System

Abstract

An object of a system according to an embodiment is to effectively collect and analyze word-of-mouth data and generate and provide an evaluation of a product or a service.SOLUTION: A system according to an embodiment includes a word-of-mouth data collector, a word-of-mouth data analyzer, an evaluation generator, and a provider. The word-of-mouth data collection unit collects word-of-mouth data. The word-of-mouth data analyzer analyzes the word-of-mouth data collected by the word-of-mouth data collector. The evaluation generator generates an evaluation of a product or a service on the basis of the word-of-mouth data analyzed by the word-of-mouth data analyzer. The providing unit provides the user with the evaluation generated by the evaluation generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively collect and analyze word-of-mouth data and generate evaluations of products and services.

[0005] The system according to the embodiment aims to effectively collect and analyze word-of-mouth data and generate and provide evaluations of products and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a word-of-mouth data collection unit, a word-of-mouth data analysis unit, a rating generation unit, and a providing unit. The word-of-mouth data collection unit collects word-of-mouth data. The word-of-mouth data analysis unit analyzes the word-of-mouth data collected by the word-of-mouth data collection unit. The rating generation unit generates ratings of products and services based on the word-of-mouth data analyzed by the word-of-mouth data analysis unit. The providing unit provides the ratings generated by the rating generation unit to users. [Effects of the Invention]

[0007] The system according to the embodiment can effectively collect and analyze word-of-mouth data and generate and provide evaluations of products and services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The word-of-mouth analysis system according to an embodiment of the present invention collects word-of-mouth data, analyzes it using a generation AI, evaluates products and services, and provides the results to users. This allows users to select products and services based on highly reliable information.

[0029] A word-of-mouth analysis system according to an embodiment includes a word-of-mouth data collection unit, a word-of-mouth data analysis unit, a rating generation unit, and a provision unit. The word-of-mouth data collection unit collects word-of-mouth data. For example, it collects text reviews, rating scores, images, videos, and the like posted by users on an e-commerce site. The word-of-mouth data collection unit can also collect text comments such as "This product is very easy to use" when users post reviews of purchased products. The word-of-mouth data collection unit can also use a generation AI to estimate user emotions in real time during word-of-mouth data collection and provide an interface for eliciting positive emotions. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate emotions in real time. The word-of-mouth data analysis unit analyzes the word-of-mouth data collected by the word-of-mouth data collection unit. For example, the generation AI can understand the content of the reviews using text analysis technology and classify them into positive and negative reviews. The generation AI can also perform sentiment analysis of the word-of-mouth data and provide detailed evaluations based on the intensity and type of sentiment. For example, a review that states "It's very easy to use" is classified as a positive review, and a review that states "It's fragile" is classified as a negative review. The evaluation generation unit generates evaluations of products and services based on the review data analyzed by the review data analysis unit. For example, the generation AI generates an overall evaluation score and ranking based on the analysis results of the review data. The generation AI can also provide evaluations based on user emotions based on the results of sentiment analysis of the review data. For example, reviews with strong positive sentiments are displayed as high ratings. The provision unit provides the evaluations generated by the evaluation generation unit to the user. For example, when searching for products on an e-commerce site, evaluation scores and rankings are displayed. The generation AI can also recommend products that are optimal for the user based on the user's past purchase history and browsing history. For example, it recommends products similar to products purchased in the past or products that other users have given high ratings. As a result, the review analysis system according to the embodiment allows users to select products and services based on reliable information.For example, users can choose products based on reviews from other users, making purchasing decisions based on reliable information. Product recommendations by generative AI also make it easier for users to find the best products for them.

[0030] The review data collection unit can analyze a user's voice input and automatically convert it into text data. For example, when a user posts a review by voice, the generation AI analyzes the voice data and automatically converts it into text data. For example, when a user posts a review by voice, "This product is very easy to use," it is saved as text data. The review data collection unit also uses voice recognition technology to build a system that converts a user's voice input into text data in real time. For example, when a user voice inputs into a smartphone, the content is instantly displayed as text data. The review data collection unit also adds a function that allows the generation AI to analyze voice data and automatically convert it into text data. For example, when a user posts a review by voice, the content is saved as text data and can be viewed by other users. This makes it easier to collect review data by automatically converting a user's voice input into text data.

[0031] The review data collection unit can analyze a user's past posting history and develop an algorithm that prioritizes collecting reviews on specific topics. For example, the generation AI in the review data collection unit analyzes a user's past posting history and develops an algorithm that prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also analyzes a user's past posting history and builds a system that prioritizes collecting reviews on specific topics. For example, it prioritizes displaying reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also develops an algorithm that analyzes a user's past posting history and prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. This allows for the collection of more relevant review data by analyzing a user's past posting history and prioritizes collecting reviews on specific topics.

[0032] The review data collection unit can analyze the content of images or videos posted by users and combine it with text data to perform an evaluation. For example, the review data collection unit uses a generation AI to analyze the content of images and videos posted by users and combine it with text data to perform an evaluation. For example, the unit analyzes videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses image recognition technology to build a system that analyzes the content of images posted by users and combines it with text data to perform an evaluation. For example, the unit analyzes images showing the appearance and condition of a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses video analysis technology to build a system that analyzes the content of videos posted by users and combines it with text data to perform an evaluation. For example, the unit analyzes videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. This enables more detailed evaluations by analyzing the content of images and videos posted by users and combining it with text data to perform an evaluation.

[0033] The review data collection unit can automatically translate reviews posted in different languages, enabling evaluation from a global perspective. The review data collection unit, for example, builds a system in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Japanese are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also uses machine translation technology to develop a system that translates reviews posted in different languages ​​in real time. For example, reviews posted in French are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also builds a system in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Chinese are translated into English and evaluations are provided to English-speaking users as well. This makes it possible to automatically translate reviews posted in different languages, enabling evaluation from a global perspective.

[0034] The review data analysis unit takes into account the user's background information (age, gender, region, etc.) when analyzing review data, allowing it to provide more personalized evaluations. For example, the review data analysis unit takes into account the user's background information (age, gender, region, etc.) when analyzing review data, building a system that provides more personalized evaluations. For example, it prioritizes displaying reviews from users in the same age group. The review data analysis unit also develops a system in which a generation AI analyzes the user's background information and provides personalized evaluations based on that information. For example, it prioritizes displaying reviews from users in the same region. The review data analysis unit also builds a system that takes into account the user's background information when analyzing review data, allowing it to provide more personalized evaluations. For example, it prioritizes displaying reviews from users of the same gender. In this way, by taking the user's background information into consideration, it is possible to provide more personalized evaluations.

[0035] The review data analysis unit can add a function that understands the context of the review data and subdivides the ratings based on specific keywords and phrases. For example, the review data analysis unit adds a function that allows the generation AI to understand the context of the review data and subdivide the ratings based on specific keywords and phrases. For example, it may classify ratings based on keywords such as "easy to use" and "easy to break." The review data analysis unit also uses context analysis technology to build a system that subdivides ratings based on specific keywords and phrases in the review data. For example, it may classify ratings based on keywords such as "good design" and "expensive." The review data analysis unit also adds a function that allows the generation AI to understand the context of the review data and subdivide the ratings based on specific keywords and phrases. For example, it may classify ratings based on keywords such as "easy to use" and "easy to break." This enables more detailed evaluations by understanding the context of the review data and subdividing ratings based on specific keywords and phrases.

[0036] The review data analysis unit can convert the results of review data analysis into visual notes or mind maps to make it easier to understand visually. For example, the review data analysis unit converts the results of review data analysis into visual notes and builds a system that makes it easier to understand visually. For example, important points are shown using diagrams and icons. The review data analysis unit also develops a system in which the generative AI converts the results of review data analysis into mind map format and visually organizes related keywords and concepts. For example, it makes it possible to understand the overall picture of an idea at a glance. The review data analysis unit also converts the results of review data analysis into visual notes or mind maps and builds a system that makes it easier to understand visually. For example, important points are shown using diagrams and icons. In this way, converting the results of review data analysis into visual notes or mind maps makes it easier to understand visually.

[0037] The word-of-mouth data analysis unit analyzes word-of-mouth data from different industries and fields, thereby promoting crossover innovation. For example, the generative AI in the word-of-mouth data analysis unit analyzes word-of-mouth data from different industries and fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different industries to develop a system that proposes new products and services. For example, it generates ideas that apply medical technology to everyday life. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. This allows crossover innovation to be promoted by analyzing word-of-mouth data from different industries and fields.

[0038] The rating generation unit takes into account a user's past purchase history and browsing history when rating products and services, thereby enabling the provision of more personalized ratings. The rating generation unit, for example, builds a system that takes into account a user's past purchase history and browsing history when rating products and services, thereby providing more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. The rating generation unit also develops a system in which a generation AI analyzes a user's past purchase history and browsing history, and provides personalized ratings based on that information. For example, products related to products previously viewed are displayed as highly rated. The rating generation unit also builds a system that takes into account a user's past purchase history and browsing history when rating products and services, thereby providing more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. In this way, more personalized ratings can be provided by taking into account a user's past purchase history and browsing history.

[0039] The rating generation unit can add a function to update rating scores and rankings in real time based on the analysis results of review data. For example, the rating generation unit builds a system that adds a function to update rating scores and rankings in real time based on the analysis results of review data by the generation AI. For example, the rating scores and rankings are updated every time a new review is posted. The rating generation unit also develops a system that analyzes review data in real time and updates rating scores and rankings based on the results. For example, the rating scores increase as the number of positive reviews increases. The rating generation unit also builds a system that adds a function to update rating scores and rankings in real time based on the analysis results of review data by the generation AI. For example, the rating scores and rankings are updated every time a new review is posted. This makes it possible to provide the latest rating information by updating rating scores and rankings in real time based on the analysis results of review data.

[0040] The evaluation generation unit can automatically translate evaluation results of products and services into different languages ​​and provide evaluations from an international perspective. For example, the evaluation generation unit builds a system in which a generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, it translates evaluation results in Japanese into English and provides evaluations to English-speaking users as well. The evaluation generation unit also develops a system that uses machine translation technology to translate evaluation results of products and services in real time. For example, it translates evaluation results in French into English and provides evaluations to English-speaking users as well. The evaluation generation unit also builds a system in which a generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, it translates evaluation results in Chinese into English and provides evaluations to English-speaking users as well. This makes it possible to provide evaluations from an international perspective by automatically translating evaluation results into different languages.

[0041] The evaluation generation unit can convert the evaluation results into visual notes or mind maps to make them easier to understand visually. For example, the evaluation generation unit builds a system in which a generation AI converts the evaluation results of products or services into visual notes to make them easier to understand visually. For example, it shows important points with diagrams and icons. The evaluation generation unit also develops a system in which the evaluation results are converted into mind map format to visually organize related keywords and concepts. For example, it makes it possible to understand the overall picture of an idea at a glance. The evaluation generation unit also builds a system in which a generation AI converts the evaluation results of products or services into visual notes or mind maps to make them easier to understand visually. For example, it shows important points with diagrams and icons. By converting the evaluation results into visual notes or mind maps, it makes them easier to understand visually.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The review data collection unit can analyze a user's voice input and automatically convert it into text data. For example, when a user posts a review by voice, the generation AI analyzes the voice data and automatically converts it into text data. For example, if a user posts "This product is very easy to use" by voice, it is saved as text data. The review data collection unit also uses voice recognition technology to build a system that converts a user's voice input into text data in real time. For example, when a user voice inputs into a smartphone, the content is instantly displayed as text data. The review data collection unit also adds a function that enables the generation AI to analyze voice data and automatically convert it into text data. For example, when a user posts a review by voice, the content is saved as text data and can be viewed by other users. This makes it easier to collect review data by automatically converting a user's voice input into text data.

[0044] The review data collection unit can analyze a user's past posting history and develop an algorithm that prioritizes collecting reviews on specific topics. For example, the generation AI analyzes a user's past posting history and develops an algorithm that prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also analyzes a user's past posting history and builds a system that prioritizes collecting reviews on specific topics. For example, it prioritizes displaying reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also analyzes a user's past posting history and develops an algorithm that prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. In this way, by analyzing a user's past posting history and priority collecting reviews on specific topics, more relevant review data can be collected.

[0045] The review data collection unit can analyze the content of images or videos posted by users and combine it with text data to perform an evaluation. For example, a generation AI can analyze the content of images and videos posted by users and combine it with text data to perform an evaluation. For example, it can analyze videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses image recognition technology to build a system that analyzes the content of images posted by users and combines it with text data to perform an evaluation. For example, it can analyze images showing the appearance or condition of a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses video analysis technology to build a system that analyzes the content of videos posted by users and combines it with text data to perform an evaluation. For example, it can analyze videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. This allows for more detailed evaluations by analyzing the content of images and videos posted by users and combining it with text data to perform an evaluation.

[0046] The review data collection unit can automatically translate reviews posted in different languages, enabling evaluation from a global perspective. For example, a system is constructed in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Japanese are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also uses machine translation technology to develop a system that translates reviews posted in different languages ​​in real time. For example, reviews posted in French are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also constructs a system in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Chinese are translated into English and evaluations are provided to English-speaking users as well. This makes it possible to automatically translate reviews posted in different languages, enabling evaluation from a global perspective.

[0047] The review data analysis unit takes into account the user's background information (age, gender, region, etc.) when analyzing review data, allowing it to provide more personalized evaluations. For example, a system is constructed that takes into account the user's background information (age, gender, region, etc.) when analyzing review data and provides more personalized evaluations. For example, reviews from users in the same age group are preferentially displayed. The review data analysis unit also develops a system in which the generation AI analyzes the user's background information and provides personalized evaluations based on that information. For example, reviews from users in the same region are preferentially displayed. The review data analysis unit also builds a system that takes into account the user's background information when analyzing review data and provides more personalized evaluations. For example, reviews from users of the same gender are preferentially displayed. In this way, by taking the user's background information into consideration, it is possible to provide more personalized evaluations.

[0048] The review data analysis unit can add a function that understands the context of the review data and subdivides ratings based on specific keywords and phrases. For example, the generation AI can add a function that understands the context of the review data and subdivides ratings based on specific keywords and phrases. For example, it can classify ratings based on keywords such as "easy to use" and "easy to break." The review data analysis unit also uses context analysis technology to build a system that subdivides ratings based on specific keywords and phrases in the review data. For example, it can classify ratings based on keywords such as "good design" and "expensive." The review data analysis unit also adds a function that enables the generation AI to understand the context of the review data and subdivide ratings based on specific keywords and phrases. For example, it can classify ratings based on keywords such as "easy to use" and "easy to break." This enables more detailed evaluations by understanding the context of the review data and subdividing ratings based on specific keywords and phrases.

[0049] The review data analysis department can convert the results of review data analysis into visual notes or mind maps to make them easier to understand visually. For example, we will build a system that converts the results of review data analysis into visual notes to make them easier to understand visually. For example, we will show important points with diagrams and icons. The review data analysis department will also develop a system in which the generative AI converts the results of review data analysis into mind map format to visually organize related keywords and concepts. For example, we will make it possible to understand the overall picture of an idea at a glance. The review data analysis department will also convert the results of review data analysis into visual notes or mind maps to build a system that makes them easier to understand visually. For example, we will show important points with diagrams and icons. In this way, converting the results of review data analysis into visual notes or mind maps will make them easier to understand visually.

[0050] The word-of-mouth data analysis unit analyzes word-of-mouth data from different industries and fields, enabling it to promote crossover innovation. For example, the generation AI analyzes word-of-mouth data from different industries and fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different industries to develop a system that proposes new products and services. For example, it generates ideas that apply medical technology to everyday life. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. In this way, crossover innovation can be promoted by analyzing word-of-mouth data from different industries and fields.

[0051] The rating generation unit takes into account a user's past purchase history and browsing history when rating products and services, allowing it to provide more personalized ratings. For example, a system is constructed that takes into account a user's past purchase history and browsing history when rating products and services, allowing it to provide more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. The rating generation unit also develops a system in which a generation AI analyzes a user's past purchase history and browsing history, and provides personalized ratings based on that information. For example, products related to products previously viewed are displayed as highly rated. The rating generation unit also takes into account a user's past purchase history and browsing history when rating products and services, allowing it to provide more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. In this way, more personalized ratings can be provided by taking into account a user's past purchase history and browsing history.

[0052] The rating generation unit can add a function to update rating scores and rankings in real time based on the analysis results of review data. For example, a system can be built in which the generation AI adds a function to update rating scores and rankings in real time based on the analysis results of review data. For example, the rating scores and rankings are updated every time a new review is posted. The rating generation unit can also develop a system that analyzes review data in real time and updates rating scores and rankings based on the results. For example, the rating scores increase as the number of positive reviews increases. The rating generation unit can also build a system in which the generation AI adds a function to update rating scores and rankings in real time based on the analysis results of review data. For example, the rating scores and rankings are updated every time a new review is posted. This makes it possible to provide the latest rating information by updating rating scores and rankings in real time based on the analysis results of review data.

[0053] The evaluation generation unit can automatically translate evaluation results of products and services into different languages ​​and provide evaluations from an international perspective. For example, a system is built in which the generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, evaluation results in Japanese are translated into English and evaluations are provided to English-speaking users as well. The evaluation generation unit also uses machine translation technology to develop a system that translates evaluation results of products and services in real time. For example, evaluation results in French are translated into English and evaluations are provided to English-speaking users as well. The evaluation generation unit also builds a system in which the generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, evaluation results in Chinese are translated into English and evaluations are provided to English-speaking users as well. This makes it possible to provide evaluations from an international perspective by automatically translating evaluation results into different languages.

[0054] The evaluation generation unit can convert the evaluation results into visual notes or mind maps to make them easier to understand visually. For example, a system can be built in which the generation AI converts the evaluation results of products and services into visual notes to make them easier to understand visually. For example, important points can be shown with diagrams and icons. The evaluation generation unit can also develop a system in which the generation AI converts the evaluation results into mind map format to visually organize related keywords and concepts. For example, to make it possible to understand the overall picture of an idea at a glance. The evaluation generation unit can also develop a system in which the generation AI converts the evaluation results of products and services into visual notes or mind maps to make them easier to understand visually. For example, important points can be shown with diagrams and icons. By converting the evaluation results into visual notes or mind maps, they can be easier to understand visually.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The word-of-mouth data collection unit collects word-of-mouth data. For example, it collects text reviews, rating scores, images, videos, and other data posted by users on e-commerce sites. The word-of-mouth data collection unit can also collect text comments such as "This product is very easy to use" when users post reviews of purchased products. The word-of-mouth data collection unit can also use a generation AI to estimate user emotions in real time as the word-of-mouth data is collected and provide an interface to elicit positive emotions. For example, the generation AI can analyze the user's facial expressions and tone of voice to estimate emotions in real time. Step 2: The review data analysis unit analyzes the review data collected by the review data collection unit. For example, the generation AI uses text analysis technology to understand the content of the reviews and classify them into positive and negative evaluations. The generation AI can also perform sentiment analysis of the review data and provide a detailed evaluation based on the intensity and type of emotion. For example, a review that says "it's very easy to use" is classified as a positive evaluation, and a review that says "it's fragile" is classified as a negative evaluation. Step 3: The evaluation generation unit generates evaluations of products and services based on the review data analyzed by the review data analysis unit. For example, the generation AI generates an overall evaluation score and ranking based on the results of the review data analysis. The generation AI can also provide evaluations based on user emotions based on the results of sentiment analysis of the review data. For example, reviews with strong positive sentiments are displayed as high ratings. Step 4: The providing unit provides the user with the ratings generated by the rating generation unit. For example, when searching for a product on an e-commerce site, rating scores and rankings are displayed. The generation AI can also recommend optimal products to users based on their past purchase and browsing history. For example, it can recommend products similar to products purchased in the past or products that other users have given high ratings to.

[0057] (Example 2) The word-of-mouth analysis system according to an embodiment of the present invention collects word-of-mouth data, analyzes it using a generation AI, evaluates products and services, and provides the results to users. This allows users to select products and services based on highly reliable information.

[0058] A word-of-mouth analysis system according to an embodiment includes a word-of-mouth data collection unit, a word-of-mouth data analysis unit, a rating generation unit, and a provision unit. The word-of-mouth data collection unit collects word-of-mouth data. For example, it collects text reviews, rating scores, images, videos, and the like posted by users on an e-commerce site. The word-of-mouth data collection unit can also collect text comments such as "This product is very easy to use" when users post reviews of purchased products. The word-of-mouth data collection unit can also use a generation AI to estimate user emotions in real time during word-of-mouth data collection and provide an interface for eliciting positive emotions. For example, the generation AI analyzes the user's facial expressions and tone of voice to estimate emotions in real time. The word-of-mouth data analysis unit analyzes the word-of-mouth data collected by the word-of-mouth data collection unit. For example, the generation AI can understand the content of the reviews using text analysis technology and classify them into positive and negative reviews. The generation AI can also perform sentiment analysis of the word-of-mouth data and provide detailed evaluations based on the intensity and type of sentiment. For example, a review that states "It's very easy to use" is classified as a positive review, and a review that states "It's fragile" is classified as a negative review. The evaluation generation unit generates evaluations of products and services based on the review data analyzed by the review data analysis unit. For example, the generation AI generates an overall evaluation score and ranking based on the analysis results of the review data. The generation AI can also provide evaluations based on user emotions based on the results of sentiment analysis of the review data. For example, reviews with strong positive sentiments are displayed as high ratings. The provision unit provides the evaluations generated by the evaluation generation unit to the user. For example, when searching for products on an e-commerce site, evaluation scores and rankings are displayed. The generation AI can also recommend products that are optimal for the user based on the user's past purchase history and browsing history. For example, it recommends products similar to products purchased in the past or products that other users have given high ratings. As a result, the review analysis system according to the embodiment allows users to select products and services based on reliable information.For example, users can choose products based on reviews from other users, making purchasing decisions based on reliable information. Product recommendations by generative AI also make it easier for users to find the best products for them.

[0059] The review data collection unit can estimate a user's emotions in real time when collecting review data and provide an interface for eliciting positive emotions. The review data collection unit, for example, uses a generation AI to analyze a user's facial expressions and voice tone when collecting review data and estimate emotions in real time. For example, a camera or microphone is used to analyze a user's emotions and provide an interface for eliciting positive emotions. In addition, when a user posts a review, the generation AI estimates the user's emotions in real time and displays messages or suggestions for eliciting positive emotions. For example, if the user has negative emotions, an encouraging message is displayed. In addition, the review data collection unit, when collecting review data, the generation AI analyzes the user's emotions in real time and provides an interface for eliciting positive emotions. For example, if the user has positive emotions, a message of gratitude is displayed. This allows for better review data to be collected by estimating a user's emotions in real time and eliciting positive emotions.

[0060] The review data collection unit can analyze a user's voice input and automatically convert it into text data. For example, when a user posts a review by voice, the generation AI analyzes the voice data and automatically converts it into text data. For example, when a user posts a review by voice, "This product is very easy to use," it is saved as text data. The review data collection unit also uses voice recognition technology to build a system that converts a user's voice input into text data in real time. For example, when a user voice inputs into a smartphone, the content is instantly displayed as text data. The review data collection unit also adds a function that allows the generation AI to analyze voice data and automatically convert it into text data. For example, when a user posts a review by voice, the content is saved as text data and can be viewed by other users. This makes it easier to collect review data by automatically converting a user's voice input into text data.

[0061] The review data collection unit can analyze a user's past posting history and develop an algorithm that prioritizes collecting reviews on specific topics. For example, the generation AI in the review data collection unit analyzes a user's past posting history and develops an algorithm that prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also analyzes a user's past posting history and builds a system that prioritizes collecting reviews on specific topics. For example, it prioritizes displaying reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also develops an algorithm that analyzes a user's past posting history and prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. This allows for the collection of more relevant review data by analyzing a user's past posting history and prioritizes collecting reviews on specific topics.

[0062] The review data collection unit can analyze the content of images or videos posted by users and combine it with text data to perform an evaluation. For example, the review data collection unit uses a generation AI to analyze the content of images and videos posted by users and combine it with text data to perform an evaluation. For example, the unit analyzes videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses image recognition technology to build a system that analyzes the content of images posted by users and combines it with text data to perform an evaluation. For example, the unit analyzes images showing the appearance and condition of a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses video analysis technology to build a system that analyzes the content of videos posted by users and combines it with text data to perform an evaluation. For example, the unit analyzes videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. This enables more detailed evaluations by analyzing the content of images and videos posted by users and combining it with text data to perform an evaluation.

[0063] The review data collection unit can automatically translate reviews posted in different languages, enabling evaluation from a global perspective. The review data collection unit, for example, builds a system in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Japanese are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also uses machine translation technology to develop a system that translates reviews posted in different languages ​​in real time. For example, reviews posted in French are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also builds a system in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Chinese are translated into English and evaluations are provided to English-speaking users as well. This makes it possible to automatically translate reviews posted in different languages, enabling evaluation from a global perspective.

[0064] The review data collection unit uses the emotion estimation function to analyze the emotions of users when they post reviews in real time, and can provide positive feedback to users who have negative emotions. For example, the review data collection unit uses the emotion estimation function to analyze the emotions of users when they post reviews in real time, and builds a system that provides positive feedback to users who have negative emotions. For example, if a user has negative emotions, an encouraging message is displayed. The review data collection unit also develops a system in which a generation AI analyzes user emotions in real time, and provides positive feedback to users who have negative emotions. For example, if a user has negative emotions, a message of gratitude is displayed. The review data collection unit also uses the emotion estimation function to analyze the emotions of users when they post reviews in real time, and builds a system that provides positive feedback to users who have negative emotions. For example, if a user has negative emotions, an encouraging message is displayed. This makes it possible to collect better review data by analyzing user emotions in real time and providing positive feedback to users who have negative emotions.

[0065] The review data analysis unit performs sentiment analysis of the review data and is able to provide a detailed evaluation based on the intensity and type of emotion. The review data analysis unit, for example, uses a generation AI to perform sentiment analysis of the review data and builds a system that provides a detailed evaluation based on the intensity and type of emotion. For example, reviews with strong positive emotions are classified as high ratings. The review data analysis unit also uses sentiment analysis technology to analyze the intensity and type of emotion in the review data and develops a system that provides a detailed evaluation based on the results. For example, reviews with strong negative emotions are classified as low ratings. The review data analysis unit also uses a generation AI to perform sentiment analysis of the review data and builds a system that provides a detailed evaluation based on the intensity and type of emotion. For example, reviews with strong positive emotions are classified as high ratings. This enables more accurate evaluations by performing sentiment analysis of the review data and providing a detailed evaluation based on the intensity and type of emotion.

[0066] The review data analysis unit takes into account the user's background information (age, gender, region, etc.) when analyzing review data, allowing it to provide more personalized evaluations. For example, the review data analysis unit takes into account the user's background information (age, gender, region, etc.) when analyzing review data, building a system that provides more personalized evaluations. For example, it prioritizes displaying reviews from users in the same age group. The review data analysis unit also develops a system in which a generation AI analyzes the user's background information and provides personalized evaluations based on that information. For example, it prioritizes displaying reviews from users in the same region. The review data analysis unit also builds a system that takes into account the user's background information when analyzing review data, allowing it to provide more personalized evaluations. For example, it prioritizes displaying reviews from users of the same gender. In this way, by taking the user's background information into consideration, it is possible to provide more personalized evaluations.

[0067] The review data analysis unit can add a function that understands the context of the review data and subdivides the ratings based on specific keywords and phrases. For example, the review data analysis unit adds a function that allows the generation AI to understand the context of the review data and subdivide the ratings based on specific keywords and phrases. For example, it may classify ratings based on keywords such as "easy to use" and "easy to break." The review data analysis unit also uses context analysis technology to build a system that subdivides ratings based on specific keywords and phrases in the review data. For example, it may classify ratings based on keywords such as "good design" and "expensive." The review data analysis unit also adds a function that allows the generation AI to understand the context of the review data and subdivide the ratings based on specific keywords and phrases. For example, it may classify ratings based on keywords such as "easy to use" and "easy to break." This enables more detailed evaluations by understanding the context of the review data and subdividing ratings based on specific keywords and phrases.

[0068] The review data analysis unit can convert the results of review data analysis into visual notes or mind maps to make it easier to understand visually. For example, the review data analysis unit converts the results of review data analysis into visual notes and builds a system that makes it easier to understand visually. For example, important points are shown using diagrams and icons. The review data analysis unit also develops a system in which the generative AI converts the results of review data analysis into mind map format and visually organizes related keywords and concepts. For example, it makes it possible to understand the overall picture of an idea at a glance. The review data analysis unit also converts the results of review data analysis into visual notes or mind maps and builds a system that makes it easier to understand visually. For example, important points are shown using diagrams and icons. In this way, converting the results of review data analysis into visual notes or mind maps makes it easier to understand visually.

[0069] The word-of-mouth data analysis unit analyzes word-of-mouth data from different industries and fields, thereby promoting crossover innovation. For example, the generative AI in the word-of-mouth data analysis unit analyzes word-of-mouth data from different industries and fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different industries to develop a system that proposes new products and services. For example, it generates ideas that apply medical technology to everyday life. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. This allows crossover innovation to be promoted by analyzing word-of-mouth data from different industries and fields.

[0070] The review data analysis unit uses the emotion estimation function to collect users' emotional reactions to the review data analysis results, thereby improving the accuracy of the analysis algorithm. The review data analysis unit, for example, uses the emotion estimation function to collect users' emotional reactions to the review data analysis results and builds a system that improves the accuracy of the analysis algorithm. For example, it prioritizes the adoption of analysis results with a high number of positive emotional reactions. The review data analysis unit also develops a system in which the generation AI analyzes users' emotional reactions in real time and improves the accuracy of the analysis algorithm based on that data. For example, it reanalyzes analysis results with a high number of negative emotional reactions. The review data analysis unit also uses the emotion estimation function to collect users' emotional reactions to the review data analysis results and builds a system that improves the accuracy of the analysis algorithm based on that data. For example, it prioritizes the adoption of analysis results with a high number of positive emotional reactions. In this way, by collecting users' emotional reactions to the review data analysis results and improving the accuracy of the analysis algorithm, more accurate evaluations are possible.

[0071] The rating generation unit can provide ratings based on user emotions based on the results of sentiment analysis of the review data. The rating generation unit, for example, uses a generation AI to build a system that provides ratings based on user emotions based on the results of sentiment analysis of the review data. For example, reviews with strong positive emotions are displayed as high ratings. The rating generation unit also uses sentiment analysis technology to analyze the intensity and type of emotions in the review data, and develops a system that provides ratings based on user emotions based on the results. For example, reviews with strong negative emotions are displayed as low ratings. The rating generation unit also builds a system that uses a generation AI to perform sentiment analysis of the review data, and provides ratings based on user emotions based on the results. For example, reviews with strong positive emotions are displayed as high ratings. This enables more personalized ratings by providing ratings based on user emotions based on the results of sentiment analysis of the review data.

[0072] The rating generation unit takes into account a user's past purchase history and browsing history when rating products and services, thereby enabling the provision of more personalized ratings. The rating generation unit, for example, builds a system that takes into account a user's past purchase history and browsing history when rating products and services, thereby providing more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. The rating generation unit also develops a system in which a generation AI analyzes a user's past purchase history and browsing history, and provides personalized ratings based on that information. For example, products related to products previously viewed are displayed as highly rated. The rating generation unit also builds a system that takes into account a user's past purchase history and browsing history when rating products and services, thereby providing more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. In this way, more personalized ratings can be provided by taking into account a user's past purchase history and browsing history.

[0073] The rating generation unit can add a function to update rating scores and rankings in real time based on the analysis results of review data. For example, the rating generation unit builds a system that adds a function to update rating scores and rankings in real time based on the analysis results of review data by the generation AI. For example, the rating scores and rankings are updated every time a new review is posted. The rating generation unit also develops a system that analyzes review data in real time and updates rating scores and rankings based on the results. For example, the rating scores increase as the number of positive reviews increases. The rating generation unit also builds a system that adds a function to update rating scores and rankings in real time based on the analysis results of review data by the generation AI. For example, the rating scores and rankings are updated every time a new review is posted. This makes it possible to provide the latest rating information by updating rating scores and rankings in real time based on the analysis results of review data.

[0074] The evaluation generation unit can automatically translate evaluation results of products and services into different languages ​​and provide evaluations from an international perspective. For example, the evaluation generation unit builds a system in which a generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, it translates evaluation results in Japanese into English and provides evaluations to English-speaking users as well. The evaluation generation unit also develops a system that uses machine translation technology to translate evaluation results of products and services in real time. For example, it translates evaluation results in French into English and provides evaluations to English-speaking users as well. The evaluation generation unit also builds a system in which a generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, it translates evaluation results in Chinese into English and provides evaluations to English-speaking users as well. This makes it possible to provide evaluations from an international perspective by automatically translating evaluation results into different languages.

[0075] The evaluation generation unit can convert the evaluation results into visual notes or mind maps to make them easier to understand visually. For example, the evaluation generation unit builds a system in which a generation AI converts the evaluation results of products or services into visual notes to make them easier to understand visually. For example, it shows important points with diagrams and icons. The evaluation generation unit also develops a system in which the evaluation results are converted into mind map format to visually organize related keywords and concepts. For example, it makes it possible to understand the overall picture of an idea at a glance. The evaluation generation unit also builds a system in which a generation AI converts the evaluation results of products or services into visual notes or mind maps to make them easier to understand visually. For example, it shows important points with diagrams and icons. By converting the evaluation results into visual notes or mind maps, it makes them easier to understand visually.

[0076] The rating generation unit can use the emotion estimation function to collect users' emotional responses to the rating results and improve the accuracy of the rating algorithm. The rating generation unit, for example, uses the emotion estimation function to collect users' emotional responses to the rating results and build a system that improves the accuracy of the rating algorithm. For example, it prioritizes the adoption of rating results with a high number of positive emotional responses. The rating generation unit also develops a system in which a generation AI analyzes users' emotional responses in real time and improves the accuracy of the rating algorithm based on that data. For example, it reanalyzes rating results with a high number of negative emotional responses. The rating generation unit also uses the emotion estimation function to collect users' emotional responses to the rating results and builds a system that improves the accuracy of the rating algorithm based on that data. For example, it prioritizes the adoption of rating results with a high number of positive emotional responses. In this way, by collecting users' emotional responses to the rating results and improving the accuracy of the rating algorithm, more accurate ratings are possible.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The review data collection unit can analyze a user's voice input and automatically convert it into text data. For example, when a user posts a review by voice, the generation AI analyzes the voice data and automatically converts it into text data. For example, if a user posts "This product is very easy to use" by voice, it is saved as text data. The review data collection unit also uses voice recognition technology to build a system that converts a user's voice input into text data in real time. For example, when a user voice inputs into a smartphone, the content is instantly displayed as text data. The review data collection unit also adds a function that enables the generation AI to analyze voice data and automatically convert it into text data. For example, when a user posts a review by voice, the content is saved as text data and can be viewed by other users. This makes it easier to collect review data by automatically converting a user's voice input into text data.

[0079] The review data collection unit can analyze a user's past posting history and develop an algorithm that prioritizes collecting reviews on specific topics. For example, the generation AI analyzes a user's past posting history and develops an algorithm that prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also analyzes a user's past posting history and builds a system that prioritizes collecting reviews on specific topics. For example, it prioritizes displaying reviews on products in a category in which the user has posted many reviews in the past. The review data collection unit also analyzes a user's past posting history and develops an algorithm that prioritizes collecting reviews on specific topics. For example, it prioritizes collecting reviews on products in a category in which the user has posted many reviews in the past. In this way, by analyzing a user's past posting history and priority collecting reviews on specific topics, more relevant review data can be collected.

[0080] The review data collection unit can analyze the content of images or videos posted by users and combine it with text data to perform an evaluation. For example, a generation AI can analyze the content of images and videos posted by users and combine it with text data to perform an evaluation. For example, it can analyze videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses image recognition technology to build a system that analyzes the content of images posted by users and combines it with text data to perform an evaluation. For example, it can analyze images showing the appearance or condition of a product and their explanatory text to perform a comprehensive evaluation. The review data collection unit also uses video analysis technology to build a system that analyzes the content of videos posted by users and combines it with text data to perform an evaluation. For example, it can analyze videos showing how to use a product and their explanatory text to perform a comprehensive evaluation. This allows for more detailed evaluations by analyzing the content of images and videos posted by users and combining it with text data to perform an evaluation.

[0081] The review data collection unit can automatically translate reviews posted in different languages, enabling evaluation from a global perspective. For example, a system is constructed in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Japanese are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also uses machine translation technology to develop a system that translates reviews posted in different languages ​​in real time. For example, reviews posted in French are translated into English and evaluations are provided to English-speaking users as well. The review data collection unit also constructs a system in which a generation AI automatically translates reviews posted in different languages, enabling evaluation from a global perspective. For example, reviews posted in Chinese are translated into English and evaluations are provided to English-speaking users as well. This makes it possible to automatically translate reviews posted in different languages, enabling evaluation from a global perspective.

[0082] The review data collection unit uses the emotion estimation function to analyze the emotions of users when they post reviews in real time, and can provide positive feedback to users who have negative emotions. For example, a system is constructed using the emotion estimation function to analyze the emotions of users when they post reviews in real time, and provide positive feedback to users who have negative emotions. For example, if a user has negative emotions, an encouraging message is displayed. The review data collection unit also develops a system in which a generation AI analyzes user emotions in real time, and provides positive feedback to users who have negative emotions. For example, if a user has negative emotions, a message of gratitude is displayed. The review data collection unit also uses the emotion estimation function to analyze the emotions of users when they post reviews in real time, and builds a system that provides positive feedback to users who have negative emotions. For example, if a user has negative emotions, an encouraging message is displayed. This allows for better review data collection by analyzing user emotions in real time and providing positive feedback to users who have negative emotions.

[0083] The review data analysis unit performs sentiment analysis of review data and is able to provide detailed evaluations based on the intensity and type of emotion. For example, a system is constructed using a generation AI to perform sentiment analysis of review data and provide detailed evaluations based on the intensity and type of emotion. For example, reviews with strong positive emotions are classified as high ratings. The review data analysis unit also uses sentiment analysis technology to analyze the intensity and type of emotion in review data and develops a system that provides detailed evaluations based on the results. For example, reviews with strong negative emotions are classified as low ratings. The review data analysis unit also uses a generation AI to perform sentiment analysis of review data and build a system that provides detailed evaluations based on the intensity and type of emotion. For example, reviews with strong positive emotions are classified as high ratings. This enables more accurate evaluations by performing sentiment analysis of review data and providing detailed evaluations based on the intensity and type of emotion.

[0084] The review data analysis unit takes into account the user's background information (age, gender, region, etc.) when analyzing review data, allowing it to provide more personalized evaluations. For example, a system is constructed that takes into account the user's background information (age, gender, region, etc.) when analyzing review data and provides more personalized evaluations. For example, reviews from users in the same age group are preferentially displayed. The review data analysis unit also develops a system in which the generation AI analyzes the user's background information and provides personalized evaluations based on that information. For example, reviews from users in the same region are preferentially displayed. The review data analysis unit also builds a system that takes into account the user's background information when analyzing review data and provides more personalized evaluations. For example, reviews from users of the same gender are preferentially displayed. In this way, by taking the user's background information into consideration, it is possible to provide more personalized evaluations.

[0085] The review data analysis unit can add a function that understands the context of the review data and subdivides ratings based on specific keywords and phrases. For example, the generation AI can add a function that understands the context of the review data and subdivides ratings based on specific keywords and phrases. For example, it can classify ratings based on keywords such as "easy to use" and "easy to break." The review data analysis unit also uses context analysis technology to build a system that subdivides ratings based on specific keywords and phrases in the review data. For example, it can classify ratings based on keywords such as "good design" and "expensive." The review data analysis unit also adds a function that enables the generation AI to understand the context of the review data and subdivide ratings based on specific keywords and phrases. For example, it can classify ratings based on keywords such as "easy to use" and "easy to break." This enables more detailed evaluations by understanding the context of the review data and subdividing ratings based on specific keywords and phrases.

[0086] The review data analysis department can convert the results of review data analysis into visual notes or mind maps to make them easier to understand visually. For example, we will build a system that converts the results of review data analysis into visual notes to make them easier to understand visually. For example, we will show important points with diagrams and icons. The review data analysis department will also develop a system in which the generative AI converts the results of review data analysis into mind map format to visually organize related keywords and concepts. For example, we will make it possible to understand the overall picture of an idea at a glance. The review data analysis department will also convert the results of review data analysis into visual notes or mind maps to build a system that makes them easier to understand visually. For example, we will show important points with diagrams and icons. In this way, converting the results of review data analysis into visual notes or mind maps will make them easier to understand visually.

[0087] The word-of-mouth data analysis unit analyzes word-of-mouth data from different industries and fields, enabling it to promote crossover innovation. For example, the generation AI analyzes word-of-mouth data from different industries and fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different industries to develop a system that proposes new products and services. For example, it generates ideas that apply medical technology to everyday life. The word-of-mouth data analysis unit also analyzes word-of-mouth data from different fields to build a system that promotes crossover innovation. For example, it generates ideas that combine the technology field with the consumer market. In this way, crossover innovation can be promoted by analyzing word-of-mouth data from different industries and fields.

[0088] The review data analysis unit uses the emotion estimation function to collect users' emotional reactions to the review data analysis results, thereby improving the accuracy of the analysis algorithm. For example, the emotion estimation function is used to collect users' emotional reactions to the review data analysis results, thereby building a system that improves the accuracy of the analysis algorithm. For example, analysis results with a high number of positive emotional reactions are prioritized. The review data analysis unit also develops a system in which a generative AI analyzes users' emotional reactions in real time and improves the accuracy of the analysis algorithm based on that data. For example, analysis results with a high number of negative emotional reactions are reanalyzed. The review data analysis unit also uses the emotion estimation function to collect users' emotional reactions to the review data analysis results, thereby building a system that improves the accuracy of the analysis algorithm based on that data. For example, analysis results with a high number of positive emotional reactions are prioritized. This allows for more accurate evaluations by collecting users' emotional reactions to the review data analysis results and improving the accuracy of the analysis algorithm.

[0089] The rating generation unit can provide ratings based on user emotions based on the results of sentiment analysis of review data. For example, a system is built using generation AI to provide ratings based on user emotions based on the results of sentiment analysis of review data. For example, reviews with strong positive emotions are displayed as high ratings. The rating generation unit also uses sentiment analysis technology to analyze the intensity and type of emotions in review data, and develops a system that provides ratings based on user emotions based on the results. For example, reviews with strong negative emotions are displayed as low ratings. The rating generation unit also builds a system using generation AI to perform sentiment analysis of review data, and provides ratings based on user emotions based on the results. For example, reviews with strong positive emotions are displayed as high ratings. This enables more personalized ratings by providing ratings based on user emotions based on the results of sentiment analysis of review data.

[0090] The rating generation unit takes into account a user's past purchase history and browsing history when rating products and services, allowing it to provide more personalized ratings. For example, a system is constructed that takes into account a user's past purchase history and browsing history when rating products and services, allowing it to provide more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. The rating generation unit also develops a system in which a generation AI analyzes a user's past purchase history and browsing history, and provides personalized ratings based on that information. For example, products related to products previously viewed are displayed as highly rated. The rating generation unit also takes into account a user's past purchase history and browsing history when rating products and services, allowing it to provide more personalized ratings. For example, products similar to products purchased in the past are displayed as highly rated. In this way, more personalized ratings can be provided by taking into account a user's past purchase history and browsing history.

[0091] The rating generation unit can add a function to update rating scores and rankings in real time based on the analysis results of review data. For example, a system can be built in which the generation AI adds a function to update rating scores and rankings in real time based on the analysis results of review data. For example, the rating scores and rankings are updated every time a new review is posted. The rating generation unit can also develop a system that analyzes review data in real time and updates rating scores and rankings based on the results. For example, the rating scores increase as the number of positive reviews increases. The rating generation unit can also build a system in which the generation AI adds a function to update rating scores and rankings in real time based on the analysis results of review data. For example, the rating scores and rankings are updated every time a new review is posted. This makes it possible to provide the latest rating information by updating rating scores and rankings in real time based on the analysis results of review data.

[0092] The evaluation generation unit can automatically translate evaluation results of products and services into different languages ​​and provide evaluations from an international perspective. For example, a system is built in which the generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, evaluation results in Japanese are translated into English and evaluations are provided to English-speaking users as well. The evaluation generation unit also uses machine translation technology to develop a system that translates evaluation results of products and services in real time. For example, evaluation results in French are translated into English and evaluations are provided to English-speaking users as well. The evaluation generation unit also builds a system in which the generation AI automatically translates evaluation results of products and services into different languages ​​and provides evaluations from an international perspective. For example, evaluation results in Chinese are translated into English and evaluations are provided to English-speaking users as well. This makes it possible to provide evaluations from an international perspective by automatically translating evaluation results into different languages.

[0093] The evaluation generation unit can convert the evaluation results into visual notes or mind maps to make them easier to understand visually. For example, a system can be built in which the generation AI converts the evaluation results of products and services into visual notes to make them easier to understand visually. For example, important points can be shown with diagrams and icons. The evaluation generation unit can also develop a system in which the generation AI converts the evaluation results into mind map format to visually organize related keywords and concepts. For example, to make it possible to understand the overall picture of an idea at a glance. The evaluation generation unit can also develop a system in which the generation AI converts the evaluation results of products and services into visual notes or mind maps to make them easier to understand visually. For example, important points can be shown with diagrams and icons. By converting the evaluation results into visual notes or mind maps, they can be easier to understand visually.

[0094] The rating generation unit can use the emotion estimation function to collect users' emotional responses to rating results and improve the accuracy of the rating algorithm. For example, the emotion estimation function is used to collect users' emotional responses to rating results and build a system that improves the accuracy of the rating algorithm. For example, rating results with a high number of positive emotional responses are preferentially adopted. The rating generation unit also develops a system in which a generation AI analyzes users' emotional responses in real time and improves the accuracy of the rating algorithm based on that data. For example, rating results with a high number of negative emotional responses are reanalyzed. The rating generation unit also uses the emotion estimation function to collect users' emotional responses to rating results and build a system that improves the accuracy of the rating algorithm based on that data. For example, rating results with a high number of positive emotional responses are preferentially adopted. In this way, by collecting users' emotional responses to rating results and improving the accuracy of the rating algorithm, more accurate ratings are possible.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The word-of-mouth data collection unit collects word-of-mouth data. For example, it collects text reviews, rating scores, images, videos, and other data posted by users on e-commerce sites. The word-of-mouth data collection unit can also collect text comments such as "This product is very easy to use" when users post reviews of purchased products. The word-of-mouth data collection unit can also use a generation AI to estimate user emotions in real time as the word-of-mouth data is collected and provide an interface to elicit positive emotions. For example, the generation AI can analyze the user's facial expressions and tone of voice to estimate emotions in real time. Step 2: The review data analysis unit analyzes the review data collected by the review data collection unit. For example, the generation AI uses text analysis technology to understand the content of the reviews and classify them into positive and negative evaluations. The generation AI can also perform sentiment analysis of the review data and provide a detailed evaluation based on the intensity and type of emotion. For example, a review that says "it's very easy to use" is classified as a positive evaluation, and a review that says "it's fragile" is classified as a negative evaluation. Step 3: The evaluation generation unit generates evaluations of products and services based on the review data analyzed by the review data analysis unit. For example, the generation AI generates an overall evaluation score and ranking based on the results of the review data analysis. The generation AI can also provide evaluations based on user emotions based on the results of sentiment analysis of the review data. For example, reviews with strong positive sentiments are displayed as high ratings. Step 4: The providing unit provides the user with the ratings generated by the rating generation unit. For example, when searching for a product on an e-commerce site, rating scores and rankings are displayed. The generation AI can also recommend optimal products to users based on their past purchase and browsing history. For example, it can recommend products similar to products purchased in the past or products that other users have given high ratings to.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 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.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the 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.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 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 word-of-mouth data collection unit that collects word-of-mouth data; a word-of-mouth data analysis unit that analyzes the word-of-mouth data collected by the word-of-mouth data collection unit; a review generation unit that generates reviews of products and services based on the review data analyzed by the review data analysis unit; a providing unit that provides the evaluation generated by the evaluation generating unit to a user. A system characterized by:

2. The word-of-mouth data collection unit The user's emotions are estimated in real time when the word-of-mouth data is collected, and an interface is provided for eliciting positive emotions.

2. The system of claim 1.

3. The word-of-mouth data collection unit Analyzing the user's voice input and automatically converting it into text data 2. The system of claim 1.

4. The word-of-mouth data collection unit Analyze the user's past posting history and develop an algorithm that prioritizes collecting reviews on specific topics.

2. The system of claim 1.

5. The word-of-mouth data collection unit The content of the image or video posted by the user is analyzed and combined with text data to make the evaluation.

2. The system of claim 1.

Images