system

Abstract

The system according to the embodiment aims to efficiently collect user needs and create new products based on them. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a feedback collection unit. The collection unit collects data on the Internet. The analysis unit analyzes the data collected by the collection unit to identify user needs. The generation unit generates a new product based on the needs identified by the analysis unit. The feedback collection unit collects feedback on the product prototype generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] In conventional technologies, the process of efficiently gathering user needs and generating new products based on them is not sufficiently automated, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect user needs and create new products based on them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a feedback collection unit. The collection unit collects data on the Internet. The analysis unit analyzes the data collected by the collection unit and identifies user needs. The generation unit generates a new product based on the needs identified by the analysis unit. The feedback collection unit collects feedback on the prototype of the product generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect user needs and generate new products based on the collected needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A product generation system according to an embodiment of the present invention automatically collects features and services that users "want more of" and automatically generates products based on the collected information. The product generation system collects information about features and services that users "want more of" from online review sites, social networking sites, survey results, and other sources. This information collection is performed by a generation AI. For example, a car review site might collect comments such as "I want the same model of car at a slightly lower price" or a television review might collect comments such as "I wish the screen was a little bigger." The generation AI then analyzes the collected information to identify user needs. For example, if there are many comments about cars, the AI ​​can identify the features and services that most users are particularly looking for. This allows the AI ​​to understand user needs. The generation AI then automatically generates new products based on the identified needs. For example, a new car model might be designed taking into account the user's desired price range and features. For televisions, a new model might be designed that reflects the user's desired screen size and features. Finally, a prototype of the generated product is manufactured and user feedback is collected. Based on this feedback, the product is further improved and the final product is released to market. For example, in a hospital, a system can be developed to shorten the time from prescription to payment and receipt of medication. This will reduce patient waiting times and improve the quality of medical services. This will enable the product generation system to quickly develop and provide products that match user needs. For example, products that reflect the functions and services desired by users can be quickly brought to market. Furthermore, by improving products based on user feedback, it will be possible to provide higher quality products.

[0029] A product creation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a feedback collection unit. The collection unit collects data from the Internet. Examples of the data on the Internet include, but are not limited to, text data, image data, and audio data. For example, the collection unit collects data from review sites using web scraping technology. The collection unit can also acquire data from social networking sites using an API. The collection unit can also collect results of online surveys. For example, the collection unit collects user comments from a specific review site and saves them as text data. The collection unit uses the social networking site's API to collect user posts and save them in a database. The collection unit automatically collects results of online surveys and saves them for analysis. The analysis unit analyzes the data collected by the collection unit to identify user needs. The analysis is performed, for example, using natural language processing technology, but is not limited to, examples. For example, the analysis unit analyzes text data using morphological analysis to extract frequently occurring keywords. The analysis unit can also analyze sentence structure using grammatical analysis to identify user intent. The analysis unit can also analyze the meaning of text data using semantic analysis to identify user needs. For example, the analysis unit can extract frequently occurring keywords from user comments using morphological analysis. The analysis unit can analyze the sentence structure of the user comments using grammatical analysis to identify the user's intention. The analysis unit can analyze the meaning of the user comments using semantic analysis to identify the user's needs. The generation unit generates a new product based on the needs identified by the analysis unit. Generation can be performed using, for example, machine learning technology, but is not limited to such examples. For example, the generation unit can design a new product using a neural network. The generation unit can also determine product specifications using a decision tree. The generation unit can also optimally design a product using a support vector machine. For example, the generation unit can design a new car model based on user needs using a neural network. The generation unit can determine television specifications based on user needs using a decision tree.The generation unit uses a support vector machine to optimally design a medical system based on user needs. The feedback collection unit collects user feedback on the product prototype generated by the generation unit. Feedback collection can be performed, for example, using a questionnaire survey, but is not limited to such examples. For example, the feedback collection unit conducts an online survey to collect user feedback. The feedback collection unit can also collect user reviews. The feedback collection unit can also conduct interviews to collect user feedback. For example, the feedback collection unit collects user opinions on the generated product prototype through an online survey. The feedback collection unit collects user ratings on the generated product from a user review site. The feedback collection unit collects detailed user feedback on the generated product through interviews. This allows the product generation system according to the embodiment to automatically generate a new product based on user needs and improve it by collecting feedback. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit may input user feedback data into the generation AI and cause the generation AI to analyze the feedback.

[0030] The collection unit can collect data from review sites, social networking sites, and survey results. Examples of review sites include, but are not limited to, Yahoo! Shopping and Amazon. Examples of social networking sites include, but are not limited to, Twitter and Facebook. Examples of survey results include, but are not limited to, online surveys and paper-based surveys. For example, the collection unit collects user comments from review sites. The collection unit can also collect user posts from social networking sites. The collection unit can also collect survey results. For example, the collection unit collects user comments from Amazon's review site and saves them as text data. The collection unit uses Twitter's API to collect user posts and save them in a database. The collection unit automatically collects results of online surveys and saves them for analysis. This makes it possible to collect user needs from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected from review sites into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data using natural language processing to identify user needs. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can analyze text data using morphological analysis to extract frequently occurring keywords. The analysis unit can also analyze sentence structure using grammatical analysis to identify user intention. The analysis unit can also analyze the meaning of text data using semantic analysis to identify user needs. For example, the analysis unit can use morphological analysis to extract frequently occurring keywords from user comments. The analysis unit can use grammatical analysis to analyze the sentence structure of the user comments to identify the user intention. The analysis unit can use semantic analysis to analyze the meaning of the user comments to identify user needs. In this way, natural language processing can be used to accurately identify user needs. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI identify user needs.

[0032] The generation unit can generate a new product using machine learning based on the identified needs. Examples of machine learning include, but are not limited to, neural networks, decision trees, and support vector machines. The generation unit can, for example, use a neural network to design a new product. The generation unit can also use a decision tree to determine product specifications. The generation unit can also use a support vector machine to optimally design a product. For example, the generation unit can use a neural network to design a new car model based on user needs. The generation unit can use a decision tree to determine television specifications based on user needs. The generation unit can also use a support vector machine to optimally design a medical system based on user needs. In this way, by using machine learning, it is possible to automatically generate a product that meets user needs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the identified needs into a generation AI and cause the generation AI to generate a new product.

[0033] The feedback collection unit can collect user feedback on the generated product prototype. Prototype creation includes, but is not limited to, 3D printing, CAD design, and the like. For example, the feedback collection unit can collect user feedback by conducting an online survey. The feedback collection unit can also collect user reviews. The feedback collection unit can also collect user feedback by conducting interviews. For example, the feedback collection unit can collect user opinions on the generated product prototype through an online survey. The feedback collection unit can collect user ratings on the generated product from a user review site. The feedback collection unit can collect detailed user feedback on the generated product through interviews. Collecting user feedback can be useful for improving the product. Some or all of the above-described processing in the feedback collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback collection unit can input user feedback data into a generation AI and cause the generation AI to analyze the feedback.

[0034] During collection, the collection unit can analyze the user's past posting history and select the optimal collection method. For example, the collection unit prioritizes collecting data from review sites where the user frequently posted in the past. The collection unit can also collect data from social networking sites where the user has left many comments in the past. The collection unit can also collect data from similar surveys based on the results of surveys the user has previously participated in. For example, the collection unit prioritizes collecting data from review sites where the user frequently posted in the past. The collection unit collects data from social networking sites where the user has left many comments in the past. The collection unit collects data from similar surveys based on the results of surveys the user has previously participated in. This allows efficient data collection by selecting the optimal collection method based on the user's past posting history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history data into a generation AI and cause the generation AI to select the optimal collection method.

[0035] The collection unit may perform filtering based on the user's current areas of interest during collection. For example, the collection unit may prioritize collecting reviews related to product categories in which the user is currently interested. The collection unit may also collect social media posts related to keywords recently searched by the user. The collection unit may also collect data based on the themes of surveys in which the user recently participated. For example, the collection unit may prioritize collecting reviews related to product categories in which the user is currently interested. The collection unit may collect social media posts related to keywords recently searched by the user. The collection unit may collect data based on the themes of surveys in which the user recently participated. This allows highly relevant data to be collected by filtering the data based on the user's current areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's current areas of interest data into a generation AI and cause the generation AI to filter the data.

[0036] The collection unit can select an appropriate collection means depending on the user's input method during collection. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. If the user is using text input, the collection unit can also prioritize collection of text data. If the user is posting images, the collection unit can also prioritize collection of image data. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. If the user is using text input, the collection unit prioritizes collection of text data. If the user is posting images, the collection unit prioritizes collection of image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0037] During collection, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes collecting reviews related to that region. If the user is in a specific city, the collection unit can also collect social media posts related to that city. If the user is in a specific country, the collection unit can also collect survey results related to that country. For example, if the user is in a specific region, the collection unit prioritizes collecting reviews related to that region. If the user is in a specific city, the collection unit collects social media posts related to that city. If the user is in a specific country, the collection unit collects survey results related to that country. This makes it possible to understand regionally specific needs by preferentially collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information data into a generation AI and cause the generation AI to collect highly relevant data.

[0038] During collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit preferentially collects data from social media sites to which the user frequently posts. The collection unit can also collect data from social media sites to which the user leaves many comments. The collection unit can also collect data based on posts to which the user receives many likes. For example, the collection unit preferentially collects data from social media sites to which the user frequently posts. The collection unit collects data from social media sites to which the user leaves many comments. The collection unit collects data based on posts to which the user receives many likes. In this way, by collecting related data based on the user's social media activity, it is possible to collect data that reflects the user's interests. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0039] The collection unit can adjust the collection method during collection by reflecting the user's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize use of collection methods that the user has previously preferred. The collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit prioritizes use of collection methods that the user has previously preferred. The collection unit avoids collection methods that the user has previously expressed dissatisfaction with. In this way, by customizing the collection method based on the user's past feedback, data can be collected in a manner that is optimal for the user. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0040] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit performs a simplified analysis on data of low importance. The analysis unit performs an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to set the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a sentiment analysis algorithm to product reviews. The analysis unit can also apply a topic modeling algorithm to social media posts. The analysis unit can also apply a statistical analysis algorithm to survey results. For example, the analysis unit applies a sentiment analysis algorithm to product reviews. The analysis unit applies a topic modeling algorithm to social media posts. The analysis unit applies a statistical analysis algorithm to survey results. In this way, by applying an appropriate analysis algorithm depending on the data category, highly accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also prioritize the use of analysis methods that the user has previously preferred. The analysis unit can also avoid analysis methods that the user has previously expressed dissatisfaction with. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit prioritizes the use of analysis methods that the user has previously preferred. The analysis unit avoids analysis methods that the user has previously expressed dissatisfaction with. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can set analysis priorities based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also moderately analyze data that was submitted at a medium time. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit postpones analysis of older data. The analysis unit moderately analyzes data that was submitted at a medium time. In this way, by determining the analysis priorities based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI and cause the generation AI to set the analysis priorities.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. The analysis unit can also moderately analyze data with medium relevance. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit postpones analysis of data with low relevance. The analysis unit moderately analyzes data with medium relevance. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can set the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also avoid technical terminology for users with low levels of expertise. The analysis unit can also use technical terminology moderately for users with intermediate levels of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit avoids technical terminology for users with low levels of expertise. The analysis unit uses technical terminology moderately for users with intermediate levels of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0046] The generation unit can set the level of detail of the generation based on the importance of the identified needs during generation. For example, the generation unit sets detailed specifications for needs of high importance. The generation unit can also set simple specifications for needs of low importance. The generation unit can also set specifications with an appropriate level of detail for needs of medium importance. For example, the generation unit sets detailed specifications for needs of high importance. The generation unit sets simple specifications for needs of low importance. The generation unit sets specifications with an appropriate level of detail for needs of medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the identified needs, a detailed product can be generated for important needs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the identified needs to the generation AI and cause the generation AI to set the level of detail of the generation.

[0047] During generation, the generation unit can apply different generation algorithms depending on the category of the need. For example, the generation unit applies a vehicle design algorithm to a need related to a car. The generation unit can also apply a home appliance design algorithm to a need related to a television. The generation unit can also apply a medical system design algorithm to a need related to medical services. For example, the generation unit applies a vehicle design algorithm to a need related to a car. The generation unit applies a home appliance design algorithm to a need related to a television. The generation unit applies a medical system design algorithm to a need related to medical services. In this way, by applying an appropriate generation algorithm depending on the category of the need, a highly accurate product can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input need category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0048] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit can also prioritize the use of generation methods that the user has previously preferred. The generation unit can also avoid generation methods that the user has previously expressed dissatisfaction with. For example, the generation unit adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit prioritizes the use of generation methods that the user has previously preferred. The generation unit avoids generation methods that the user has previously expressed dissatisfaction with. This makes it possible to improve the accuracy of generation by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0049] The generation unit can set a generation priority based on the submission time of the needs at the time of generation. For example, the generation unit generates the latest needs with priority. The generation unit can also generate older needs later. The generation unit can also generate needs with a medium submission time in a moderate manner. For example, the generation unit generates the latest needs with priority. The generation unit generates older needs later. The generation unit generates needs with a medium submission time in a moderate manner. In this way, by determining the generation priority based on the submission time of the needs, the latest needs can be generated with priority. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the submission time of the needs into the generation AI and cause the generation AI to set the generation priority.

[0050] The generation unit can adjust the order of generation based on the relevance of needs during generation. For example, the generation unit prioritizes generating needs with high relevance. The generation unit can also postpone generating needs with low relevance. The generation unit can also moderately generate needs with medium relevance. For example, the generation unit prioritizes generating needs with high relevance. The generation unit postpones generating needs with low relevance. The generation unit moderately generates needs with medium relevance. In this way, by adjusting the order of generation based on the relevance of needs, highly relevant needs can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input needs relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0051] During generation, the generation unit can set the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit uses a lot of technical terminology for users with high expertise. The generation unit can also avoid technical terminology for users with low expertise. The generation unit can also use technical terminology moderately for users with intermediate expertise. For example, the generation unit uses a lot of technical terminology for users with high expertise. The generation unit avoids technical terminology for users with low expertise. The generation unit uses technical terminology moderately for users with intermediate expertise. This makes it possible to provide product specifications that are easy for users to understand by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's expertise level data into the generation AI and cause the generation AI to use technical terminology.

[0052] When collecting feedback, the feedback collection unit can select an appropriate collection method based on the user's past feedback history. For example, if the user provided detailed feedback in the past, the feedback collection unit requests similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit can also request similar brief feedback. The feedback collection unit can also select an optimal collection method based on the content of feedback provided by the user in the past. For example, if the user provided detailed feedback in the past, the feedback collection unit requests similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit requests similar brief feedback. The feedback collection unit selects an optimal collection method based on the content of feedback provided by the user in the past. In this way, by selecting an optimal collection method based on the user's past feedback history, feedback can be collected efficiently. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history data to the generation AI and cause the generation AI to select an optimal collection method.

[0053] The feedback collection unit can filter the feedback based on the user's current areas of interest when collecting the feedback. For example, the feedback collection unit prioritizes collecting feedback related to product categories in which the user is currently interested. The feedback collection unit can also collect feedback related to keywords recently searched by the user. The feedback collection unit can also collect feedback based on themes of surveys in which the user has recently participated. For example, the feedback collection unit prioritizes collecting feedback related to product categories in which the user is currently interested. The feedback collection unit collects feedback related to keywords recently searched by the user. The feedback collection unit collects feedback based on themes of surveys in which the user has recently participated. In this way, by filtering the feedback based on the user's current areas of interest, it is possible to collect highly relevant feedback. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input data on the user's current areas of interest to a generation AI and cause the generation AI to filter the feedback.

[0054] When collecting feedback, the feedback collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the feedback collection unit prioritizes collecting voice feedback. When the user uses text input, the feedback collection unit can also prioritize collecting text feedback. When the user posts images, the feedback collection unit can also prioritize collecting image feedback. For example, when the user uses voice input, the feedback collection unit prioritizes collecting voice feedback. When the user uses text input, the feedback collection unit prioritizes collecting text feedback. When the user posts images, the feedback collection unit prioritizes collecting image feedback. This allows efficient collection of feedback by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input user input method data to a generation AI and cause the generation AI to select the optimal collection means.

[0055] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback based on the user's geographical location information. For example, when the user is in a specific region, the feedback collection unit prioritizes collecting feedback related to the region. When the user is in a specific city, the feedback collection unit can also collect feedback related to the city. When the user is in a specific country, the feedback collection unit can also collect feedback related to the country. For example, when the user is in a specific region, the feedback collection unit prioritizes collecting feedback related to the region. When the user is in a specific city, the feedback collection unit collects feedback related to the city. When the user is in a specific country, the feedback collection unit collects feedback related to the country. This makes it possible to understand region-specific needs by preferentially collecting highly relevant feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit may input the user's geographical location information data to a generation AI and cause the generation AI to collect highly relevant feedback.

[0056] The feedback collection unit can analyze the user's social media activity and collect relevant feedback when collecting feedback. For example, the feedback collection unit preferentially collects feedback from social media platforms to which the user frequently posts. The feedback collection unit can also collect feedback from social media platforms to which the user leaves many comments. The feedback collection unit can also collect feedback based on posts to which the user receives many likes. For example, the feedback collection unit preferentially collects feedback from social media platforms to which the user frequently posts. The feedback collection unit collects feedback from social media platforms to which the user leaves many comments. The feedback collection unit collects feedback based on posts to which the user receives many likes. In this way, by collecting relevant feedback based on the user's social media activity, it is possible to collect feedback that reflects the user's interests. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect relevant feedback.

[0057] When collecting feedback, the feedback collection unit can adjust the collection method by reflecting the user's past feedback. The feedback collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The feedback collection unit can also prioritize use of collection methods that the user has previously preferred. The feedback collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. For example, the feedback collection unit adjusts the collection method based on feedback provided by the user in the past. The feedback collection unit prioritizes use of collection methods that the user has previously preferred. The feedback collection unit avoids collection methods that the user has previously expressed dissatisfaction with. In this way, by customizing the collection method based on the user's past feedback, it is possible to collect feedback in a manner that is optimal for the user. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When collecting data, the collection unit can analyze the user's past posting history and select the optimal collection method. For example, the collection unit can prioritize collecting data from review sites where the user frequently posted in the past. The collection unit can also collect data from social networking sites where the user has left many comments in the past. The collection unit can also collect data from similar surveys based on the results of surveys the user has previously participated in. This allows for efficient data collection by selecting the optimal collection method based on the user's past posting history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history data into a generation AI and have the generation AI select the optimal collection method.

[0060] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, a detailed analysis is performed on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI set the level of detail of the analysis.

[0061] The generation unit can set the level of detail of the generation based on the importance of the identified needs during generation. For example, detailed specifications are set for needs of high importance. The generation unit can also set simple specifications for needs of low importance. The generation unit can also set specifications with an appropriate level of detail for needs of medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the identified needs, a detailed product can be generated for important needs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the identified needs into the generation AI and cause the generation AI to set the level of detail of the generation.

[0062] When collecting feedback, the feedback collection unit can select an appropriate collection method based on the user's past feedback history. For example, if the user provided detailed feedback in the past, the feedback collection unit can request similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit can also request similar brief feedback. The feedback collection unit can also select an optimal collection method based on the content of feedback provided by the user in the past. This allows feedback to be collected efficiently by selecting an optimal collection method based on the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history data into the generation AI and cause the generation AI to select an optimal collection method.

[0063] The collection unit may perform filtering based on the user's current areas of interest during collection. For example, reviews related to product categories in which the user is currently interested may be preferentially collected. The collection unit may also collect social media posts related to keywords recently searched by the user. The collection unit may also collect data based on the themes of surveys in which the user recently participated. This allows highly relevant data to be collected by filtering the data based on the user's current areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's current areas of interest data into a generation AI and have the generation AI perform data filtering.

[0064] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a sentiment analysis algorithm can be applied to product reviews. The analysis unit can also apply a topic modeling algorithm to social media posts. The analysis unit can also apply a statistical analysis algorithm to survey results. This makes it possible to obtain highly accurate analysis results by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply an appropriate analysis algorithm.

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

[0066] Step 1: The collection unit collects data from the Internet. This data includes text data, image data, and audio data. The collection unit can use web scraping technology to collect data from review sites and APIs to obtain data from social media. It can also collect the results of online surveys. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the user's needs. The analysis is carried out using natural language processing techniques, such as morphological analysis, grammatical analysis, and semantic analysis, to analyze the text data and identify frequently occurring keywords and the user's intent. Step 3: The generation unit generates new products based on the needs identified by the analysis unit. This is done using machine learning techniques, such as neural networks, decision trees, and support vector machines, to determine the product design and specifications. Step 4: The feedback collection unit collects user feedback on the product prototype generated by the generation unit. The feedback collection is carried out through online surveys, user reviews, interviews, etc.

[0067] (Example 2) A product generation system according to an embodiment of the present invention automatically collects features and services that users "want more of" and automatically generates products based on the collected information. The product generation system collects information about features and services that users "want more of" from online review sites, social networking sites, survey results, and other sources. This information collection is performed by a generation AI. For example, a car review site might collect comments such as "I want the same model of car at a slightly lower price" or a television review might collect comments such as "I wish the screen was a little bigger." The generation AI then analyzes the collected information to identify user needs. For example, if there are many comments about cars, the AI ​​can identify the features and services that most users are particularly looking for. This allows the AI ​​to understand user needs. The generation AI then automatically generates new products based on the identified needs. For example, a new car model might be designed taking into account the user's desired price range and features. For televisions, a new model might be designed that reflects the user's desired screen size and features. Finally, a prototype of the generated product is manufactured and user feedback is collected. Based on this feedback, the product is further improved and the final product is released to market. For example, in a hospital, a system can be developed to shorten the time from prescription to payment and receipt of medication. This will reduce patient waiting times and improve the quality of medical services. This will enable the product generation system to quickly develop and provide products that match user needs. For example, products that reflect the functions and services desired by users can be quickly brought to market. Furthermore, by improving products based on user feedback, it will be possible to provide higher quality products.

[0068] A product creation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a feedback collection unit. The collection unit collects data from the Internet. Examples of the data on the Internet include, but are not limited to, text data, image data, and audio data. For example, the collection unit collects data from review sites using web scraping technology. The collection unit can also acquire data from social networking sites using an API. The collection unit can also collect results of online surveys. For example, the collection unit collects user comments from a specific review site and saves them as text data. The collection unit uses the social networking site's API to collect user posts and save them in a database. The collection unit automatically collects results of online surveys and saves them for analysis. The analysis unit analyzes the data collected by the collection unit to identify user needs. The analysis is performed, for example, using natural language processing technology, but is not limited to, examples. For example, the analysis unit analyzes text data using morphological analysis to extract frequently occurring keywords. The analysis unit can also analyze sentence structure using grammatical analysis to identify user intent. The analysis unit can also analyze the meaning of text data using semantic analysis to identify user needs. For example, the analysis unit can extract frequently occurring keywords from user comments using morphological analysis. The analysis unit can analyze the sentence structure of the user comments using grammatical analysis to identify the user's intention. The analysis unit can analyze the meaning of the user comments using semantic analysis to identify the user's needs. The generation unit generates a new product based on the needs identified by the analysis unit. Generation can be performed using, for example, machine learning technology, but is not limited to such examples. For example, the generation unit can design a new product using a neural network. The generation unit can also determine product specifications using a decision tree. The generation unit can also optimally design a product using a support vector machine. For example, the generation unit can design a new car model based on user needs using a neural network. The generation unit can determine television specifications based on user needs using a decision tree.The generation unit uses a support vector machine to optimally design a medical system based on user needs. The feedback collection unit collects user feedback on the product prototype generated by the generation unit. Feedback collection can be performed, for example, using a questionnaire survey, but is not limited to such examples. For example, the feedback collection unit conducts an online survey to collect user feedback. The feedback collection unit can also collect user reviews. The feedback collection unit can also conduct interviews to collect user feedback. For example, the feedback collection unit collects user opinions on the generated product prototype through an online survey. The feedback collection unit collects user ratings on the generated product from a user review site. The feedback collection unit collects detailed user feedback on the generated product through interviews. This allows the product generation system according to the embodiment to automatically generate a new product based on user needs and improve it by collecting feedback. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit may input user feedback data into the generation AI and cause the generation AI to analyze the feedback.

[0069] The collection unit can collect data from review sites, social networking sites, and survey results. Examples of review sites include, but are not limited to, Yahoo! Shopping, Amazon, etc. Examples of social networking sites include, but are not limited to, Twitter and Facebook. Examples of survey results include, but are not limited to, online surveys and paper-based surveys. For example, the collection unit collects user comments from review sites. The collection unit can also collect user posts from social networking sites. The collection unit can also collect survey results. For example, the collection unit collects user comments from Amazon's review site and saves them as text data. The collection unit uses Twitter's API to collect user posts and save them in a database. The collection unit automatically collects online survey results and saves them for analysis. This allows user needs to be collected from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data collected from review sites into a generation AI and have the generation AI analyze the data.

[0070] The analysis unit can analyze the collected data using natural language processing to identify user needs. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can analyze text data using morphological analysis to extract frequently occurring keywords. The analysis unit can also analyze sentence structure using grammatical analysis to identify user intention. The analysis unit can also analyze the meaning of text data using semantic analysis to identify user needs. For example, the analysis unit can use morphological analysis to extract frequently occurring keywords from user comments. The analysis unit can use grammatical analysis to analyze the sentence structure of the user comments to identify the user intention. The analysis unit can use semantic analysis to analyze the meaning of the user comments to identify user needs. In this way, natural language processing can be used to accurately identify user needs. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI identify user needs.

[0071] The generation unit can generate a new product using machine learning based on the identified needs. Examples of machine learning include, but are not limited to, neural networks, decision trees, and support vector machines. The generation unit can, for example, use a neural network to design a new product. The generation unit can also use a decision tree to determine product specifications. The generation unit can also use a support vector machine to optimally design a product. For example, the generation unit can use a neural network to design a new car model based on user needs. The generation unit can use a decision tree to determine television specifications based on user needs. The generation unit can also use a support vector machine to optimally design a medical system based on user needs. In this way, by using machine learning, it is possible to automatically generate a product that meets user needs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the identified needs into a generation AI and cause the generation AI to generate a new product.

[0072] The feedback collection unit can collect user feedback on the generated product prototype. Prototype creation includes, but is not limited to, 3D printing, CAD design, and the like. For example, the feedback collection unit can collect user feedback by conducting an online survey. The feedback collection unit can also collect user reviews. The feedback collection unit can also collect user feedback by conducting interviews. For example, the feedback collection unit can collect user opinions on the generated product prototype through an online survey. The feedback collection unit can collect user ratings on the generated product from a user review site. The feedback collection unit can collect detailed user feedback on the generated product through interviews. Collecting user feedback can be useful for improving the product. Some or all of the above-described processing in the feedback collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback collection unit can input user feedback data into a generation AI and cause the generation AI to analyze the feedback.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is expressing positive emotions, the collection unit collects data from review sites and social networking sites at that timing. When the user is expressing negative emotions, the collection unit can also prioritize collecting survey results at that timing. When the user is expressing neutral emotions, the collection unit can also collect data in a balanced manner from a wide range of data sources at that timing. For example, when the user is expressing positive emotions, the collection unit prioritizes collecting data from review sites. When the user is expressing negative emotions, the collection unit prioritizes collecting survey results. When the user is expressing neutral emotions, the collection unit collects data in a balanced manner from social networking sites and review sites. This allows data to be collected at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0074] During collection, the collection unit can analyze the user's past posting history and select the optimal collection method. For example, the collection unit prioritizes collecting data from review sites where the user frequently posted in the past. The collection unit can also collect data from social networking sites where the user has left many comments in the past. The collection unit can also collect data from similar surveys based on the results of surveys the user has previously participated in. For example, the collection unit prioritizes collecting data from review sites where the user frequently posted in the past. The collection unit collects data from social networking sites where the user has left many comments in the past. The collection unit collects data from similar surveys based on the results of surveys the user has previously participated in. This allows efficient data collection by selecting the optimal collection method based on the user's past posting history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history data into a generation AI and cause the generation AI to select the optimal collection method.

[0075] The collection unit may perform filtering based on the user's current areas of interest during collection. For example, the collection unit may prioritize collecting reviews related to product categories in which the user is currently interested. The collection unit may also collect social media posts related to keywords recently searched by the user. The collection unit may also collect data based on the themes of surveys in which the user recently participated. For example, the collection unit may prioritize collecting reviews related to product categories in which the user is currently interested. The collection unit may collect social media posts related to keywords recently searched by the user. The collection unit may collect data based on the themes of surveys in which the user recently participated. This allows highly relevant data to be collected by filtering the data based on the user's current areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's current areas of interest data into a generation AI and cause the generation AI to filter the data.

[0076] The collection unit can select an appropriate collection means depending on the user's input method during collection. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. If the user is using text input, the collection unit can also prioritize collection of text data. If the user is posting images, the collection unit can also prioritize collection of image data. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. If the user is using text input, the collection unit prioritizes collection of text data. If the user is posting images, the collection unit prioritizes collection of image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is expressing a positive emotion, the collection unit prioritizes collecting data related to that emotion. When the user is expressing a negative emotion, the collection unit can also prioritize collecting data related to that emotion. When the user is expressing a neutral emotion, the collection unit can also collect data in a balanced manner. For example, when the user is expressing a positive emotion, the collection unit prioritizes collecting data related to that emotion. When the user is expressing a negative emotion, the collection unit prioritizes collecting data related to that emotion. When the user is expressing a neutral emotion, the collection unit collects data in a balanced manner. In this way, by determining the priority of data based on the user's emotions, important data can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0078] During collection, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user is in a specific region, the collection unit prioritizes collecting reviews related to that region. If the user is in a specific city, the collection unit can also collect social media posts related to that city. If the user is in a specific country, the collection unit can also collect survey results related to that country. For example, if the user is in a specific region, the collection unit prioritizes collecting reviews related to that region. If the user is in a specific city, the collection unit collects social media posts related to that city. If the user is in a specific country, the collection unit collects survey results related to that country. This makes it possible to understand regionally specific needs by preferentially collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information data into a generation AI and cause the generation AI to collect highly relevant data.

[0079] During collection, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit preferentially collects data from social media sites to which the user frequently posts. The collection unit can also collect data from social media sites to which the user leaves many comments. The collection unit can also collect data based on posts to which the user receives many likes. For example, the collection unit preferentially collects data from social media sites to which the user frequently posts. The collection unit collects data from social media sites to which the user leaves many comments. The collection unit collects data based on posts to which the user receives many likes. In this way, by collecting related data based on the user's social media activity, it is possible to collect data that reflects the user's interests. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0080] The collection unit can adjust the collection method during collection by reflecting the user's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize use of collection methods that the user has previously preferred. The collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit prioritizes use of collection methods that the user has previously preferred. The collection unit avoids collection methods that the user has previously expressed dissatisfaction with. In this way, by customizing the collection method based on the user's past feedback, data can be collected in a manner that is optimal for the user. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is expressing positive emotions, the analysis unit presents the analysis results in a bright tone. If the user is expressing negative emotions, the analysis unit can also present the analysis results in a cautious manner. If the user is expressing neutral emotions, the analysis unit can also present the analysis results objectively. For example, if the user is expressing positive emotions, the analysis unit presents the analysis results in a bright tone. If the user is expressing negative emotions, the analysis unit presents the analysis results in a cautious manner. If the user is expressing neutral emotions, the analysis unit presents the analysis results objectively. This allows the analysis results to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0082] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit performs a simplified analysis on data of low importance. The analysis unit performs an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to set the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a sentiment analysis algorithm to product reviews. The analysis unit can also apply a topic modeling algorithm to social media posts. The analysis unit can also apply a statistical analysis algorithm to survey results. For example, the analysis unit applies a sentiment analysis algorithm to product reviews. The analysis unit applies a topic modeling algorithm to social media posts. The analysis unit applies a statistical analysis algorithm to survey results. In this way, by applying an appropriate analysis algorithm depending on the data category, highly accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also prioritize the use of analysis methods that the user has previously preferred. The analysis unit can also avoid analysis methods that the user has previously expressed dissatisfaction with. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit prioritizes the use of analysis methods that the user has previously preferred. The analysis unit avoids analysis methods that the user has previously expressed dissatisfaction with. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit provides detailed analysis results when the user is expressing positive emotions. The analysis unit can also provide concise analysis results when the user is expressing negative emotions. The analysis unit can also provide analysis results of appropriate length when the user is expressing neutral emotions. For example, the analysis unit provides detailed analysis results when the user is expressing positive emotions. The analysis unit provides concise analysis results when the user is expressing negative emotions. The analysis unit provides analysis results of appropriate length when the user is expressing neutral emotions. In this way, by adjusting the length of the analysis based on the user's emotions, it is possible to provide analysis results of an appropriate length for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0086] During analysis, the analysis unit can set analysis priorities based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also moderately analyze data that was submitted at a medium time. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit postpones analysis of older data. The analysis unit moderately analyzes data that was submitted at a medium time. In this way, by determining the analysis priorities based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI and cause the generation AI to set the analysis priorities.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. The analysis unit can also moderately analyze data with medium relevance. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit postpones analysis of data with low relevance. The analysis unit moderately analyzes data with medium relevance. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0088] During analysis, the analysis unit can set the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also avoid technical terminology for users with low levels of expertise. The analysis unit can also use technical terminology moderately for users with intermediate levels of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit avoids technical terminology for users with low levels of expertise. The analysis unit uses technical terminology moderately for users with intermediate levels of expertise. This allows the use of technical terminology in the analysis to be adjusted according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0089] The generation unit can estimate the user's emotions and adjust the specifications of the product to be generated based on the estimated user emotions. For example, the generation unit can set the specifications higher when the user is expressing positive emotions. The generation unit can also set the specifications lower when the user is expressing negative emotions. The generation unit can also set balanced specifications when the user is expressing neutral emotions. For example, the generation unit can set the specifications higher when the user is expressing positive emotions. The generation unit can set the specifications lower when the user is expressing negative emotions. The generation unit can set balanced specifications when the user is expressing neutral emotions. This allows the product to be generated according to the user's expectations by adjusting the product specifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the product specifications.

[0090] The generation unit can set the level of detail of the generation based on the importance of the identified needs during generation. For example, the generation unit sets detailed specifications for needs of high importance. The generation unit can also set simple specifications for needs of low importance. The generation unit can also set specifications with an appropriate level of detail for needs of medium importance. For example, the generation unit sets detailed specifications for needs of high importance. The generation unit sets simple specifications for needs of low importance. The generation unit sets specifications with an appropriate level of detail for needs of medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the identified needs, a detailed product can be generated for important needs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the identified needs to the generation AI and cause the generation AI to set the level of detail of the generation.

[0091] During generation, the generation unit can apply different generation algorithms depending on the category of the need. For example, the generation unit applies a vehicle design algorithm to a need related to a car. The generation unit can also apply a home appliance design algorithm to a need related to a television. The generation unit can also apply a medical system design algorithm to a need related to medical services. For example, the generation unit applies a vehicle design algorithm to a need related to a car. The generation unit applies a home appliance design algorithm to a need related to a television. The generation unit applies a medical system design algorithm to a need related to medical services. In this way, by applying an appropriate generation algorithm depending on the category of the need, a highly accurate product can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input need category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0092] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit can also prioritize the use of generation methods that the user has previously preferred. The generation unit can also avoid generation methods that the user has previously expressed dissatisfaction with. For example, the generation unit adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit prioritizes the use of generation methods that the user has previously preferred. The generation unit avoids generation methods that the user has previously expressed dissatisfaction with. This makes it possible to improve the accuracy of generation by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0093] The generation unit can estimate the user's emotions and determine the priority of products to be generated based on the estimated user emotions. For example, when the user is expressing a positive emotion, the generation unit prioritizes generating products related to that emotion. When the user is expressing a negative emotion, the generation unit can also prioritize generating products related to that emotion. When the user is expressing a neutral emotion, the generation unit can also generate a balanced range of products. For example, when the user is expressing a positive emotion, the generation unit prioritizes generating products related to that emotion. When the user is expressing a negative emotion, the generation unit prioritizes generating products related to that emotion. When the user is expressing a neutral emotion, the generation unit generates a balanced range of products. In this way, by determining the priority of products based on the user's emotions, it is possible to prioritize generating products that correspond to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine product priorities.

[0094] The generation unit can set a generation priority based on the submission time of the needs at the time of generation. For example, the generation unit generates the latest needs with priority. The generation unit can also generate older needs later. The generation unit can also generate needs with a medium submission time in a moderate manner. For example, the generation unit generates the latest needs with priority. The generation unit generates older needs later. The generation unit generates needs with a medium submission time in a moderate manner. In this way, by determining the generation priority based on the submission time of the needs, the latest needs can be generated with priority. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the submission time of the needs into the generation AI and cause the generation AI to set the generation priority.

[0095] The generation unit can adjust the order of generation based on the relevance of needs during generation. For example, the generation unit prioritizes generating needs with high relevance. The generation unit can also postpone generating needs with low relevance. The generation unit can also moderately generate needs with medium relevance. For example, the generation unit prioritizes generating needs with high relevance. The generation unit postpones generating needs with low relevance. The generation unit moderately generates needs with medium relevance. In this way, by adjusting the order of generation based on the relevance of needs, highly relevant needs can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input needs relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0096] During generation, the generation unit can set the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit uses a lot of technical terminology for users with high expertise. The generation unit can also avoid technical terminology for users with low expertise. The generation unit can also use technical terminology moderately for users with intermediate expertise. For example, the generation unit uses a lot of technical terminology for users with high expertise. The generation unit avoids technical terminology for users with low expertise. The generation unit uses technical terminology moderately for users with intermediate expertise. This makes it possible to provide product specifications that are easy for users to understand by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's expertise level data into the generation AI and cause the generation AI to use technical terminology.

[0097] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, the feedback collection unit requests detailed feedback when the user is expressing positive emotions. The feedback collection unit can also request concise feedback when the user is expressing negative emotions. The feedback collection unit can also request balanced feedback when the user is expressing neutral emotions. For example, the feedback collection unit requests detailed feedback when the user is expressing positive emotions. The feedback collection unit requests concise feedback when the user is expressing negative emotions. The feedback collection unit requests balanced feedback when the user is expressing neutral emotions. In this way, by adjusting the feedback collection method based on the user's emotions, it is possible to collect feedback appropriate for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit can be performed, for example, using AI or without AI. For example, the feedback collection unit can input user emotional data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0098] When collecting feedback, the feedback collection unit can select an appropriate collection method based on the user's past feedback history. For example, if the user provided detailed feedback in the past, the feedback collection unit requests similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit can also request similar brief feedback. The feedback collection unit can also select an optimal collection method based on the content of feedback provided by the user in the past. For example, if the user provided detailed feedback in the past, the feedback collection unit requests similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit requests similar brief feedback. The feedback collection unit selects an optimal collection method based on the content of feedback provided by the user in the past. In this way, by selecting an optimal collection method based on the user's past feedback history, feedback can be collected efficiently. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history data to the generation AI and cause the generation AI to select an optimal collection method.

[0099] The feedback collection unit can filter the feedback based on the user's current areas of interest when collecting the feedback. For example, the feedback collection unit prioritizes collecting feedback related to product categories in which the user is currently interested. The feedback collection unit can also collect feedback related to keywords recently searched by the user. The feedback collection unit can also collect feedback based on themes of surveys in which the user has recently participated. For example, the feedback collection unit prioritizes collecting feedback related to product categories in which the user is currently interested. The feedback collection unit collects feedback related to keywords recently searched by the user. The feedback collection unit collects feedback based on themes of surveys in which the user has recently participated. In this way, by filtering the feedback based on the user's current areas of interest, it is possible to collect highly relevant feedback. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input data on the user's current areas of interest to a generation AI and cause the generation AI to filter the feedback.

[0100] When collecting feedback, the feedback collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the feedback collection unit prioritizes collecting voice feedback. When the user uses text input, the feedback collection unit can also prioritize collecting text feedback. When the user posts images, the feedback collection unit can also prioritize collecting image feedback. For example, when the user uses voice input, the feedback collection unit prioritizes collecting voice feedback. When the user uses text input, the feedback collection unit prioritizes collecting text feedback. When the user posts images, the feedback collection unit prioritizes collecting image feedback. This allows efficient collection of feedback by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input user input method data to a generation AI and cause the generation AI to select the optimal collection means.

[0101] The feedback collection unit can estimate the user's emotions and determine the priority of feedback to be collected based on the estimated user emotions. For example, when the user is expressing a positive emotion, the feedback collection unit prioritizes collecting feedback related to the emotion. When the user is expressing a negative emotion, the feedback collection unit can also prioritize collecting feedback related to the emotion. When the user is expressing a neutral emotion, the feedback collection unit can also collect feedback in a balanced manner. For example, when the user is expressing a positive emotion, the feedback collection unit prioritizes collecting feedback related to the emotion. When the user is expressing a negative emotion, the feedback collection unit prioritizes collecting feedback related to the emotion. When the user is expressing a neutral emotion, the feedback collection unit collects feedback in a balanced manner. In this way, by determining the priority of feedback based on the user's emotions, it is possible to prioritize and collect important feedback. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input user emotion data to the generation AI and have the generation AI determine the priority of the feedback.

[0102] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback based on the user's geographical location information. For example, when the user is in a specific region, the feedback collection unit prioritizes collecting feedback related to the region. When the user is in a specific city, the feedback collection unit can also collect feedback related to the city. When the user is in a specific country, the feedback collection unit can also collect feedback related to the country. For example, when the user is in a specific region, the feedback collection unit prioritizes collecting feedback related to the region. When the user is in a specific city, the feedback collection unit collects feedback related to the city. When the user is in a specific country, the feedback collection unit collects feedback related to the country. This makes it possible to understand region-specific needs by preferentially collecting highly relevant feedback based on the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit may input the user's geographical location information data to a generation AI and cause the generation AI to collect highly relevant feedback.

[0103] The feedback collection unit can analyze the user's social media activity and collect relevant feedback when collecting feedback. For example, the feedback collection unit preferentially collects feedback from social media platforms to which the user frequently posts. The feedback collection unit can also collect feedback from social media platforms to which the user leaves many comments. The feedback collection unit can also collect feedback based on posts to which the user receives many likes. For example, the feedback collection unit preferentially collects feedback from social media platforms to which the user frequently posts. The feedback collection unit collects feedback from social media platforms to which the user leaves many comments. The feedback collection unit collects feedback based on posts to which the user receives many likes. In this way, by collecting relevant feedback based on the user's social media activity, it is possible to collect feedback that reflects the user's interests. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect relevant feedback.

[0104] When collecting feedback, the feedback collection unit can adjust the collection method by reflecting the user's past feedback. The feedback collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The feedback collection unit can also prioritize use of collection methods that the user has previously preferred. The feedback collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. For example, the feedback collection unit adjusts the collection method based on feedback provided by the user in the past. The feedback collection unit prioritizes use of collection methods that the user has previously preferred. The feedback collection unit avoids collection methods that the user has previously expressed dissatisfaction with. In this way, by customizing the collection method based on the user's past feedback, it is possible to collect feedback in a manner that is optimal for the user. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the collection method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and feedback collection unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on the Internet via the control unit 46A of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify user needs. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a new product based on the identified needs. The feedback collection unit is realized, for example, by the control unit 46A of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and feedback collection unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on the Internet via the control unit 46A of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify user needs. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a new product based on the identified needs. The feedback collection unit is realized, for example, by the control unit 46A of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and feedback collection unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data on the Internet by the control unit 46A of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify user needs. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a new product based on the identified needs. The feedback collection unit collects user feedback by the control unit 46A of the headset type terminal 314, and the feedback is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and feedback collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the Internet by the control unit 46A of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify user needs. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a new product based on the identified needs. The feedback collection unit is realized, for example, by the control unit 46A of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if a user is expressing positive emotions, the collection unit can collect data from review sites and social media at that timing. If a user is expressing negative emotions, the collection unit can also prioritize collecting survey results at that timing. If a user is expressing neutral emotions, the collection unit can also collect data in a balanced manner from a wide range of data sources at that timing. This allows data to be collected at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0107] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is expressing positive emotions, the analysis results can be presented in a bright tone. If the user is expressing negative emotions, the analysis unit can also present the analysis results cautiously. If the user is expressing neutral emotions, the analysis unit can also present the analysis results objectively. This allows the analysis results to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0108] The generation unit can estimate the user's emotions and adjust the specifications of the product to be generated based on the estimated user emotions. For example, if the user is expressing positive emotions, the generation unit can set the specifications higher. If the user is expressing negative emotions, the generation unit can also set the specifications lower. If the user is expressing neutral emotions, the generation unit can also set balanced specifications. This allows the generation of a product that meets the user's expectations by adjusting the product specifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the product specifications.

[0109] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is expressing positive emotions, the feedback collection unit can request detailed feedback. If the user is expressing negative emotions, the feedback collection unit can also request concise feedback. If the user is expressing neutral emotions, the feedback collection unit can also request balanced feedback. This allows appropriate feedback to be collected by adjusting the feedback collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit can be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0110] When collecting data, the collection unit can analyze the user's past posting history and select the optimal collection method. For example, the collection unit can prioritize collecting data from review sites where the user frequently posted in the past. The collection unit can also collect data from social networking sites where the user has left many comments in the past. The collection unit can also collect data from similar surveys based on the results of surveys the user has previously participated in. This allows for efficient data collection by selecting the optimal collection method based on the user's past posting history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past posting history data into a generation AI and have the generation AI select the optimal collection method.

[0111] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, a detailed analysis is performed on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI set the level of detail of the analysis.

[0112] The generation unit can set the level of detail of the generation based on the importance of the identified needs during generation. For example, detailed specifications are set for needs of high importance. The generation unit can also set simple specifications for needs of low importance. The generation unit can also set specifications with an appropriate level of detail for needs of medium importance. In this way, by adjusting the level of detail of the generation based on the importance of the identified needs, a detailed product can be generated for important needs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the identified needs into the generation AI and cause the generation AI to set the level of detail of the generation.

[0113] When collecting feedback, the feedback collection unit can select an appropriate collection method based on the user's past feedback history. For example, if the user provided detailed feedback in the past, the feedback collection unit can request similar detailed feedback. If the user provided brief feedback in the past, the feedback collection unit can also request similar brief feedback. The feedback collection unit can also select an optimal collection method based on the content of feedback provided by the user in the past. This allows feedback to be collected efficiently by selecting an optimal collection method based on the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history data into the generation AI and cause the generation AI to select an optimal collection method.

[0114] The collection unit may perform filtering based on the user's current areas of interest during collection. For example, reviews related to product categories in which the user is currently interested may be preferentially collected. The collection unit may also collect social media posts related to keywords recently searched by the user. The collection unit may also collect data based on the themes of surveys in which the user recently participated. This allows highly relevant data to be collected by filtering the data based on the user's current areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's current areas of interest data into a generation AI and have the generation AI perform data filtering.

[0115] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a sentiment analysis algorithm can be applied to product reviews. The analysis unit can also apply a topic modeling algorithm to social media posts. The analysis unit can also apply a statistical analysis algorithm to survey results. This makes it possible to obtain highly accurate analysis results by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects data from the Internet. This data includes text data, image data, and audio data. The collection unit can use web scraping technology to collect data from review sites and APIs to obtain data from social media. It can also collect the results of online surveys. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the user's needs. The analysis is carried out using natural language processing techniques, such as morphological analysis, grammatical analysis, and semantic analysis, to analyze the text data and identify frequently occurring keywords and the user's intent. Step 3: The generation unit generates new products based on the needs identified by the analysis unit. This is done using machine learning techniques, such as neural networks, decision trees, and support vector machines, to determine the product design and specifications. Step 4: The feedback collection unit collects user feedback on the product prototype generated by the generation unit. The feedback collection is carried out through online surveys, user reviews, interviews, etc.

[0118] 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.

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

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

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

[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 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.

[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 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 collection unit that collects data on the Internet; an analysis unit that analyzes the data collected by the collection unit and identifies user needs; a generator for generating new products based on the needs identified by the analyzer; a feedback collection unit that collects feedback on the product prototype generated by the generation unit. A system characterized by:

2. The collecting unit Collect data from review sites, social media, and survey results 2. The system of claim 1.

3. The analysis unit Analyze collected data using natural language processing to identify user needs 2. The system of claim 1.

4. The generation unit Use machine learning to generate new products based on identified needs 2. The system of claim 1.

5. The feedback collection unit: Gather user feedback on the resulting product prototype 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of data collection according to the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting data, analyze the user's past posting history and select the appropriate collection method.

2. The system of claim 1.

8. The collecting unit At the time of collection, filtering is performed based on the user's current interests 2. The system of claim 1.

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