, Chinese, etc. and compares the preferences of consumers in each country. This makes it possible to analyze survey results in different languages ​​to reveal trends from an international perspective.

[0037] The generative AI analysis unit can analyze visual data and provide survey results based on visual elements. The generative AI analysis unit, for example, analyzes visual data and provides survey results based on visual elements. For example, it analyzes images of product designs and packaging to clarify the influence of visual elements on consumers. The generative AI analysis unit also analyzes visual data and provides survey results based on visual elements. For example, it analyzes designs of advertisements and posters to clarify the influence of visual elements on consumers' willingness to purchase. The generative AI analysis unit also analyzes visual data and provides survey results based on visual elements. For example, it analyzes images of product designs and packaging to clarify the influence of visual elements on consumers. This makes it possible to analyze visual data and provide survey results based on visual elements.

[0038] The generative AI analysis unit can provide feedback to a user's input by referring to the results of similar past surveys. The generative AI analysis unit can, for example, provide feedback to a user's input by referring to the results of similar past surveys. For example, for a name or catchphrase entered by a user, feedback is provided based on past success stories and failure stories. The generative AI analysis unit can also provide feedback to a user's input by referring to the results of similar past surveys. For example, for a design proposal entered by a user, feedback is provided based on past success stories and failure stories. The generative AI analysis unit can also provide feedback to a user's input by referring to the results of similar past surveys. For example, for a name or catchphrase entered by a user, feedback is provided based on past success stories and failure stories. This makes it possible to provide feedback by referring to the results of similar past surveys.

[0039] The generative AI analysis unit provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. The generative AI analysis unit, for example, provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. For example, it provides feedback from the perspectives of different industries and fields in response to names and catchphrases entered by the user. The generative AI analysis unit also provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. For example, it suggests improvements to design proposals entered by the user from the perspectives of different cultures and markets. The generative AI analysis unit also provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. For example, it provides feedback from the perspectives of different industries and fields in response to names and catchphrases entered by the user. This makes it possible to provide feedback from different perspectives in response to user input, thereby promoting multifaceted consideration.

[0040] The generative AI analysis unit can provide feedback to a user's input by referring to examples from different industries and fields. The generative AI analysis unit can, for example, provide feedback to a user's input by referring to examples from different industries and fields. For example, for a name or catchphrase entered by a user, feedback is provided based on success stories from different industries. The generative AI analysis unit can also provide feedback to a user's input by referring to examples from different industries and fields. For example, for a design proposal entered by a user, feedback is provided based on success stories from different industries. The generative AI analysis unit can also provide feedback to a user's input by referring to examples from different industries and fields. For example, for a name or catchphrase entered by a user, feedback is provided based on success stories from different industries. This makes it possible to provide feedback by referring to examples from different industries and fields.

[0041] The generative AI analysis unit can provide visual feedback in response to user input to promote visual consideration. The generative AI analysis unit can, for example, provide visual feedback in response to user input to promote visual consideration. For example, it can provide visual feedback in response to a design proposal entered by the user to suggest visual improvements. The generative AI analysis unit can also provide visual feedback in response to user input to promote visual consideration. For example, it can provide visual feedback in response to a name or catchphrase entered by the user to suggest visual improvements. The generative AI analysis unit can also provide visual feedback in response to user input to promote visual consideration. For example, it can provide visual feedback in response to a design proposal entered by the user to suggest visual improvements. In this way, it is possible to provide visual feedback and promote visual consideration.

[0042] The generation AI analysis unit can automatically analyze the survey results and generate a detailed report. The generation AI analysis unit, for example, automatically analyzes the survey results and generates a detailed report. For example, it analyzes the results of a popularity poll and automatically generates a detailed report on a specific product or service. The generation AI analysis unit also automatically analyzes the survey results and generates a detailed report. For example, it analyzes trends for each user attribute and creates a detailed report based on the results. The generation AI analysis unit also automatically analyzes the survey results and generates a detailed report. For example, it analyzes the results of a popularity poll and automatically generates a detailed report on a specific product or service. This makes it possible to automatically analyze the survey results and generate a detailed report.

[0043] The generative AI analysis unit can automatically visualize the survey results and generate graphs and charts. The generative AI analysis unit, for example, automatically visualizes the survey results and generates graphs and charts. For example, the results of a popularity poll can be displayed in a graph or chart to make them visually easier to understand. The generative AI analysis unit also automatically visualizes the survey results and generates graphs and charts. For example, the results of a popularity poll can be displayed in a graph or chart to make them visually easier to understand. The generative AI analysis unit also automatically visualizes the survey results and generates graphs and charts. For example, the results of a popularity poll can be displayed in a graph or chart to make them visually easier to understand. This makes it possible to automatically visualize the survey results and generate graphs and charts.

[0044] The generative AI analysis unit can integrate different data sources and provide comprehensive survey results. The generative AI analysis unit can, for example, integrate different data sources and provide comprehensive survey results. For example, it can integrate social media, online reviews, survey results, etc., to generate comprehensive survey results. The generative AI analysis unit can also integrate different data sources and provide comprehensive survey results. For example, it can integrate user attribute data, purchase history, online reviews, etc., to generate comprehensive survey results. The generative AI analysis unit can also integrate different data sources and provide comprehensive survey results. For example, it can integrate social media, online reviews, survey results, etc., to generate comprehensive survey results. This makes it possible to integrate different data sources and provide comprehensive survey results.

[0045] The generative AI analysis unit can automatically output the investigation results in different formats (PDF, Excel, etc.). For example, the generative AI analysis unit automatically outputs the investigation results in different formats (PDF, Excel, etc.). For example, the investigation results may be output as a report in PDF format to make them easier to share. The generative AI analysis unit also automatically outputs the investigation results in different formats and generates a report in PDF or Excel format. For example, the investigation results may be output in Excel format to make it easier to analyze and process the data. The generative AI analysis unit also automatically outputs the investigation results in different formats (PDF, Excel, etc.). For example, the investigation results may be output as a report in PDF format to make it easier to share. This allows the investigation results to be output in different formats.

[0046] The generative AI analysis unit can analyze past survey results and develop an algorithm that automatically eliminates safe ideas. The generative AI analysis unit, for example, analyzes past survey results and develops an algorithm that automatically eliminates safe ideas. For example, it automatically eliminates ideas that have received low ratings in the past and focuses on more meaningful ideas. The generative AI analysis unit can also analyze past survey results and develop an algorithm that automatically eliminates safe ideas. For example, it can identify safe ideas based on past data and eliminate them, improving the quality of the survey. The generative AI analysis unit can also analyze past survey results and develop an algorithm that automatically eliminates safe ideas. For example, it can automatically eliminate ideas that have received low ratings in the past and focus on more meaningful ideas. This makes it possible to develop an algorithm that automatically eliminates safe ideas.

[0047] The generation AI analysis unit can analyze the user's preferences and prioritize presenting the most meaningful proposals to the user. The generation AI analysis unit, for example, analyzes the user's preferences and prioritizes presenting the most meaningful proposals to the user. For example, based on the user's past voting history and purchasing history, it prioritizes presenting proposals that are likely to interest the user. The generation AI analysis unit also analyzes the user's preferences and prioritizes presenting the most meaningful proposals to the user. For example, based on the user's attribute data and behavioral history, it prioritizes presenting proposals that are likely to interest the user. The generation AI analysis unit also analyzes the user's preferences and prioritizes presenting the most meaningful proposals to the user. For example, based on the user's past voting history and purchasing history, it prioritizes presenting proposals that are likely to interest the user. This makes it possible to prioritize presenting the most meaningful proposals to the user.

[0048] The generative AI analysis unit can refer to cases from different industries and fields and eliminate safe ideas. The generative AI analysis unit, for example, refers to cases from different industries and fields and eliminates safe ideas. For example, it can identify safe ideas based on successful cases from different industries and eliminate them, thereby improving the quality of the survey. The generative AI analysis unit can also refer to cases from different industries and fields and eliminate safe ideas. For example, it can identify safe ideas based on successful cases from different fields and eliminate them, thereby improving the quality of the survey. The generative AI analysis unit can also refer to cases from different industries and fields and eliminate safe ideas. For example, it can identify safe ideas based on successful cases from different industries and eliminate them, thereby improving the quality of the survey. In this way, it is possible to refer to cases from different industries and fields and eliminate safe ideas.

[0049] The generative AI analysis unit can collect user feedback in real time and automatically exclude safe ideas. The generative AI analysis unit, for example, collects user feedback in real time and automatically excludes safe ideas. For example, the quality of the survey is improved by identifying safe ideas based on user ratings and comments and excluding them. The generative AI analysis unit can also collect user feedback in real time and automatically exclude safe ideas. For example, the quality of the survey is improved by identifying safe ideas based on user ratings and comments and excluding them. The generative AI analysis unit can also collect user feedback in real time and automatically exclude safe ideas. For example, the quality of the survey is improved by identifying safe ideas based on user ratings and comments and excluding them. This allows the system to collect user feedback in real time and automatically exclude safe ideas.

[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 research system may further include a voice input unit. The voice input unit allows the user to provide survey target information by voice. For example, the user may explain the features of a product or service by voice, and the voice data may be analyzed and acquired as survey target information. The voice input unit also allows the user to respond to a survey by voice. For example, the user may answer questions by voice, and the voice data may be analyzed and acquired as survey results. This allows the user to easily participate in a survey by voice, improving the convenience of the survey.

[0052] The research system can further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes the user's website browsing history and purchase history to understand the user's interests. For example, it analyzes the websites the user frequently visits and the products they purchase, and generates personalized survey results based on that data. The behavioral analysis unit can also analyze the user's online behavior in real time and provide survey results that reflect the user's most recent interests. This makes it possible to utilize user behavioral data to provide more accurate, personalized survey results.

[0053] The research system can further include a data integration unit that integrates different data sources. The data integration unit integrates different data sources, such as social media, online reviews, and survey results, to generate comprehensive research results. For example, it can integrate Twitter and Instagram posts, Amazon reviews, and online survey results, and generate research results by combining the information from each data source. The data integration unit can also collect information from different data sources in real time and provide the latest research results. This makes it possible to utilize multiple data sources to provide more comprehensive and reliable research results.

[0054] The research system can further include an attribute analysis unit that analyzes user attribute data. The attribute analysis unit analyzes attribute data such as user age, gender, and region to identify trends for each attribute. For example, it can analyze trends in products and services preferred by users of a specific age group or gender, and provide personalized survey results based on that data. The attribute analysis unit can also analyze trends in popular products and services by region, which can be used in marketing strategies. This makes it possible to utilize user attribute data to provide more accurate survey results.

[0055] The research system can further include a multifaceted feedback unit that provides feedback from different perspectives on user input. The multifaceted feedback unit provides feedback on user input from the perspectives of different industries and fields, promoting multifaceted consideration. For example, the multifaceted feedback unit can provide feedback on the name or catchphrase entered by the user based on success stories from different industries. The multifaceted feedback unit can also suggest improvements to the user input from the perspectives of different cultures and markets. This provides feedback from different perspectives, allowing the user's ideas to be considered from multiple angles.

[0056] The research system may further include a visual feedback unit that provides visual feedback to the user's input. The visual feedback unit provides visual feedback to the user's input to promote visual consideration. For example, the visual feedback unit may provide visual feedback to a design proposal entered by the user and suggest visual improvements. The visual feedback unit may also provide visual feedback to a name or catchphrase entered by the user and suggest visual improvements. This provides visual feedback and allows the user's ideas to be visually considered.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The survey target information acquisition unit acquires survey target information, such as user behavior data, survey results, and social media posts. Step 2: The generation AI analysis unit analyzes the survey target information acquired by the survey target information acquisition unit. For example, the analysis is performed using data mining technology, statistical analysis technology, and machine learning algorithms. Step 3: The suggestion provider provides suggestions based on the results analyzed by the generative AI analyzer, for example, in the form of a report, dashboard display, or notification.

[0059] (Example 2) The research system according to an embodiment of the present invention automates simple research in the form of a popularity poll, and uses a generation AI to dig deeper into the survey results and provide suggestions for names, catchphrases, designs, etc. This allows the research system to replace simple quantitative surveys and improve the quality of the surveys.

[0060] A research system according to an embodiment includes a survey target information acquisition unit, a generation AI analysis unit, and a suggestion providing unit. The survey target information acquisition unit acquires survey target information. For example, the survey target information acquisition unit collects user behavior data. The survey target information acquisition unit can also acquire survey results. The survey target information acquisition unit can also collect social media posts. The generation AI analysis unit analyzes the survey target information acquired by the survey target information acquisition unit. For example, the generation AI analysis unit analyzes the survey target information using data mining technology. The generation AI analysis unit can also analyze the survey target information using statistical analysis technology. The generation AI analysis unit can also analyze the survey target information using a machine learning algorithm. The suggestion providing unit provides suggestions based on the results of the analysis by the generation AI analysis unit. For example, the suggestion providing unit provides suggestions in the form of a report. The suggestion providing unit can also provide suggestions in a dashboard display. The suggestion providing unit can also provide suggestions in the form of a notification. This allows the research system to automate simple research in the form of a popularity poll and provide marketing-meaningful output. For example, a research system can quickly provide suggestions for names, catchphrases, and designs for specific products or services. Research systems can also automate surveys, reducing man-hours and shortening the time to first responses. Furthermore, research systems can reduce the number of safe ideas surveyed and improve the quality of surveys.

[0061] The generation AI analysis unit can analyze a user's past voting history and generate personalized survey results that reflect the user's preferences. The generation AI analysis unit, for example, analyzes a user's past voting history and generates personalized survey results that reflect the preferences of each individual user. For example, new survey results are customized based on products and services that a specific user has previously given high ratings to. The generation AI analysis unit also provides personalized survey results based on a user's past voting history. For example, it generates survey results for new products and services related to categories that the user has previously shown interest in. The generation AI analysis unit also analyzes a user's past voting history and generates survey results that reflect the preferences of each individual user. For example, it customizes new survey results based on products and services that the user has previously given high ratings to. This makes it possible to provide personalized survey results based on the preferences of each individual user.

[0062] The generative AI analysis unit can analyze social media trends in real time and provide survey results that reflect the latest trends. The generative AI analysis unit, for example, analyzes social media trends in real time and provides survey results that reflect the latest trends. For example, it analyzes hashtags on Twitter and Instagram and generates survey results on popular topics and products. The generative AI analysis unit also analyzes social media trends in real time and provides survey results that reflect the latest trends. For example, it analyzes posts on Facebook and TikTok and generates survey results on products and services that are of high interest to users. The generative AI analysis unit also analyzes social media trends in real time and provides survey results that reflect the latest trends. For example, it analyzes hashtags on Twitter and Instagram and generates survey results on popular topics and products. This makes it possible to provide survey results that reflect the latest trends.

[0063] The generative AI analysis unit can use the emotion estimation function to analyze the user's emotional state and generate survey results that elicit positive emotions. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate survey results that elicit positive emotions. For example, it provides survey results on products and services that make the user feel joyful and excited. The generative AI analysis unit also analyzes the user's emotional state in real time and generates survey results that elicit positive emotions. For example, it provides survey results on topics and products that are likely to interest the user. The generative AI analysis unit also uses the emotion estimation function to analyze the user's emotional state and generate survey results that elicit positive emotions. For example, it provides survey results on products and services that make the user feel joyful and excited. This makes it possible to provide survey results that elicit positive emotions from the user.

[0064] The generation AI analysis unit can use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. The generation AI analysis unit can, for example, use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. For example, it can analyze images of products uploaded by users and generate investigation results related to the products. The generation AI analysis unit can also use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. For example, it can analyze images of events uploaded by users and generate investigation results related to the events. The generation AI analysis unit can also use image recognition technology to automatically extract related investigation subjects from images uploaded by users and conduct investigations. For example, it can analyze images of products uploaded by users and generate investigation results related to the products. This makes it possible to automatically extract related investigation subjects from images uploaded by users and conduct investigations.

[0065] The generative AI analysis unit can use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. The generative AI analysis unit can, for example, use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. For example, it can analyze the features of a product described by the user in the voice and provide survey results related to that product. The generative AI analysis unit can also use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. For example, it can analyze the details of an event described by the user in the voice and provide survey results related to that event. The generative AI analysis unit can also use speech recognition technology to analyze the survey target from the user's voice input and generate survey results. For example, it can analyze the features of a product described by the user in the voice and provide survey results related to that product. In this way, the generative AI analysis unit can analyze the survey target from the user's voice input and generate survey results.

[0066] The generative AI analysis unit can use the emotion estimation function to analyze the emotions that users have toward the survey subject in real time and provide survey results based on the emotions. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the emotions that users have toward the survey subject in real time and provide survey results based on the emotions. For example, it generates survey results related to products that users feel excited or happy about. The generative AI analysis unit can also analyze the user's emotions in real time and provide survey results based on the emotions. For example, it generates survey results related to events that users feel positive about. The generative AI analysis unit can also use the emotion estimation function to analyze the emotions that users have toward the survey subject in real time and provide survey results based on the emotions. For example, it generates survey results related to products that users feel excited or happy about. This makes it possible to provide survey results based on the user's emotions.

[0067] The generative AI analysis unit can analyze user attributes (age, gender, region, etc.) in detail to reveal trends for each attribute. The generative AI analysis unit can, for example, analyze user attributes (age, gender, region, etc.) in detail to reveal trends for each attribute. For example, it can analyze trends in products and services preferred by users of a particular age group or gender. The generative AI analysis unit can also reveal trends for each attribute based on user attribute data. For example, it can analyze trends in popular products and services by region to help with marketing strategies. The generative AI analysis unit can also analyze user attributes (age, gender, region, etc.) in detail to reveal trends for each attribute. For example, it can analyze trends in products and services preferred by users of a particular age group or gender. This can reveal trends for each user attribute.

[0068] The generative AI analysis unit can compare past survey results with current survey results and analyze changes over time. The generative AI analysis unit, for example, compares past survey results with current survey results and analyzes changes over time. For example, it analyzes how the popularity of a particular product or service has changed over time. The generative AI analysis unit also compares past survey results with current survey results and analyzes changes over time. For example, it analyzes how particular trends and consumer preferences have changed over time. The generative AI analysis unit also compares past survey results with current survey results and analyzes changes over time. For example, it analyzes how the popularity of a particular product or service has changed over time. This makes it possible to compare past and current survey results and analyze changes over time.

[0069] The generative AI analysis unit can use the emotion estimation function to analyze the user's emotional response to the survey results and perform a detailed analysis based on the emotions. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to the survey results and perform a detailed analysis based on the emotions. For example, it analyzes the emotions the user has toward a particular product or service and develops a marketing strategy based on those emotions. The generative AI analysis unit also analyzes the user's emotional response in real time and performs a detailed analysis based on the emotions. For example, it analyzes the characteristics of products or services that users have positive emotions about and reflects those characteristics in the marketing strategy. The generative AI analysis unit also uses the emotion estimation function to analyze the user's emotional response to the survey results and perform a detailed analysis based on the emotions. For example, it analyzes the emotions the user has toward a particular product or service and develops a marketing strategy based on those emotions. This makes it possible to perform a detailed analysis based on the user's emotional response.

[0070] The generative AI analysis unit can analyze survey results in different languages ​​to reveal trends from an international perspective. The generative AI analysis unit, for example, analyzes survey results in different languages ​​to reveal trends from an international perspective. For example, it analyzes survey results in English, French, Chinese, etc. and compares the preferences of consumers in each country. The generative AI analysis unit also analyzes survey results in different languages ​​to reveal trends from an international perspective. For example, it analyzes the characteristics of products and services preferred by consumers in each country and develops a global marketing strategy. The generative AI analysis unit also analyzes survey results in different languages ​​to reveal trends from an international perspective. For example, it analyzes survey results in English, French, Chinese, etc. and compares the preferences of consumers in each country. This makes it possible to analyze survey results in different languages ​​to reveal trends from an international perspective.

[0071] The generative AI analysis unit can analyze visual data and provide survey results based on visual elements. The generative AI analysis unit, for example, analyzes visual data and provides survey results based on visual elements. For example, it analyzes images of product designs and packaging to clarify the influence of visual elements on consumers. The generative AI analysis unit also analyzes visual data and provides survey results based on visual elements. For example, it analyzes designs of advertisements and posters to clarify the influence of visual elements on consumers' willingness to purchase. The generative AI analysis unit also analyzes visual data and provides survey results based on visual elements. For example, it analyzes images of product designs and packaging to clarify the influence of visual elements on consumers. This makes it possible to analyze visual data and provide survey results based on visual elements.

[0072] The generative AI analysis unit can use the emotion estimation function to collect users' emotional reactions to the survey results and provide new suggestions based on their emotions. The generative AI analysis unit, for example, uses the emotion estimation function to collect users' emotional reactions to the survey results and provide new suggestions based on their emotions. For example, the generative AI analysis unit analyzes the emotions users have toward a particular product or service and proposes a marketing strategy based on those emotions. The generative AI analysis unit also collects users' emotional reactions in real time and provides new suggestions based on their emotions. For example, the generative AI analysis unit analyzes the characteristics of products or services that users have positive emotions about and reflects those characteristics in a marketing strategy. The generative AI analysis unit also uses the emotion estimation function to collect users' emotional reactions to the survey results and provide new suggestions based on their emotions. For example, the generative AI analysis unit analyzes the emotions users have toward a particular product or service and proposes a marketing strategy based on those emotions. This makes it possible to provide new suggestions based on the users' emotional reactions.

[0073] The generative AI analysis unit can provide feedback to a user's input by referring to the results of similar past surveys. The generative AI analysis unit can, for example, provide feedback to a user's input by referring to the results of similar past surveys. For example, for a name or catchphrase entered by a user, feedback is provided based on past success stories and failure stories. The generative AI analysis unit can also provide feedback to a user's input by referring to the results of similar past surveys. For example, for a design proposal entered by a user, feedback is provided based on past success stories and failure stories. The generative AI analysis unit can also provide feedback to a user's input by referring to the results of similar past surveys. For example, for a name or catchphrase entered by a user, feedback is provided based on past success stories and failure stories. This makes it possible to provide feedback by referring to the results of similar past surveys.

[0074] The generative AI analysis unit provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. The generative AI analysis unit, for example, provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. For example, it provides feedback from the perspectives of different industries and fields in response to names and catchphrases entered by the user. The generative AI analysis unit also provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. For example, it suggests improvements to design proposals entered by the user from the perspectives of different cultures and markets. The generative AI analysis unit also provides feedback from different perspectives in response to user input, thereby promoting multifaceted consideration. For example, it provides feedback from the perspectives of different industries and fields in response to names and catchphrases entered by the user. This makes it possible to provide feedback from different perspectives in response to user input, thereby promoting multifaceted consideration.

[0075] The generative AI analysis unit can use the emotion estimation function to analyze the emotional response to the user's input and provide feedback based on the emotion. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the emotional response to the user's input and provide feedback based on the emotion. For example, it provides feedback that elicits positive emotions in response to the name or catchphrase entered by the user. The generative AI analysis unit also analyzes the user's emotional response in real time and provides feedback based on the emotion. For example, it suggests improvements to a design proposal entered by the user based on the emotional response. The generative AI analysis unit also uses the emotion estimation function to analyze the emotional response to the user's input and provide feedback based on the emotion. For example, it provides feedback that elicits positive emotions in response to the name or catchphrase entered by the user. This makes it possible to provide feedback based on the user's emotional response.

[0076] The generative AI analysis unit can provide feedback to a user's input by referring to examples from different industries and fields. The generative AI analysis unit can, for example, provide feedback to a user's input by referring to examples from different industries and fields. For example, for a name or catchphrase entered by a user, feedback is provided based on success stories from different industries. The generative AI analysis unit can also provide feedback to a user's input by referring to examples from different industries and fields. For example, for a design proposal entered by a user, feedback is provided based on success stories from different industries. The generative AI analysis unit can also provide feedback to a user's input by referring to examples from different industries and fields. For example, for a name or catchphrase entered by a user, feedback is provided based on success stories from different industries. This makes it possible to provide feedback by referring to examples from different industries and fields.

[0077] The generative AI analysis unit can provide visual feedback in response to user input to promote visual consideration. The generative AI analysis unit can, for example, provide visual feedback in response to user input to promote visual consideration. For example, it can provide visual feedback in response to a design proposal entered by the user to suggest visual improvements. The generative AI analysis unit can also provide visual feedback in response to user input to promote visual consideration. For example, it can provide visual feedback in response to a name or catchphrase entered by the user to suggest visual improvements. The generative AI analysis unit can also provide visual feedback in response to user input to promote visual consideration. For example, it can provide visual feedback in response to a design proposal entered by the user to suggest visual improvements. In this way, it is possible to provide visual feedback and promote visual consideration.

[0078] The generative AI analysis unit can use the emotion estimation function to analyze the emotional response to the user's input in real time and provide feedback based on the emotion. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the emotional response to the user's input in real time and provide feedback based on the emotion. For example, it provides feedback that elicits positive emotions in response to the name or catchphrase entered by the user. The generative AI analysis unit can also analyze the user's emotional response in real time and provide feedback based on the emotion. For example, it can suggest improvements to a design proposal entered by the user based on the emotional response. The generative AI analysis unit can also use the emotion estimation function to analyze the emotional response to the user's input in real time and provide feedback based on the emotion. For example, it can provide feedback that elicits positive emotions in response to the name or catchphrase entered by the user. This makes it possible to provide feedback based on the user's emotional response in real time.

[0079] The generation AI analysis unit can automatically analyze the survey results and generate a detailed report. The generation AI analysis unit, for example, automatically analyzes the survey results and generates a detailed report. For example, it analyzes the results of a popularity poll and automatically generates a detailed report on a specific product or service. The generation AI analysis unit also automatically analyzes the survey results and generates a detailed report. For example, it analyzes trends for each user attribute and creates a detailed report based on the results. The generation AI analysis unit also automatically analyzes the survey results and generates a detailed report. For example, it analyzes the results of a popularity poll and automatically generates a detailed report on a specific product or service. This makes it possible to automatically analyze the survey results and generate a detailed report.

[0080] The generative AI analysis unit can automatically visualize the survey results and generate graphs and charts. The generative AI analysis unit, for example, automatically visualizes the survey results and generates graphs and charts. For example, the results of a popularity poll can be displayed in a graph or chart to make them visually easier to understand. The generative AI analysis unit also automatically visualizes the survey results and generates graphs and charts. For example, the results of a popularity poll can be displayed in a graph or chart to make them visually easier to understand. The generative AI analysis unit also automatically visualizes the survey results and generates graphs and charts. For example, the results of a popularity poll can be displayed in a graph or chart to make them visually easier to understand. This makes it possible to automatically visualize the survey results and generate graphs and charts.

[0081] The generative AI analysis unit can use the emotion estimation function to automatically analyze the user's emotional response to the survey results and generate a report based on the emotions. The generative AI analysis unit, for example, uses the emotion estimation function to automatically analyze the user's emotional response to the survey results and generate a report based on the emotions. For example, it analyzes the emotions a user has toward a particular product or service and creates a report based on those emotions. The generative AI analysis unit also analyzes the user's emotional response in real time and generates a report based on the emotions. For example, it analyzes the characteristics of a product or service that the user has positive emotions about and reflects those characteristics in the report. The generative AI analysis unit also uses the emotion estimation function to automatically analyze the user's emotional response to the survey results and generate a report based on the emotions. For example, it analyzes the emotions a user has toward a particular product or service and creates a report based on those emotions. In this way, a report based on the user's emotional response can be automatically generated.

[0082] The generative AI analysis unit can integrate different data sources and provide comprehensive survey results. The generative AI analysis unit can, for example, integrate different data sources and provide comprehensive survey results. For example, it can integrate social media, online reviews, survey results, etc., to generate comprehensive survey results. The generative AI analysis unit can also integrate different data sources and provide comprehensive survey results. For example, it can integrate user attribute data, purchase history, online reviews, etc., to generate comprehensive survey results. The generative AI analysis unit can also integrate different data sources and provide comprehensive survey results. For example, it can integrate social media, online reviews, survey results, etc., to generate comprehensive survey results. This makes it possible to integrate different data sources and provide comprehensive survey results.

[0083] The generative AI analysis unit can automatically output the investigation results in different formats (PDF, Excel, etc.). For example, the generative AI analysis unit automatically outputs the investigation results in different formats (PDF, Excel, etc.). For example, the investigation results may be output as a report in PDF format to make them easier to share. The generative AI analysis unit also automatically outputs the investigation results in different formats and generates a report in PDF or Excel format. For example, the investigation results may be output in Excel format to make it easier to analyze and process the data. The generative AI analysis unit also automatically outputs the investigation results in different formats (PDF, Excel, etc.). For example, the investigation results may be output as a report in PDF format to make it easier to share. This allows the investigation results to be output in different formats.

[0084] The generative AI analysis unit can use the emotion estimation function to analyze the user's emotional response to the survey results in real time and provide feedback based on the emotions. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to the survey results in real time and provide feedback based on the emotions. For example, the generative AI analysis unit analyzes the user's emotional response to a particular product or service and provides feedback based on the emotions. The generative AI analysis unit also analyzes the user's emotional response in real time and provides feedback based on the emotions. For example, the generative AI analysis unit analyzes the characteristics of a product or service that the user feels positive about and reflects those characteristics in the feedback. The generative AI analysis unit also uses the emotion estimation function to analyze the user's emotional response to the survey results in real time and provides feedback based on the emotions. For example, the generative AI analysis unit analyzes the user's emotional response to a particular product or service and provides feedback based on the emotions. This makes it possible to provide feedback based on the user's emotional response in real time.

[0085] The generative AI analysis unit can analyze past survey results and develop an algorithm that automatically eliminates safe ideas. The generative AI analysis unit, for example, analyzes past survey results and develops an algorithm that automatically eliminates safe ideas. For example, it automatically eliminates ideas that have received low ratings in the past and focuses on more meaningful ideas. The generative AI analysis unit can also analyze past survey results and develop an algorithm that automatically eliminates safe ideas. For example, it can identify safe ideas based on past data and eliminate them, improving the quality of the survey. The generative AI analysis unit can also analyze past survey results and develop an algorithm that automatically eliminates safe ideas. For example, it can automatically eliminate ideas that have received low ratings in the past and focus on more meaningful ideas. This makes it possible to develop an algorithm that automatically eliminates safe ideas.

[0086] The generation AI analysis unit can analyze the user's preferences and prioritize presenting the most meaningful proposals to the user. The generation AI analysis unit, for example, analyzes the user's preferences and prioritizes presenting the most meaningful proposals to the user. For example, based on the user's past voting history and purchasing history, it prioritizes presenting proposals that are likely to interest the user. The generation AI analysis unit also analyzes the user's preferences and prioritizes presenting the most meaningful proposals to the user. For example, based on the user's attribute data and behavioral history, it prioritizes presenting proposals that are likely to interest the user. The generation AI analysis unit also analyzes the user's preferences and prioritizes presenting the most meaningful proposals to the user. For example, based on the user's past voting history and purchasing history, it prioritizes presenting proposals that are likely to interest the user. This makes it possible to prioritize presenting the most meaningful proposals to the user.

[0087] The generative AI analysis unit can use the emotion estimation function to analyze the user's emotional response and present meaningful suggestions based on the emotion. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response and present meaningful suggestions based on the emotion. For example, it analyzes the characteristics of products and services that the user feels positive about and presents suggestions that have those characteristics. The generative AI analysis unit also analyzes the user's emotional response in real time and presents meaningful suggestions based on the emotion. For example, it analyzes the characteristics of products and services that the user feels positive about and presents suggestions that have those characteristics. The generative AI analysis unit also uses the emotion estimation function to analyze the user's emotional response and present meaningful suggestions based on the emotion. For example, it analyzes the characteristics of products and services that the user feels positive about and presents suggestions that have those characteristics. In this way, it is possible to present meaningful suggestions based on the user's emotional response.

[0088] The generative AI analysis unit can refer to cases from different industries and fields and eliminate safe ideas. The generative AI analysis unit, for example, refers to cases from different industries and fields and eliminates safe ideas. For example, it can identify safe ideas based on successful cases from different industries and eliminate them, thereby improving the quality of the survey. The generative AI analysis unit can also refer to cases from different industries and fields and eliminate safe ideas. For example, it can identify safe ideas based on successful cases from different fields and eliminate them, thereby improving the quality of the survey. The generative AI analysis unit can also refer to cases from different industries and fields and eliminate safe ideas. For example, it can identify safe ideas based on successful cases from different industries and eliminate them, thereby improving the quality of the survey. In this way, it is possible to refer to cases from different industries and fields and eliminate safe ideas.

[0089] The generative AI analysis unit can collect user feedback in real time and automatically exclude safe ideas. The generative AI analysis unit, for example, collects user feedback in real time and automatically excludes safe ideas. For example, the quality of the survey is improved by identifying safe ideas based on user ratings and comments and excluding them. The generative AI analysis unit can also collect user feedback in real time and automatically exclude safe ideas. For example, the quality of the survey is improved by identifying safe ideas based on user ratings and comments and excluding them. The generative AI analysis unit can also collect user feedback in real time and automatically exclude safe ideas. For example, the quality of the survey is improved by identifying safe ideas based on user ratings and comments and excluding them. This allows the system to collect user feedback in real time and automatically exclude safe ideas.

[0090] The generative AI analysis unit can use the emotion estimation function to analyze the user's emotional responses in real time and present meaningful ideas based on the emotions. The generative AI analysis unit, for example, uses the emotion estimation function to analyze the user's emotional responses in real time and present meaningful ideas based on the emotions. For example, it analyzes the characteristics of products and services that the user feels positive about and presents ideas that have those characteristics. The generative AI analysis unit can also analyze the user's emotional responses in real time and present meaningful ideas based on the emotions. For example, it analyzes the characteristics of products and services that the user feels positive about and presents ideas that have those characteristics. The generative AI analysis unit can also analyze the user's emotional responses in real time and present meaningful ideas based on the emotions. For example, it analyzes the characteristics of products and services that the user feels positive about and presents ideas that have those characteristics. The generative AI analysis unit can also use the emotion estimation function to analyze the user's emotional responses in real time and present meaningful ideas based on the emotions. For example, it analyzes the characteristics of products and services that the user feels positive about and presents ideas that have those characteristics. This makes it possible to present meaningful ideas based on the user's emotional responses in real time.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The research system may further include a voice input unit. The voice input unit allows the user to provide survey target information by voice. For example, the user may explain the features of a product or service by voice, and the voice data may be analyzed and acquired as survey target information. The voice input unit also allows the user to respond to a survey by voice. For example, the user may answer questions by voice, and the voice data may be analyzed and acquired as survey results. This allows the user to easily participate in a survey by voice, improving the convenience of the survey.

[0093] The research system can further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes the user's website browsing history and purchase history to understand the user's interests. For example, it analyzes the websites the user frequently visits and the products they purchase, and generates personalized survey results based on that data. The behavioral analysis unit can also analyze the user's online behavior in real time and provide survey results that reflect the user's most recent interests. This makes it possible to utilize user behavioral data to provide more accurate, personalized survey results.

[0094] The research system can further include a data integration unit that integrates different data sources. The data integration unit integrates different data sources, such as social media, online reviews, and survey results, to generate comprehensive research results. For example, it can integrate Twitter and Instagram posts, Amazon reviews, and online survey results, and generate research results by combining the information from each data source. The data integration unit can also collect information from different data sources in real time and provide the latest research results. This makes it possible to utilize multiple data sources to provide more comprehensive and reliable research results.

[0095] The research system can further include an emotion estimation unit that estimates the user's emotion. The emotion estimation unit estimates the user's emotion from their facial expressions and voice, and generates survey results based on that emotion. For example, if the user feels joy or excitement about the survey subject, it provides a positive survey result that reflects that emotion. The emotion estimation unit can also analyze the user's emotion in real time and provide survey results that reflect changes in emotion. This allows the system to provide survey results that take the user's emotion into consideration, improving user satisfaction.

[0096] The research system can further include an attribute analysis unit that analyzes user attribute data. The attribute analysis unit analyzes attribute data such as user age, gender, and region to identify trends for each attribute. For example, it can analyze trends in products and services preferred by users of a specific age group or gender, and provide personalized survey results based on that data. The attribute analysis unit can also analyze trends in popular products and services by region, which can be used in marketing strategies. This makes it possible to utilize user attribute data to provide more accurate survey results.

[0097] The research system may further include an emotion analysis unit that estimates the user's emotions and provides survey results based on those emotions. The emotion analysis unit estimates emotions from the user's facial expressions and voice and generates survey results based on those emotions. For example, if the user has positive emotions toward the survey subject, the system provides survey results that reflect those emotions. The emotion analysis unit can also analyze the user's emotions in real time and provide survey results that correspond to changes in emotions. This allows the system to provide survey results that take the user's emotions into consideration, improving user satisfaction.

[0098] The research system can further include a multifaceted feedback unit that provides feedback from different perspectives on user input. The multifaceted feedback unit provides feedback on user input from the perspectives of different industries and fields, promoting multifaceted consideration. For example, the multifaceted feedback unit can provide feedback on the name or catchphrase entered by the user based on success stories from different industries. The multifaceted feedback unit can also suggest improvements to the user input from the perspectives of different cultures and markets. This provides feedback from different perspectives, allowing the user's ideas to be considered from multiple angles.

[0099] The research system may further include an emotion feedback unit that estimates the user's emotion and provides feedback based on that emotion. The emotion feedback unit estimates the user's emotion from their facial expressions and voice, and provides feedback based on that emotion. For example, the emotion feedback unit may provide feedback that elicits positive emotions in response to a name or catchphrase entered by the user. The emotion feedback unit may also analyze the user's emotion in real time and provide feedback according to changes in emotion. This allows for providing feedback that takes the user's emotion into consideration, thereby improving user satisfaction.

[0100] The research system may further include a visual feedback unit that provides visual feedback to the user's input. The visual feedback unit provides visual feedback to the user's input to promote visual consideration. For example, the visual feedback unit may provide visual feedback to a design proposal entered by the user and suggest visual improvements. The visual feedback unit may also provide visual feedback to a name or catchphrase entered by the user and suggest visual improvements. This provides visual feedback and allows the user's ideas to be visually considered.

[0101] The research system can further include an emotion proposal presentation unit that estimates the user's emotion and presents meaningful proposals based on the emotion. The emotion proposal presentation unit estimates the user's emotion from their facial expressions and voice, and presents meaningful proposals based on the emotion. For example, it analyzes the characteristics of products and services that the user feels positive about, and presents proposals that have those characteristics. The emotion proposal presentation unit can also analyze the user's emotion in real time and present meaningful proposals in response to changes in emotion. This allows meaningful proposals that take the user's emotion into consideration to be presented, improving user satisfaction.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The survey target information acquisition unit acquires survey target information, such as user behavior data, survey results, and social media posts. Step 2: The generation AI analysis unit analyzes the survey target information acquired by the survey target information acquisition unit. For example, the analysis is performed using data mining technology, statistical analysis technology, and machine learning algorithms. Step 3: The suggestion provider provides suggestions based on the results analyzed by the generative AI analyzer, for example, in the form of a report, dashboard display, or notification.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a survey target information acquisition unit that acquires survey target information; a generation AI analysis unit that analyzes the survey target information acquired by the survey target information acquisition unit; a suggestion providing unit that provides suggestions based on the results of the analysis by the generation AI analysis unit. A system characterized by:

2. The generation AI analysis unit Analyzing a user's past voting history and generating personalized survey results that reflect the user's preferences 2. The system of claim 1.

3. The generation AI analysis unit Analyze social media trends in real time and provide research results that reflect the latest trends 2. The system of claim 1.

4. The generation AI analysis unit Analyze the user's emotional state and generate survey results that elicit positive emotions 2. The system of claim 1.

5. The generation AI analysis unit Using image recognition technology, relevant research subjects are automatically extracted from images uploaded by users and research is conducted.

2. The system of claim 1.

Images