System

Abstract

An object of the system according to the embodiment is to efficiently collect, classify, and analyze word of mouth from various platforms on the Internet.SOLUTION: A system includes a word-of-mouth collection part, a word-of-mouth classification part, and a word-of-mouth analysis part. The word-of-mouth collection unit automatically collects words of mouth from various platforms on the Internet. The word-of-mouth classifying unit classifies the words of mouth collected by the word-of-mouth collecting unit. The word-of-mouth analysis unit analyzes the words of mouth classified by the word-of-mouth classification 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 technology has faced the challenge of making it difficult to efficiently collect, classify, and analyze word-of-mouth information from various platforms on the Internet.

[0005] The system according to the embodiment aims to efficiently collect, classify, and analyze word-of-mouth information from various platforms on the Internet. [Means for solving the problem]

[0006] The system according to the embodiment includes a review collection unit, a review classification unit, and a review analysis unit. The review collection unit automatically collects reviews from various platforms on the Internet. The review classification unit classifies the reviews collected by the review collection unit. The review analysis unit analyzes the reviews classified by the review classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, classify, and analyze reviews from various platforms on the Internet. [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 review collector system according to an embodiment of the present invention allows companies to easily understand the reputation of their services and products, and allows individuals to easily obtain reference information when purchasing products. This system comprehensively investigates reviews of all kinds of products and provides a variety of opinions. As a result, the review collector system allows companies to easily understand the reputation of their services and products, and allows individuals to easily obtain reference information when purchasing products.

[0029] A review collector system according to an embodiment includes a review collection unit, a review classification unit, and a review analysis unit. The review collection unit automatically collects reviews from various online platforms. For example, reviews are collected from online shopping sites, social networking sites, blogs, bulletin boards, etc. The review collection unit also uses a generation AI to evaluate the reliability of the collected reviews and prioritize collecting reliable reviews. For example, the generation AI analyzes the poster's account information and past posting history to evaluate the reliability of the collected reviews. The generation AI also prioritizes collecting reviews from reliable accounts. The review collection unit also analyzes the content of reviews and prioritizes collecting reviews from reliable sources. For example, reviews from official websites and verified accounts are prioritized. The generation AI also measures the degree of agreement between the content of reviews and other reliable sources and prioritizes collecting reviews that match multiple reliable sources. The review classification unit classifies the collected reviews. For example, the generation AI analyzes the content of reviews and classifies them into positive, negative, and neutral opinions. The generation AI also analyzes the content of reviews in detail and further categorizes them based on specific keywords and phrases. For example, the generation AI categorizes reviews into categories such as product quality, price, and design. Furthermore, the generation AI analyzes the content of reviews and extracts and categorizes them based on specific themes or topics. For example, the generation AI categorizes reviews into categories such as service response, delivery speed, and after-sales service. The review analysis unit analyzes the categorized reviews. For example, the generation AI creates a report for a company based on the analysis results. The report for the company includes detailed information about the reputation of the service or product. For example, the report summarizes the ratio of positive and negative opinions, specific problems, and areas for improvement. The generation AI also provides reference information for individuals when purchasing products. For example, the report provides information summarizing reviews about specific products and recommended products based on other users' ratings. As a result, the review collector system according to the embodiment allows companies to easily understand the reputation of services and products and individuals to easily obtain reference information when purchasing products.

[0030] The review collection unit can collect reviews from online shopping sites, social media, blogs, and message boards. The review collection unit, for example, collects reviews from online shopping sites. For example, it collects reviews from sites such as Amazon and Rakuten. The review collection unit also collects reviews from social media. For example, it collects reviews from social media such as Facebook, Twitter, and Instagram. The review collection unit also collects reviews from blogs. For example, it collects reviews from personal blogs and corporate blogs. The review collection unit also collects reviews from message boards. For example, it collects reviews from message boards such as 2channel and Reddit. This makes it possible to provide comprehensive information by collecting reviews from a variety of platforms.

[0031] The review classification unit can analyze the content of reviews and classify them into positive, negative, and neutral opinions. The review classification unit, for example, uses a generation AI to analyze the content of reviews and classify them into positive, negative, and neutral opinions. For example, the generation AI classifies positive evaluations and good reviews as positive opinions. The generation AI also classifies negative evaluations and bad reviews as negative opinions. The generation AI also classifies mixed reviews and neutral opinions as neutral opinions. This allows for more detailed classification of the content of reviews, enabling more precise analysis.

[0032] The review analysis unit can create a report for a company based on the analysis results. The review analysis unit, for example, uses a generation AI to create a report for a company based on the analysis results. For example, the generation AI compiles detailed information about the reputation of a service or product into a report. For example, the report may include the ratio of positive and negative opinions, specific problems, and areas for improvement. The generation AI can also include statistical data and graphs in the report for a company. For example, the report may include graphs showing word-of-mouth trends and statistical data on specific themes. In this way, creating a report for a company makes it easier for the company to understand the reputation of its service or product.

[0033] The review analysis unit can provide reference information for individuals when purchasing products. The review analysis unit uses, for example, a generation AI to provide reference information for individuals when purchasing products. For example, the generation AI provides information that summarizes reviews about a specific product. For example, the generation AI provides recommended products based on the ratings of other users. The generation AI also analyzes the content of reviews to provide detailed information about specific products. For example, it provides information such as product ratings, usability, and price comparisons. This allows individuals to easily obtain reference information when purchasing products.

[0034] The word-of-mouth analysis unit can compare the opinions of users of different age groups and regions, and analyze fluctuations in word-of-mouth over a specific period of time. The word-of-mouth analysis unit, for example, uses generation AI to compare the opinions of users of different age groups and regions. For example, generation AI compares the opinions of young people and older people. Generation AI also compares opinions in urban and rural areas. Furthermore, generation AI analyzes fluctuations in word-of-mouth over a specific period of time. For example, generation AI analyzes fluctuations in word-of-mouth during a specific event period. This makes it possible to perform a multifaceted analysis by comparing opinions from different perspectives.

[0035] The review collection unit can evaluate the reliability of the collected reviews and prioritize collecting highly reliable reviews. The review collection unit evaluates the reliability of the collected reviews, for example, using a generation AI. For example, the generation AI analyzes the poster's account information and past posting history, and prioritizes collecting reviews from highly reliable accounts. The generation AI also analyzes the content of the reviews and prioritizes collecting reviews from highly reliable sources. For example, it prioritizes reviews from official websites and verified accounts. Furthermore, the generation AI measures the degree of match between the content of the reviews and other highly reliable sources, and prioritizes collecting reviews that match multiple highly reliable sources. This allows for the provision of more accurate information by prioritized collection of highly reliable reviews.

[0036] The review collection unit collects reviews by associating them with specific time periods or events, making it possible to understand fluctuations over time. The review collection unit, for example, uses a generation AI to collect reviews during specific events or campaign periods. For example, the generation AI concentrates on collecting reviews during sales periods. The generation AI also limits the review collection to specific time periods and analyzes fluctuations in reputation for each time period. For example, the generation AI compares reviews from daytime and nighttime to understand differences in reputation depending on the time of day. Furthermore, the generation AI collects reviews related to specific events and analyzes the impact of the event over time. For example, the generation AI collects fluctuations in reviews before and after a new product launch event. In this way, by collecting reviews related to specific time periods or events, it is possible to understand fluctuations over time.

[0037] The word-of-mouth collection unit can also collect word-of-mouth in audio or video format and convert it into text using voice recognition technology or video analysis technology. The word-of-mouth collection unit, for example, uses generation AI to collect word-of-mouth in audio format and converts it into text using voice recognition technology. For example, the generation AI collects word-of-mouth from podcasts or audio messages. The generation AI also collects word-of-mouth in video format and converts the content into text using video analysis technology. For example, the generation AI extracts word-of-mouth from review videos on YouTube. Furthermore, the generation AI collects word-of-mouth in audio or video format and integrates it with text data for analysis. For example, the generation AI combines the content of the audio or video with the text data to comprehensively evaluate it. In this way, by collecting word-of-mouth in audio or video format, more diverse information can be analyzed.

[0038] The review classification unit can analyze the content of reviews in detail and further classify them based on specific keywords and phrases. The review classification unit, for example, uses a generation AI to analyze the content of reviews in detail and further classify them based on specific keywords and phrases. For example, the generation AI classifies reviews into categories such as product quality, price, and design. The generation AI also analyzes the content of reviews and extracts and classifies keywords related to specific themes or topics. For example, the generation AI classifies reviews based on service response, delivery speed, after-sales service, etc. The generation AI also analyzes the content of reviews in detail and builds a system that further classifies them based on specific keywords and phrases. For example, the generation AI classifies reviews into positive opinions, negative opinions, and neutral opinions. This enables more precise analysis by analyzing the content of reviews in detail and further classifying them based on specific keywords and phrases.

[0039] The review classification unit takes into account the user's background information when classifying reviews, enabling more precise classification. The review classification unit, for example, uses a generation AI to take into account the user's background information when classifying reviews. For example, the generation AI considers background information such as the user's age, gender, and region when classifying. For example, the generation AI analyzes opinions of young and older users separately. The generation AI also analyzes differences in reputation by region. Furthermore, the generation AI builds a system that precisely classifies the content of reviews based on the user's background information. For example, the generation AI analyzes differences in opinions by gender. This allows for more precise analysis by classifying reviews taking into account the user's background information.

[0040] The review classification unit can visualize the content of the reviews and provide the classification results in a format that is visually easy to understand. The review classification unit visualizes the content of the reviews, for example, using a generation AI. For example, the generation AI displays the trends of reviews using graphs and charts. The generation AI also visualizes the classification results of the reviews so that the user can intuitively understand them. For example, the generation AI displays them using a word cloud or heat map. Furthermore, the generation AI visualizes the content of the reviews and builds a system that provides the classification results in a format that is visually easy to understand. For example, the generation AI provides an interactive dashboard. This makes it possible to provide the classification results in a format that is visually easy to understand by visualizing the content of the reviews.

[0041] The review classification unit can compare reviews from different industries and fields to identify similarities and differences. The review classification unit, for example, uses generation AI to compare reviews from different industries and fields. For example, the generation AI compares reviews from technology products and consumer goods. The generation AI also analyzes reviews from different industries to extract common problems and success factors. For example, the generation AI compares reviews from the service industry and the manufacturing industry. The generation AI also builds a system that compares reviews from different fields to identify similarities and differences. For example, the generation AI compares reviews from the medical field and the education field. This makes it possible to identify similarities and differences by comparing reviews from different industries and fields.

[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 collector system can further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes the user's past purchase history to identify products and services that the user may be interested in. For example, it recommends products that are suitable for the user based on the category and price range of products purchased in the past. The purchase history analysis unit also analyzes the user's purchasing patterns to understand purchasing trends related to specific times and events. For example, it analyzes purchasing trends related to specific events such as Christmas and birthdays. Furthermore, the purchase history analysis unit can combine the user's purchase history with the content of reviews to make more personalized recommendations. For example, it can recommend new products that are suitable for the user based on reviews of products purchased in the past. In this way, analyzing the user's purchase history enables more accurate product recommendations.

[0044] The review collector system can further include a location information analysis unit that analyzes the user's location information. The location information analysis unit analyzes the user's current location and past movement history to understand local reputations. For example, it identifies products and services that are popular in a particular area. The location information analysis unit can also provide reviews of nearby stores and services based on the user's location information. For example, it can provide real-time reviews of stores close to the user's current location. Furthermore, the location information analysis unit can analyze the user's movement patterns to understand fluctuations in reputation related to specific areas or events. For example, it can analyze fluctuations in reputation at tourist spots and event venues. This makes it easier to understand local reputations by analyzing the user's location information.

[0045] The review collector system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes information about the user's friendships and followers on the social networking site to identify reliable reviews. For example, it prioritizes collecting reviews posted by the user's friends and followers. The social network analysis unit may also analyze the user's activity history on the social networking site to identify products and services that the user may be interested in. For example, it may recommend products related to posts that the user has "liked" or shared. The social network analysis unit may also analyze the user's influence on the social networking site to prioritize collecting reviews from influential users. For example, it may prioritize reviews from users with a high number of followers or a high engagement rate. This makes it easier to collect reliable reviews by analyzing the user's social network.

[0046] The word-of-mouth collector system can further estimate a user's purchasing intent and recommend products and services based on the estimated purchasing intent. For example, if a user has a high purchasing intent, the purchasing intent estimation function can actively recommend related products. Also, if a user has a low purchasing intent, special offers and discount information can be provided. Furthermore, the purchasing intent estimation function can analyze fluctuations in a user's purchasing intent in real time and recommend products and services according to their purchasing intent. For example, if a user shows a high interest in a particular product, accessories and options related to that product can be recommended. This can improve the purchase rate by recommending products and services according to the user's purchasing intent.

[0047] The review collector system can further include a search history analysis unit that analyzes a user's search history. The search history analysis unit analyzes a user's past search history to identify products and services that the user may be interested in. For example, it recommends products that are suitable for the user based on keywords and categories searched for in the past. The search history analysis unit also analyzes the user's search patterns to understand search trends related to specific times or events. For example, it analyzes search trends related to specific seasons or events. Furthermore, the search history analysis unit can combine the user's search history with the content of reviews to make more personalized recommendations. For example, it can recommend new products that are suitable for the user based on reviews of products searched for in the past. In this way, analyzing a user's search history enables more accurate product recommendations.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The review collection unit automatically collects reviews from various online platforms. For example, reviews are collected from online shopping sites, social media, blogs, bulletin boards, etc. The generation AI is then used to evaluate the reliability of the collected reviews and prioritize the collection of highly reliable reviews. Specifically, it analyzes the poster's account information and past posting history to prioritize the collection of reviews from highly reliable accounts. It also analyzes the content of the reviews and prioritizes reviews from official websites and verified accounts. The generation AI measures the degree of match between the content of the review and other highly reliable information sources, and prioritizes the collection of reviews that match multiple highly reliable information sources. Step 2: The review classification unit classifies the collected reviews. Using generative AI, the content of the reviews is analyzed and classified into positive, negative, and neutral opinions. Reviews are further classified based on specific keywords and phrases. For example, they can be classified into categories such as product quality, price, and design. Keywords related to specific themes and topics are then extracted and classified. For example, they can be classified into categories such as service response, delivery speed, and after-sales service. Step 3: The review analysis unit analyzes the classified reviews. Generative AI is used to create a report for the company based on the analysis results. The report contains detailed information about the reputation of the service or product. For example, the report summarizes the ratio of positive to negative comments, specific problems, and areas for improvement. It also provides reference information for individuals when purchasing products. For example, it provides information summarizing reviews about specific products and recommended products based on the ratings of other users.

[0050] (Example 2) The review collector system according to an embodiment of the present invention allows companies to easily understand the reputation of their services and products, and allows individuals to easily obtain reference information when purchasing products. This system comprehensively investigates reviews of all kinds of products and provides a variety of opinions. As a result, the review collector system allows companies to easily understand the reputation of their services and products, and allows individuals to easily obtain reference information when purchasing products.

[0051] A review collector system according to an embodiment includes a review collection unit, a review classification unit, and a review analysis unit. The review collection unit automatically collects reviews from various online platforms. For example, reviews are collected from online shopping sites, social networking sites, blogs, bulletin boards, etc. The review collection unit also uses a generation AI to evaluate the reliability of the collected reviews and prioritize collecting reliable reviews. For example, the generation AI analyzes the poster's account information and past posting history to evaluate the reliability of the collected reviews. The generation AI also prioritizes collecting reviews from reliable accounts. The review collection unit also analyzes the content of reviews and prioritizes collecting reviews from reliable sources. For example, reviews from official websites and verified accounts are prioritized. The generation AI also measures the degree of agreement between the content of reviews and other reliable sources and prioritizes collecting reviews that match multiple reliable sources. The review classification unit classifies the collected reviews. For example, the generation AI analyzes the content of reviews and classifies them into positive, negative, and neutral opinions. The generation AI also analyzes the content of reviews in detail and further categorizes them based on specific keywords and phrases. For example, the generation AI categorizes reviews into categories such as product quality, price, and design. Furthermore, the generation AI analyzes the content of reviews and extracts and categorizes them based on specific themes or topics. For example, the generation AI categorizes reviews into categories such as service response, delivery speed, and after-sales service. The review analysis unit analyzes the categorized reviews. For example, the generation AI creates a report for a company based on the analysis results. The report for the company includes detailed information about the reputation of the service or product. For example, the report summarizes the ratio of positive and negative opinions, specific problems, and areas for improvement. The generation AI also provides reference information for individuals when purchasing products. For example, the report provides information summarizing reviews about specific products and recommended products based on other users' ratings. As a result, the review collector system according to the embodiment allows companies to easily understand the reputation of services and products and individuals to easily obtain reference information when purchasing products.

[0052] The review collection unit can collect reviews from online shopping sites, social media, blogs, and message boards. The review collection unit, for example, collects reviews from online shopping sites. For example, it collects reviews from sites such as Amazon and Rakuten. The review collection unit also collects reviews from social media. For example, it collects reviews from social media such as Facebook, Twitter, and Instagram. The review collection unit also collects reviews from blogs. For example, it collects reviews from personal blogs and corporate blogs. The review collection unit also collects reviews from message boards. For example, it collects reviews from message boards such as 2channel and Reddit. This makes it possible to provide comprehensive information by collecting reviews from a variety of platforms.

[0053] The review classification unit can analyze the content of reviews and classify them into positive, negative, and neutral opinions. The review classification unit, for example, uses a generation AI to analyze the content of reviews and classify them into positive, negative, and neutral opinions. For example, the generation AI classifies positive evaluations and good reviews as positive opinions. The generation AI also classifies negative evaluations and bad reviews as negative opinions. The generation AI also classifies mixed reviews and neutral opinions as neutral opinions. This allows for more detailed classification of the content of reviews, enabling more precise analysis.

[0054] The review analysis unit can create a report for a company based on the analysis results. The review analysis unit, for example, uses a generation AI to create a report for a company based on the analysis results. For example, the generation AI compiles detailed information about the reputation of a service or product into a report. For example, the report may include the ratio of positive and negative opinions, specific problems, and areas for improvement. The generation AI can also include statistical data and graphs in the report for a company. For example, the report may include graphs showing word-of-mouth trends and statistical data on specific themes. In this way, creating a report for a company makes it easier for the company to understand the reputation of its service or product.

[0055] The review analysis unit can provide reference information for individuals when purchasing products. The review analysis unit uses, for example, a generation AI to provide reference information for individuals when purchasing products. For example, the generation AI provides information that summarizes reviews about a specific product. For example, the generation AI provides recommended products based on the ratings of other users. The generation AI also analyzes the content of reviews to provide detailed information about specific products. For example, it provides information such as product ratings, usability, and price comparisons. This allows individuals to easily obtain reference information when purchasing products.

[0056] The word-of-mouth analysis unit can compare the opinions of users of different age groups and regions, and analyze fluctuations in word-of-mouth over a specific period of time. The word-of-mouth analysis unit, for example, uses generation AI to compare the opinions of users of different age groups and regions. For example, generation AI compares the opinions of young people and older people. Generation AI also compares opinions in urban and rural areas. Furthermore, generation AI analyzes fluctuations in word-of-mouth over a specific period of time. For example, generation AI analyzes fluctuations in word-of-mouth during a specific event period. This makes it possible to perform a multifaceted analysis by comparing opinions from different perspectives.

[0057] The review collection unit can evaluate the reliability of the collected reviews and prioritize collecting highly reliable reviews. The review collection unit evaluates the reliability of the collected reviews, for example, using a generation AI. For example, the generation AI analyzes the poster's account information and past posting history, and prioritizes collecting reviews from highly reliable accounts. The generation AI also analyzes the content of the reviews and prioritizes collecting reviews from highly reliable sources. For example, it prioritizes reviews from official websites and verified accounts. Furthermore, the generation AI measures the degree of match between the content of the reviews and other highly reliable sources, and prioritizes collecting reviews that match multiple highly reliable sources. This allows for the provision of more accurate information by prioritized collection of highly reliable reviews.

[0058] The review collection unit collects reviews by associating them with specific time periods or events, making it possible to understand fluctuations over time. The review collection unit, for example, uses a generation AI to collect reviews during specific events or campaign periods. For example, the generation AI concentrates on collecting reviews during sales periods. The generation AI also limits the review collection to specific time periods and analyzes fluctuations in reputation for each time period. For example, the generation AI compares reviews from daytime and nighttime to understand differences in reputation depending on the time of day. Furthermore, the generation AI collects reviews related to specific events and analyzes the impact of the event over time. For example, the generation AI collects fluctuations in reviews before and after a new product launch event. In this way, by collecting reviews related to specific time periods or events, it is possible to understand fluctuations over time.

[0059] The review collection unit can use the emotion estimation function to analyze the emotional tone of the collected reviews and prioritize collecting reviews that show strong emotional reactions. The review collection unit, for example, uses the emotion estimation function to analyze the emotional tone of the collected reviews. For example, the emotion estimation function prioritizes collecting reviews with high emotion scores. For example, it prioritizes very positive or negative reviews. The generation AI also uses the emotion estimation function to detect reviews that show strong emotional reactions in real time and prioritize collecting them. For example, it automatically collects reviews with emotion scores above a certain level. Furthermore, the emotion estimation function collects reviews with a specific emotional tone and analyzes emotional fluctuations. For example, it prioritizes collecting reviews with strong emotions such as anger or joy. This allows important opinions to be quickly identified by prioritized collection of reviews that show strong emotional reactions.

[0060] The word-of-mouth collection unit can also collect word-of-mouth in audio or video format and convert it into text using voice recognition technology or video analysis technology. The word-of-mouth collection unit, for example, uses generation AI to collect word-of-mouth in audio format and converts it into text using voice recognition technology. For example, the generation AI collects word-of-mouth from podcasts or audio messages. The generation AI also collects word-of-mouth in video format and converts the content into text using video analysis technology. For example, the generation AI extracts word-of-mouth from review videos on YouTube. Furthermore, the generation AI collects word-of-mouth in audio or video format and integrates it with text data for analysis. For example, the generation AI combines the content of the audio or video with the text data to comprehensively evaluate it. In this way, by collecting word-of-mouth in audio or video format, more diverse information can be analyzed.

[0061] The review collection unit can use the emotion estimation function to analyze the emotional tone of the collected reviews in real time and provide feedback to elicit positive emotions. The review collection unit, for example, uses the emotion estimation function to analyze the emotional tone of the collected reviews in real time. For example, the emotion estimation function provides feedback to elicit positive emotions. For example, the emotion estimation function automatically sends a thank you message for positive reviews. Furthermore, the generation AI uses the emotion estimation function to provide improvement suggestions for negative reviews in real time. For example, the generation AI suggests specific improvement measures for negative reviews. Furthermore, the emotion estimation function analyzes the emotional tone of the collected reviews and provides interactive feedback to elicit positive emotions. For example, the emotion estimation function automatically generates questions to elicit positive emotions. This makes it possible to improve user satisfaction by providing feedback to elicit positive emotions.

[0062] The review classification unit can analyze the content of reviews in detail and further classify them based on specific keywords and phrases. The review classification unit, for example, uses a generation AI to analyze the content of reviews in detail and further classify them based on specific keywords and phrases. For example, the generation AI classifies reviews into categories such as product quality, price, and design. The generation AI also analyzes the content of reviews and extracts and classifies keywords related to specific themes or topics. For example, the generation AI classifies reviews based on service response, delivery speed, after-sales service, etc. The generation AI also analyzes the content of reviews in detail and builds a system that further classifies them based on specific keywords and phrases. For example, the generation AI classifies reviews into positive opinions, negative opinions, and neutral opinions. This enables more precise analysis by analyzing the content of reviews in detail and further classifying them based on specific keywords and phrases.

[0063] The review classification unit takes into account the user's background information when classifying reviews, enabling more precise classification. The review classification unit, for example, uses a generation AI to take into account the user's background information when classifying reviews. For example, the generation AI considers background information such as the user's age, gender, and region when classifying. For example, the generation AI analyzes opinions of young and older users separately. The generation AI also analyzes differences in reputation by region. Furthermore, the generation AI builds a system that precisely classifies the content of reviews based on the user's background information. For example, the generation AI analyzes differences in opinions by gender. This allows for more precise analysis by classifying reviews taking into account the user's background information.

[0064] The review classification unit can use the emotion estimation function to analyze the emotional tone of the review and classify it based on the intensity and type of emotion. The review classification unit, for example, uses the emotion estimation function to analyze the emotional tone of the review. For example, the emotion estimation function classifies the review based on the intensity and type of emotion. For example, the emotion estimation function classifies into emotions such as joy, anger, and sadness. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional tone of the review in real time and classify it based on the intensity and type of emotion. For example, the generation AI classifies based on the emotion score. Furthermore, the emotion estimation function analyzes the emotional tone of the review in detail and builds a system that classifies it based on the intensity and type of emotion. For example, the emotion estimation function classifies into positive emotion, negative emotion, and neutral emotion. This allows for detailed analysis of emotional tone by classifying the review based on the intensity and type of emotion.

[0065] The review classification unit can visualize the content of the reviews and provide the classification results in a format that is visually easy to understand. The review classification unit visualizes the content of the reviews, for example, using a generation AI. For example, the generation AI displays the trends of reviews using graphs and charts. The generation AI also visualizes the classification results of the reviews so that the user can intuitively understand them. For example, the generation AI displays them using a word cloud or heat map. Furthermore, the generation AI visualizes the content of the reviews and builds a system that provides the classification results in a format that is visually easy to understand. For example, the generation AI provides an interactive dashboard. This makes it possible to provide the classification results in a format that is visually easy to understand by visualizing the content of the reviews.

[0066] The review classification unit can compare reviews from different industries and fields to identify similarities and differences. The review classification unit, for example, uses generation AI to compare reviews from different industries and fields. For example, the generation AI compares reviews from technology products and consumer goods. The generation AI also analyzes reviews from different industries to extract common problems and success factors. For example, the generation AI compares reviews from the service industry and the manufacturing industry. The generation AI also builds a system that compares reviews from different fields to identify similarities and differences. For example, the generation AI compares reviews from the medical field and the education field. This makes it possible to identify similarities and differences by comparing reviews from different industries and fields.

[0067] The review classification unit can use the emotion estimation function to analyze the emotional tone of the reviews in real time and prioritize displaying reviews that are likely to resonate emotionally. The review classification unit, for example, uses the emotion estimation function to analyze the emotional tone of the reviews in real time. For example, the emotion estimation function prioritizes displaying reviews that are likely to resonate emotionally. For example, the emotion estimation function prominently displays reviews with high positive emotion scores. Furthermore, the generation AI uses the emotion estimation function to detect reviews that are likely to resonate emotionally in real time and prioritize displaying them. For example, the generation AI automatically displays reviews with emotion scores above a certain level. Furthermore, the emotion estimation function analyzes the emotional tone of collected reviews and builds a system that prioritizes displaying reviews that are likely to resonate emotionally. For example, the emotion estimation function provides feedback to elicit positive emotions. This makes it easier to attract user attention by preferentially displaying reviews that are likely to resonate emotionally.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The review collector system can further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes the user's past purchase history to identify products and services that the user may be interested in. For example, it recommends products that are suitable for the user based on the category and price range of products purchased in the past. The purchase history analysis unit also analyzes the user's purchasing patterns to understand purchasing trends related to specific times and events. For example, it analyzes purchasing trends related to specific events such as Christmas and birthdays. Furthermore, the purchase history analysis unit can combine the user's purchase history with the content of reviews to make more personalized recommendations. For example, it can recommend new products that are suitable for the user based on reviews of products purchased in the past. In this way, analyzing the user's purchase history enables more accurate product recommendations.

[0070] The review collector system can further include a location information analysis unit that analyzes the user's location information. The location information analysis unit analyzes the user's current location and past movement history to understand local reputations. For example, it identifies products and services that are popular in a particular area. The location information analysis unit can also provide reviews of nearby stores and services based on the user's location information. For example, it can provide real-time reviews of stores close to the user's current location. Furthermore, the location information analysis unit can analyze the user's movement patterns to understand fluctuations in reputation related to specific areas or events. For example, it can analyze fluctuations in reputation at tourist spots and event venues. This makes it easier to understand local reputations by analyzing the user's location information.

[0071] The review collector system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes information about the user's friendships and followers on the social networking site to identify reliable reviews. For example, it prioritizes collecting reviews posted by the user's friends and followers. The social network analysis unit may also analyze the user's activity history on the social networking site to identify products and services that the user may be interested in. For example, it may recommend products related to posts that the user has "liked" or shared. The social network analysis unit may also analyze the user's influence on the social networking site to prioritize collecting reviews from influential users. For example, it may prioritize reviews from users with a high number of followers or a high engagement rate. This makes it easier to collect reliable reviews by analyzing the user's social network.

[0072] The review collector system can further estimate the user's emotions and adjust the display order of reviews based on the estimated emotions. For example, using the emotion estimation function, if the user has positive emotions, positive reviews can be displayed preferentially. Conversely, if the user has negative emotions, negative reviews can be displayed preferentially. Furthermore, the emotion estimation function can analyze the user's emotional fluctuations in real time and provide feedback according to the emotions. For example, if the user has negative emotions, an encouraging message can be displayed. In this way, adjusting the display order of reviews according to the user's emotions can improve user satisfaction.

[0073] The review collector system can further estimate the user's emotions and recommend products and services based on the estimated emotions. For example, using the emotion estimation function, if the user has positive emotions, it can recommend products that have received positive reviews. Also, if the user has negative emotions, it can recommend products and services that will improve the user's mood. Furthermore, the emotion estimation function can analyze the user's emotional fluctuations in real time and recommend products and services according to the user's emotions. For example, if the user is feeling stressed, it can recommend relaxation products. In this way, by recommending products and services according to the user's emotions, it is possible to improve user satisfaction.

[0074] The word-of-mouth collector system can further estimate the user's emotions and adjust the content of advertisements based on the estimated emotions. For example, using the emotion estimation function, if the user has positive emotions, positive advertisements can be displayed. On the other hand, if the user has negative emotions, advertisements designed to improve the user's mood can be displayed. Furthermore, the emotion estimation function can analyze the user's emotional fluctuations in real time and adjust the content of advertisements according to the emotions. For example, if the user is tired, advertisements for relaxation products and services can be displayed. In this way, the effectiveness of advertisements can be maximized by adjusting the content of advertisements according to the user's emotions.

[0075] The review collector system can further estimate the user's emotions and adjust customer support responses based on the estimated emotions. For example, using the emotion estimation function, if the user has negative emotions, a prompt and courteous response can be provided. Also, if the user has positive emotions, a thank you message can be sent. Furthermore, the emotion estimation function can analyze the user's emotional fluctuations in real time and adjust the customer support response according to the emotions. For example, if the user is dissatisfied, a specific solution can be proposed. In this way, by adjusting the customer support response according to the user's emotions, user satisfaction can be improved.

[0076] The review collector system can further estimate the user's emotions and provide feedback based on the estimated emotions. For example, using the emotion estimation function, if the user has positive emotions, positive feedback can be provided. On the other hand, if the user has negative emotions, suggestions for improvement can be provided. Furthermore, the emotion estimation function can analyze fluctuations in the user's emotions in real time and provide feedback according to the emotions. For example, if the user is dissatisfied, specific improvement measures can be suggested. In this way, by providing feedback according to the user's emotions, user satisfaction can be improved.

[0077] The word-of-mouth collector system can further estimate a user's purchasing intent and recommend products and services based on the estimated purchasing intent. For example, if a user has a high purchasing intent, the purchasing intent estimation function can actively recommend related products. Also, if a user has a low purchasing intent, special offers and discount information can be provided. Furthermore, the purchasing intent estimation function can analyze fluctuations in a user's purchasing intent in real time and recommend products and services according to their purchasing intent. For example, if a user shows a high interest in a particular product, accessories and options related to that product can be recommended. This can improve the purchase rate by recommending products and services according to the user's purchasing intent.

[0078] The review collector system can further include a search history analysis unit that analyzes a user's search history. The search history analysis unit analyzes a user's past search history to identify products and services that the user may be interested in. For example, it recommends products that are suitable for the user based on keywords and categories searched for in the past. The search history analysis unit also analyzes the user's search patterns to understand search trends related to specific times or events. For example, it analyzes search trends related to specific seasons or events. Furthermore, the search history analysis unit can combine the user's search history with the content of reviews to make more personalized recommendations. For example, it can recommend new products that are suitable for the user based on reviews of products searched for in the past. In this way, analyzing a user's search history enables more accurate product recommendations.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The review collection unit automatically collects reviews from various online platforms. For example, reviews are collected from online shopping sites, social media, blogs, bulletin boards, etc. The generation AI is then used to evaluate the reliability of the collected reviews and prioritize the collection of highly reliable reviews. Specifically, it analyzes the poster's account information and past posting history to prioritize the collection of reviews from highly reliable accounts. It also analyzes the content of the reviews and prioritizes reviews from official websites and verified accounts. The generation AI measures the degree of match between the content of the review and other highly reliable information sources, and prioritizes the collection of reviews that match multiple highly reliable information sources. Step 2: The review classification unit classifies the collected reviews. Using generative AI, the content of the reviews is analyzed and classified into positive, negative, and neutral opinions. Reviews are further classified based on specific keywords and phrases. For example, they can be classified into categories such as product quality, price, and design. Keywords related to specific themes and topics are then extracted and classified. For example, they can be classified into categories such as service response, delivery speed, and after-sales service. Step 3: The review analysis unit analyzes the classified reviews. Generative AI is used to create a report for the company based on the analysis results. The report contains detailed information about the reputation of the service or product. For example, the report summarizes the ratio of positive to negative comments, specific problems, and areas for improvement. It also provides reference information for individuals when purchasing products. For example, it provides information summarizing reviews about specific products and recommended products based on the ratings of other users.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

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

[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 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.

[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 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 review collection unit that automatically collects reviews from various platforms on the Internet, a word-of-mouth classification unit that classifies the word-of-mouth collected by the word-of-mouth collection unit; a word-of-mouth analysis unit that analyzes the word-of-mouth classified by the word-of-mouth classification unit. A system characterized by:

2. The word-of-mouth collection unit Collect reviews from online shopping sites, social media, blogs, and message boards 2. The system of claim 1.

3. The word-of-mouth classification unit Analyze the content of the reviews and categorize them into positive, negative, and neutral opinions.

2. The system of claim 1.

4. The word-of-mouth analysis unit Create reports for your business based on the analysis results 2. The system of claim 1.

5. The word-of-mouth analysis unit Providing reference information for individuals when purchasing products 2. The system of claim 1.

6. The word-of-mouth analysis unit Compare user opinions across different age groups and regions and analyze the fluctuations of said reviews over a specific period of time 2. The system of claim 1.

7. The word-of-mouth collection unit Evaluate the reliability of the collected reviews and prioritize collection of highly reliable reviews.

2. The system of claim 1.

8. The word-of-mouth collection unit Collect the reviews in relation to specific time periods or events to understand fluctuations over time.

2. The system of claim 1.

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