System

Abstract

An object of a system according to an embodiment is to allow a user to efficiently find an optimal product.SOLUTION: A system according to an embodiment includes a word-of-mouth summarizing unit and a recommended product introducing unit. The word-of-mouth summarizing unit collects word-of-mouth data and summarizes it. The recommended product introduction unit analyzes the past purchase history and browsing history of the user on the basis of the word-of-mouth data summarized by the word-of-mouth summarization unit, and introduces an optimum recommended product to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, reading reviews on e-commerce sites can be time-consuming, making it difficult to find the perfect product.

[0005] The system according to the embodiment aims to enable a user to efficiently find the most suitable product. [Means for solving the problem]

[0006] The system according to the embodiment includes a review summarization unit and a recommended product introduction unit. The review summarization unit collects and summarizes review data. The recommended product introduction unit analyzes the user's past purchase history and browsing history based on the review data summarized by the review summarization unit, and recommends products that are optimal for the user. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently find the most suitable product. [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 EC shopping support system according to the embodiment of the present invention utilizes AI technology to summarize reviews and recommend products, thereby improving the user's shopping experience.

[0029] The EC shopping support system according to the embodiment includes a review summarization unit and a recommended product introduction unit. The review summarization unit collects and summarizes review data. For example, the review summarization unit collects text-based review data, and a generation AI summarizes the content. The review summarization unit can also collect audio-based review data, and a generation AI can convert the audio data into text for summarization. The review summarization unit can also collect image-based review data, and a generation AI can analyze the images, convert them into text data, and summarize them. For example, the generation AI summarizes review data using a text generation AI (e.g., LLM). The generation AI can also summarize review data containing audio and images using a multimodal generation AI. The generation AI can also extract and summarize important points from the review data. The recommended product introduction unit analyzes a user's past purchase history and browsing history based on the review data summarized by the review summarization unit, and recommends products that are optimal for the user. For example, the recommended product introduction unit analyzes a user's past purchase history, and a generation AI suggests recommended products based on the user's interests. The recommended product introduction unit can also analyze the user's browsing history, and the generation AI can suggest products relevant to the user. The recommended product introduction unit can also analyze a combination of the user's purchase history and browsing history, and the generation AI can suggest optimal recommended products. For example, the generation AI uses an algorithm that suggests optimal products to the user based on the user's past purchase history and browsing history. This allows the EC shopping support system according to the embodiment to improve the user's shopping experience. For example, the output unit displays recommended products to the user through a web application or a mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results by email provides quick feedback by sending the results directly to the user.

[0030] The review summarization unit can evaluate the reliability of reviews and prioritize summarization of highly reliable reviews. For example, the generation AI evaluates the reliability of reviews and prioritizes summarization of highly reliable reviews. For example, a highly reliable review is summarized as "This product is very easy to use and has a great design," while a low reliability review is summarized as "This product has a short battery life." In addition, the review summarization unit evaluates the reliability of reviews and prioritizes summarization of highly reliable reviews. For example, a highly reliable review is summarized as "This product is very easy to use and has a great design," while a low reliability review is summarized as "This product has a short battery life." In addition, the review summarization unit evaluates the reliability of reviews and prioritizes summarization of highly reliable reviews. For example, a highly reliable review is summarized as "This product is very easy to use and has a great design," while a low reliability review is summarized as "This product has a short battery life." In this way, by prioritizing summarization of highly reliable reviews, users can obtain reliable information.

[0031] The review summarization unit has a function to read out the review summary aloud, making it possible to accommodate visually impaired people and users who are driving. For example, the review summarization unit adds a function whereby the generation AI reads out the review summary aloud. For example, it reads out loud, "This product is very easy to use and has a great design." The review summarization unit also adds a function whereby the generation AI reads out the review summary aloud. For example, it reads out loud, "This product is very easy to use and has a great design." The review summarization unit also adds a function whereby the generation AI reads out the review summary aloud. For example, it reads out loud, "This product is very easy to use and has a great design." This makes it possible to accommodate visually impaired people and users who are driving, making it possible for a wider range of users to use the service.

[0032] The review summarization unit automatically translates review summaries into different languages, making it possible to accommodate international users. For example, the review summarization unit uses a generation AI to automatically translate review summaries into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The review summarization unit also uses a generation AI to automatically translate review summaries into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The review summarization unit also uses a generation AI to automatically translate review summaries into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." This allows it to accommodate international users by automatically translating into different languages.

[0033] The recommended product introduction unit can introduce recommended products by reflecting the user's social media activities and interests. For example, the generation AI analyzes the user's social media activities and interests and introduces recommended products based on the results. For example, the unit introduces products that the user often talks about on social media. Also, the recommended product introduction unit can analyze the user's social media activities and interests and introduce recommended products based on the results. For example, the unit introduces products that the user often talks about on social media. Also, the recommended product introduction unit can analyze the user's social media activities and interests and introduce recommended products based on the results. For example, the unit introduces products that the user often talks about on social media. In this way, more relevant recommended products can be provided by reflecting the user's social media activities and interests.

[0034] The recommended product introduction unit can introduce recommended products taking into consideration seasons and events. For example, the generation AI considers seasons and events and introduces recommended products based on that. For example, Christmas-related products are introduced during the Christmas season. Also, the recommended product introduction unit can consider seasons and events and introduce recommended products based on that. For example, Christmas-related products are introduced during the Christmas season. Also, the recommended product introduction unit can consider seasons and events and introduce recommended products based on that. For example, Christmas-related products are introduced during the Christmas season. By taking seasons and events into consideration, it is possible to provide timely recommended products.

[0035] The recommended product introduction unit can introduce recommended products based on the purchase history and preferences of the user's friends and family. In the recommended product introduction unit, for example, the generation AI analyzes the purchase history and preferences of the user's friends and family and introduces recommended products based on the analysis. For example, it introduces products purchased by friends. In addition, the recommended product introduction unit can analyze the purchase history and preferences of the user's friends and family and introduce recommended products based on the analysis. For example, it introduces products purchased by friends. In addition, the recommended product introduction unit can analyze the purchase history and preferences of the user's friends and family and introduce recommended products based on the analysis. For example, it introduces products purchased by friends. This makes it possible to provide a more personalized shopping experience by introducing recommended products based on the purchase history and preferences of the user's friends and family.

[0036] The recommended product introduction unit can introduce recommended products based on the user's past reviews and ratings. In the recommended product introduction unit, for example, the generation AI analyzes the user's past reviews and ratings and introduces recommended products based on them. For example, it introduces products similar to products that the user has given a high rating. In addition, the recommended product introduction unit can analyze the user's past reviews and ratings and introduce recommended products based on them. For example, it introduces products similar to products that the user has given a high rating. In addition, the recommended product introduction unit can analyze the user's past reviews and ratings and introduce recommended products based on them. For example, it introduces products similar to products that the user has given a high rating. In this way, by introducing recommended products based on the user's past reviews and ratings, it is possible to provide more relevant recommended products.

[0037] The product comparison support unit is able to suggest optimal comparison points to the user by taking into account the user's past purchase history and browsing history. In the product comparison support unit, for example, the generation AI analyzes the user's past purchase history and browsing history and suggests optimal comparison points based on that. For example, it suggests comparison points for a new smartphone based on the specifications of smartphones the user has previously purchased. In addition, the product comparison support unit is able to suggest optimal comparison points to the user by analyzing the user's past purchase history and browsing history and suggest optimal comparison points based on that. For example, it suggests comparison points for a new smartphone based on the specifications of smartphones the user has previously purchased. In addition, the product comparison support unit is able to suggest optimal comparison points to the user by analyzing the user's past purchase history and browsing history and suggest optimal comparison points based on that. For example, it suggests comparison points for a new smartphone based on the specifications of smartphones the user has previously purchased. In this way, it is possible to suggest optimal comparison points by taking into account the user's past purchase history and browsing history.

[0038] The product comparison support unit can perform product comparisons that reflect the user's personal preferences and lifestyle. In the product comparison support unit, for example, the generation AI analyzes the user's personal preferences and lifestyle and performs product comparisons based on that. For example, if the user likes the outdoors, the product comparison support unit performs comparisons that emphasize waterproof performance and durability. In addition, the product comparison support unit can analyze the user's personal preferences and lifestyle and perform product comparisons based on that. For example, if the user likes the outdoors, the product comparison support unit performs comparisons that emphasize waterproof performance and durability. In addition, the product comparison support unit can analyze the user's personal preferences and lifestyle and perform product comparisons based on that. For example, if the user likes the outdoors, the product comparison support unit performs comparisons that emphasize waterproof performance and durability. In this way, by reflecting the user's personal preferences and lifestyle, it is possible to provide more personalized product comparisons.

[0039] The product comparison support unit displays the results of the product comparison as visual notes or infographics, making them easier to understand visually. In the product comparison support unit, for example, the generation AI displays the results of the product comparison as visual notes or infographics. For example, the specifications of smartphones are visually displayed using graphs or icons. In addition, the product comparison support unit displays the results of the product comparison as visual notes or infographics. For example, the specifications of smartphones are visually displayed using graphs or icons. In addition, the product comparison support unit displays the results of the product comparison as visual notes or infographics. For example, the specifications of smartphones are visually displayed using graphs or icons. In this way, the results of the product comparison are visually displayed, making them easier for the user to understand.

[0040] The product comparison support unit can automatically translate the results of product comparisons into different languages, making it possible to accommodate international users. In the product comparison support unit, for example, a generation AI automatically translates the results of product comparisons into different languages. For example, a smartphone spec comparison is translated into English and provided as a "Comparison of smartphone specs." In addition, the product comparison support unit can automatically translate the results of product comparisons into different languages. For example, a smartphone spec comparison is translated into English and provided as a "Comparison of smartphone specs." In addition, the product comparison support unit can automatically translate the results of product comparisons into different languages. For example, a smartphone spec comparison is translated into English and provided as a "Comparison of smartphone specs." In this way, by automatically translating the results of product comparisons into different languages, it is possible to accommodate international users.

[0041] The customer review analysis unit displays the results of the analysis of customer reviews as visual notes or infographics, making them easier to understand visually. In the customer review analysis unit, for example, the generation AI displays the results of the analysis of customer reviews as visual notes or infographics. For example, the strengths and weaknesses of a product are visually displayed using graphs or icons. In addition, the customer review analysis unit displays the results of the analysis of customer reviews as visual notes or infographics. For example, the strengths and weaknesses of a product are visually displayed using graphs or icons. In addition, the customer review analysis unit displays the results of the analysis of customer reviews as visual notes or infographics. For example, the strengths and weaknesses of a product are visually displayed using graphs or icons. In this way, the results of the analysis of customer reviews are visually displayed, making them easier for users to understand.

[0042] The customer review analysis unit automatically translates the analysis results of customer reviews into different languages, making it possible to accommodate international users. For example, the generation AI in the customer review analysis unit automatically translates the analysis results of customer reviews into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The generation AI also automatically translates the analysis results of customer reviews into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The generation AI also automatically translates the analysis results of customer reviews into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." This allows the analysis results of customer reviews to be automatically translated into different languages, making it possible to accommodate international users.

[0043] The post-purchase follow-up unit can customize the content of the follow-up by reflecting the user's social media activities and interests. In the post-purchase follow-up unit, for example, the generation AI analyzes the user's social media activities and interests and customizes the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user often talks about on social media is performed. In addition, the post-purchase follow-up unit can analyze the user's social media activities and interests and customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user often talks about on social media is performed. In addition, the post-purchase follow-up unit can analyze the user's social media activities and interests and customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user often talks about on social media is performed. In this way, more relevant follow-ups can be provided by reflecting the user's social media activities and interests.

[0044] The post-purchase follow-up unit can customize the content of the follow-up based on the purchase history and preferences of the user's friends and family. In the post-purchase follow-up unit, for example, the generation AI analyzes the purchase history and preferences of the user's friends and family and customizes the content of the follow-up based on that. For example, a follow-up related to a product purchased by a friend is performed. In addition, the post-purchase follow-up unit can customize the content of the follow-up based on that. For example, a follow-up related to a product purchased by a friend is performed. In addition, the post-purchase follow-up unit can analyze the purchase history and preferences of the user's friends and family and customize the content of the follow-up based on that. For example, a follow-up related to a product purchased by a friend is performed. In this way, by customizing the content of the follow-up based on the purchase history and preferences of the user's friends and family, it is possible to provide more relevant follow-ups.

[0045] The post-purchase follow-up unit can customize the content of the follow-up based on the user's past reviews and ratings. In the post-purchase follow-up unit, for example, the generation AI analyzes the user's past reviews and ratings and customizes the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user has given a high rating is performed. In addition, the post-purchase follow-up unit can customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user has given a high rating is performed. In addition, the post-purchase follow-up unit can customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user has given a high rating is performed. In this way, by customizing the content of the follow-up based on the user's past reviews and ratings, it is possible to provide more relevant follow-ups.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The EC shopping support system can further include an incentive providing unit to increase users' purchasing motivation. The incentive providing unit has a function of, for example, awarding points when a user purchases a specific product. The incentive providing unit can also provide discount coupons when a user makes a purchase of more than a certain amount. Furthermore, the incentive providing unit can also provide benefits to both the introducer and the introduced person when a user introduces a friend. This can increase users' purchasing motivation and increase the number of repeat customers.

[0048] The EC shopping support system may further include a prediction unit that predicts a user's purchasing behavior. The prediction unit may, for example, analyze the user's past purchase history and browsing history to predict the next product that the user is likely to purchase. The prediction unit may also suggest products that the user is likely to be interested in, taking into account seasons and trends. Furthermore, the prediction unit may analyze the user's social media activity to predict products that the user is likely to be interested in. This allows for more personalized product suggestions to be made to the user.

[0049] The EC shopping support system may further include an analysis unit that analyzes users' purchasing behavior. The analysis unit may, for example, analyze users' purchasing patterns and identify the time periods during which purchases are most common. The analysis unit may also analyze which devices users use to make purchases. Furthermore, the analysis unit may also identify which categories of products users frequently purchase. This allows for the optimization of marketing strategies and promotions tailored to users' purchasing behavior.

[0050] The EC shopping support system can further include a customer support section to improve the user's purchasing experience. The customer support section has a function to answer user questions in real time using, for example, a chatbot. The customer support section can also respond quickly when a user reports a problem with a product. Furthermore, the customer support section can also have a follow-up function to check whether the user is satisfied with the product after purchase. This can improve user satisfaction and increase repeat customers.

[0051] The EC shopping support system may further include a promotion section for encouraging users to make purchases. The promotion section may, for example, provide a limited-time discount on specific products. The promotion section may also provide additional discounts when a user purchases products in a specific category. Furthermore, the promotion section may provide benefits to both the introducer and the introduced user when the user introduces a friend. This may encourage users to make purchases and increase sales.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The review summarization unit collects and summarizes review data. For example, the review summarization unit collects review data in text format, and the generation AI summarizes its content. The review summarization unit can also collect review data in audio format, and the generation AI can convert the audio data into text and summarize it. Furthermore, the review summarization unit can collect review data with images, and the generation AI can analyze the images, convert them into text data, and summarize them. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to extract and summarize the important points of the review data. Step 2: The recommended product introduction unit analyzes the user's past purchase history and browsing history based on the review data summarized by the review summary unit, and recommends the most suitable products to the user. For example, the recommended product introduction unit uses generation AI to suggest recommended products based on the user's interests. It can also analyze a combination of the user's purchase history and browsing history to suggest the most suitable recommended products. The output unit displays the recommended products to the user via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending by email provides quick feedback by sending the results directly to the user.

[0054] (Example 2) The EC shopping support system according to the embodiment of the present invention utilizes AI technology to summarize reviews and recommend products, thereby improving the user's shopping experience.

[0055] The EC shopping support system according to the embodiment includes a review summarization unit and a recommended product introduction unit. The review summarization unit collects and summarizes review data. For example, the review summarization unit collects text-based review data, and a generation AI summarizes the content. The review summarization unit can also collect audio-based review data, and a generation AI can convert the audio data into text for summarization. The review summarization unit can also collect image-based review data, and a generation AI can analyze the images, convert them into text data, and summarize them. For example, the generation AI summarizes review data using a text generation AI (e.g., LLM). The generation AI can also summarize review data containing audio and images using a multimodal generation AI. The generation AI can also extract and summarize important points from the review data. The recommended product introduction unit analyzes a user's past purchase history and browsing history based on the review data summarized by the review summarization unit, and recommends products that are optimal for the user. For example, the recommended product introduction unit analyzes a user's past purchase history, and a generation AI suggests recommended products based on the user's interests. The recommended product introduction unit can also analyze the user's browsing history, and the generation AI can suggest products relevant to the user. The recommended product introduction unit can also analyze a combination of the user's purchase history and browsing history, and the generation AI can suggest optimal recommended products. For example, the generation AI uses an algorithm that suggests optimal products to the user based on the user's past purchase history and browsing history. This allows the EC shopping support system according to the embodiment to improve the user's shopping experience. For example, the output unit displays recommended products to the user through a web application or a mobile application. If the user desires feedback in paper form, the output unit prints the results using a printer. Sending the results by email provides quick feedback by sending the results directly to the user.

[0056] The review summarization unit performs sentiment analysis of reviews and can summarize positive and negative reviews separately. For example, the generation AI performs sentiment analysis of reviews and summarizes positive and negative reviews separately. For example, positive reviews are summarized as "This product is very easy to use and has a great design," and negative reviews are summarized as "This product has a short battery life." The review summarization unit also performs sentiment analysis of reviews and summarizes positive and negative reviews separately. For example, positive reviews are summarized as "This product is very easy to use and has a great design," and negative reviews are summarized as "This product has a short battery life." The review summarization unit also performs sentiment analysis of reviews and summarizes positive and negative reviews separately. For example, positive reviews are summarized as "This product is very easy to use and has a great design," and negative reviews are summarized as "This product has a short battery life." In this way, by summarizing positive and negative reviews separately, users can obtain more accurate information.

[0057] The review summarization unit can evaluate the reliability of reviews and prioritize summarization of highly reliable reviews. For example, the generation AI evaluates the reliability of reviews and prioritizes summarization of highly reliable reviews. For example, a highly reliable review is summarized as "This product is very easy to use and has a great design," while a low reliability review is summarized as "This product has a short battery life." In addition, the review summarization unit evaluates the reliability of reviews and prioritizes summarization of highly reliable reviews. For example, a highly reliable review is summarized as "This product is very easy to use and has a great design," while a low reliability review is summarized as "This product has a short battery life." In addition, the review summarization unit evaluates the reliability of reviews and prioritizes summarization of highly reliable reviews. For example, a highly reliable review is summarized as "This product is very easy to use and has a great design," while a low reliability review is summarized as "This product has a short battery life." In this way, by prioritizing summarization of highly reliable reviews, users can obtain reliable information.

[0058] The review summarization unit uses the emotion estimation function to generate summaries of reviews that interest the user most, and can provide summaries based on the user's emotions. The review summarization unit, for example, uses the emotion estimation function to generate summaries of reviews that interest the user most. For example, reviews in which the user has positive emotions are prioritized to be summarized and provided as, "This product is very easy to use and has a great design." The review summarization unit also uses the emotion estimation function to generate summaries of reviews in which the user has most interest. For example, reviews in which the user has positive emotions are prioritized to be summarized and provided as, "This product is very easy to use and has a great design." The review summarization unit also uses the emotion estimation function to generate summaries of reviews in which the user has most interest. For example, reviews in which the user has positive emotions are prioritized to be summarized and provided as, "This product is very easy to use and has a great design." By providing summaries based on the user's emotions, it is possible to obtain information that the user is more interested in.

[0059] The review summarization unit has a function to read out the review summary aloud, making it possible to accommodate visually impaired people and users who are driving. For example, the review summarization unit adds a function whereby the generation AI reads out the review summary aloud. For example, it reads out loud, "This product is very easy to use and has a great design." The review summarization unit also adds a function whereby the generation AI reads out the review summary aloud. For example, it reads out loud, "This product is very easy to use and has a great design." The review summarization unit also adds a function whereby the generation AI reads out the review summary aloud. For example, it reads out loud, "This product is very easy to use and has a great design." This makes it possible to accommodate visually impaired people and users who are driving, making it possible for a wider range of users to use the service.

[0060] The review summarization unit automatically translates review summaries into different languages, making it possible to accommodate international users. For example, the review summarization unit uses a generation AI to automatically translate review summaries into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The review summarization unit also uses a generation AI to automatically translate review summaries into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The review summarization unit also uses a generation AI to automatically translate review summaries into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." This allows it to accommodate international users by automatically translating into different languages.

[0061] The review summarization unit uses the emotion estimation function to collect emotional reactions when users read the summaries, thereby improving the accuracy of the summaries. The review summarization unit, for example, uses the emotion estimation function to collect emotional reactions when users read the summaries. For example, summaries in which the user has positive emotions are preferentially provided. The review summarization unit also uses the emotion estimation function to collect emotional reactions when users read the summaries. For example, summaries in which the user has positive emotions are preferentially provided. The review summarization unit also uses the emotion estimation function to collect emotional reactions when users read the summaries. For example, summaries in which the user has positive emotions are preferentially provided. In this way, by collecting users' emotional reactions, the accuracy of the summaries can be improved.

[0062] The recommended product introduction unit can estimate the user's current mood and emotions and recommend products based on that. In the recommended product introduction unit, for example, the generation AI estimates the user's current mood and emotions and recommends recommended products based on that. For example, if the user feels like relaxing, relaxation goods are recommended. In addition, the recommended product introduction unit estimates the user's current mood and emotions and recommends recommended products based on that. For example, if the user feels like relaxing, relaxation goods are recommended. In addition, the recommended product introduction unit estimates the user's current mood and emotions and recommends recommended products based on that. For example, if the user feels like relaxing, relaxation goods are recommended. In this way, by recommending recommended products based on the user's current mood and emotions, a more personalized shopping experience can be provided.

[0063] The recommended product introduction unit can introduce recommended products by reflecting the user's social media activities and interests. For example, the generation AI analyzes the user's social media activities and interests and introduces recommended products based on the results. For example, the unit introduces products that the user often talks about on social media. Also, the recommended product introduction unit can analyze the user's social media activities and interests and introduce recommended products based on the results. For example, the unit introduces products that the user often talks about on social media. Also, the recommended product introduction unit can analyze the user's social media activities and interests and introduce recommended products based on the results. For example, the unit introduces products that the user often talks about on social media. In this way, more relevant recommended products can be provided by reflecting the user's social media activities and interests.

[0064] The recommended product introduction unit can introduce recommended products taking into consideration seasons and events. For example, the generation AI considers seasons and events and introduces recommended products based on that. For example, Christmas-related products are introduced during the Christmas season. Also, the recommended product introduction unit can consider seasons and events and introduce recommended products based on that. For example, Christmas-related products are introduced during the Christmas season. Also, the recommended product introduction unit can consider seasons and events and introduce recommended products based on that. For example, Christmas-related products are introduced during the Christmas season. By taking seasons and events into consideration, it is possible to provide timely recommended products.

[0065] The recommended product introduction unit can introduce recommended products based on the purchase history and preferences of the user's friends and family. In the recommended product introduction unit, for example, the generation AI analyzes the purchase history and preferences of the user's friends and family and introduces recommended products based on the analysis. For example, it introduces products purchased by friends. In addition, the recommended product introduction unit can analyze the purchase history and preferences of the user's friends and family and introduce recommended products based on the analysis. For example, it introduces products purchased by friends. In addition, the recommended product introduction unit can analyze the purchase history and preferences of the user's friends and family and introduce recommended products based on the analysis. For example, it introduces products purchased by friends. This makes it possible to provide a more personalized shopping experience by introducing recommended products based on the purchase history and preferences of the user's friends and family.

[0066] The recommended product introduction unit can introduce recommended products based on the user's past reviews and ratings. In the recommended product introduction unit, for example, the generation AI analyzes the user's past reviews and ratings and introduces recommended products based on them. For example, it introduces products similar to products that the user has given a high rating. In addition, the recommended product introduction unit can analyze the user's past reviews and ratings and introduce recommended products based on them. For example, it introduces products similar to products that the user has given a high rating. In addition, the recommended product introduction unit can analyze the user's past reviews and ratings and introduce recommended products based on them. For example, it introduces products similar to products that the user has given a high rating. In this way, by introducing recommended products based on the user's past reviews and ratings, it is possible to provide more relevant recommended products.

[0067] The recommended product introduction unit can use the emotion estimation function to collect the user's emotional reactions to the introduced products and reflect them in the next recommendation. The recommended product introduction unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the introduced products. For example, it prioritizes introducing products for which the user has positive emotions. The recommended product introduction unit also uses the emotion estimation function to collect the user's emotional reactions to the introduced products. For example, it prioritizes introducing products for which the user has positive emotions. The recommended product introduction unit also uses the emotion estimation function to collect the user's emotional reactions to the introduced products. For example, it prioritizes introducing products for which the user has positive emotions. In this way, by collecting the user's emotional reactions, it is possible to reflect them in the next recommendation and provide more personalized recommended products.

[0068] The product comparison support unit is able to suggest optimal comparison points to the user by taking into account the user's past purchase history and browsing history. In the product comparison support unit, for example, the generation AI analyzes the user's past purchase history and browsing history and suggests optimal comparison points based on that. For example, it suggests comparison points for a new smartphone based on the specifications of smartphones the user has previously purchased. In addition, the product comparison support unit is able to suggest optimal comparison points to the user by analyzing the user's past purchase history and browsing history and suggest optimal comparison points based on that. For example, it suggests comparison points for a new smartphone based on the specifications of smartphones the user has previously purchased. In addition, the product comparison support unit is able to suggest optimal comparison points to the user by analyzing the user's past purchase history and browsing history and suggest optimal comparison points based on that. For example, it suggests comparison points for a new smartphone based on the specifications of smartphones the user has previously purchased. In this way, it is possible to suggest optimal comparison points by taking into account the user's past purchase history and browsing history.

[0069] The product comparison support unit can perform product comparisons that reflect the user's personal preferences and lifestyle. In the product comparison support unit, for example, the generation AI analyzes the user's personal preferences and lifestyle and performs product comparisons based on that. For example, if the user likes the outdoors, the product comparison support unit performs comparisons that emphasize waterproof performance and durability. In addition, the product comparison support unit can analyze the user's personal preferences and lifestyle and perform product comparisons based on that. For example, if the user likes the outdoors, the product comparison support unit performs comparisons that emphasize waterproof performance and durability. In addition, the product comparison support unit can analyze the user's personal preferences and lifestyle and perform product comparisons based on that. For example, if the user likes the outdoors, the product comparison support unit performs comparisons that emphasize waterproof performance and durability. In this way, by reflecting the user's personal preferences and lifestyle, it is possible to provide more personalized product comparisons.

[0070] The product comparison support unit can use the emotion estimation function to highlight the comparison points that the user is most interested in. For example, the product comparison support unit uses the emotion estimation function to highlight the comparison points that the user is most interested in. For example, if the user is price sensitive, price comparisons are emphasized. Also, the product comparison support unit uses the emotion estimation function to highlight the comparison points that the user is most interested in. For example, if the user is price sensitive, price comparisons are emphasized. Also, the product comparison support unit uses the emotion estimation function to highlight the comparison points that the user is most interested in. For example, if the user is price sensitive, price comparisons are emphasized. In this way, by highlighting the comparison points that the user is most interested in, more effective product comparisons can be provided.

[0071] The product comparison support unit displays the results of the product comparison as visual notes or infographics, making them easier to understand visually. In the product comparison support unit, for example, the generation AI displays the results of the product comparison as visual notes or infographics. For example, the specifications of smartphones are visually displayed using graphs or icons. In addition, the product comparison support unit displays the results of the product comparison as visual notes or infographics. For example, the specifications of smartphones are visually displayed using graphs or icons. In addition, the product comparison support unit displays the results of the product comparison as visual notes or infographics. For example, the specifications of smartphones are visually displayed using graphs or icons. In this way, the results of the product comparison are visually displayed, making them easier for the user to understand.

[0072] The product comparison support unit can automatically translate the results of product comparisons into different languages, making it possible to accommodate international users. In the product comparison support unit, for example, a generation AI automatically translates the results of product comparisons into different languages. For example, a smartphone spec comparison is translated into English and provided as a "Comparison of smartphone specs." In addition, the product comparison support unit can automatically translate the results of product comparisons into different languages. For example, a smartphone spec comparison is translated into English and provided as a "Comparison of smartphone specs." In addition, the product comparison support unit can automatically translate the results of product comparisons into different languages. For example, a smartphone spec comparison is translated into English and provided as a "Comparison of smartphone specs." In this way, by automatically translating the results of product comparisons into different languages, it is possible to accommodate international users.

[0073] The product comparison support unit uses the emotion estimation function to collect emotional reactions when the user views the comparison results, thereby improving the accuracy of the comparison. The product comparison support unit, for example, uses the emotion estimation function to collect emotional reactions when the user views the comparison results. For example, it preferentially provides comparison results for which the user has positive emotions. The product comparison support unit also uses the emotion estimation function to collect emotional reactions when the user views the comparison results. For example, it preferentially provides comparison results for which the user has positive emotions. The product comparison support unit also uses the emotion estimation function to collect emotional reactions when the user views the comparison results. For example, it preferentially provides comparison results for which the user has positive emotions. In this way, by collecting the user's emotional reactions, the accuracy of the comparison can be improved.

[0074] The customer review analysis unit performs sentiment analysis of customer reviews and can analyze them separately into positive and negative reviews. For example, the generation AI in the customer review analysis unit performs sentiment analysis of customer reviews and analyzes them separately into positive and negative reviews. For example, a positive review may be analyzed as "This product is very easy to use and has a great design," while a negative review may be analyzed as "This product has a short battery life." The generation AI in the customer review analysis unit also performs sentiment analysis of customer reviews and analyzes them separately into positive and negative reviews. For example, a positive review may be analyzed as "This product is very easy to use and has a great design," while a negative review may be analyzed as "This product has a short battery life." The generation AI in the customer review analysis unit also performs sentiment analysis of customer reviews and analyzes them separately into positive and negative reviews. For example, a positive review may be analyzed as "This product is very easy to use and has a great design," while a negative review may be analyzed as "This product has a short battery life." This allows users to obtain more accurate information by analyzing positive and negative reviews separately.

[0075] The customer review analysis unit can use the emotion estimation function to provide analysis results of reviews that the user is most interested in. The customer review analysis unit, for example, uses the emotion estimation function to provide analysis results of reviews that the user is most interested in. For example, reviews that the user has positive emotions about are provided preferentially. The customer review analysis unit also uses the emotion estimation function to provide analysis results of reviews that the user is most interested in. For example, reviews that the user has positive emotions about are provided preferentially. The customer review analysis unit also uses the emotion estimation function to provide analysis results of reviews that the user is most interested in. For example, reviews that the user has positive emotions about are provided preferentially. This allows for more effective information to be provided by providing analysis results of reviews that the user is most interested in.

[0076] The customer review analysis unit displays the results of the analysis of customer reviews as visual notes or infographics, making them easier to understand visually. In the customer review analysis unit, for example, the generation AI displays the results of the analysis of customer reviews as visual notes or infographics. For example, the strengths and weaknesses of a product are visually displayed using graphs or icons. In addition, the customer review analysis unit displays the results of the analysis of customer reviews as visual notes or infographics. For example, the strengths and weaknesses of a product are visually displayed using graphs or icons. In addition, the customer review analysis unit displays the results of the analysis of customer reviews as visual notes or infographics. For example, the strengths and weaknesses of a product are visually displayed using graphs or icons. In this way, the results of the analysis of customer reviews are visually displayed, making them easier for users to understand.

[0077] The customer review analysis unit automatically translates the analysis results of customer reviews into different languages, making it possible to accommodate international users. For example, the generation AI in the customer review analysis unit automatically translates the analysis results of customer reviews into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The generation AI also automatically translates the analysis results of customer reviews into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." The generation AI also automatically translates the analysis results of customer reviews into different languages. For example, it translates "This product is very easy to use and has a great design" into English and provides it as "This product is very easy to use and has a great design." This allows the analysis results of customer reviews to be automatically translated into different languages, making it possible to accommodate international users.

[0078] The customer review analysis unit uses the emotion estimation function to collect emotional reactions when users view the analysis results, thereby improving the accuracy of the analysis. The customer review analysis unit, for example, uses the emotion estimation function to collect emotional reactions when users view the analysis results. For example, it prioritizes providing analysis results in which the user has positive emotions. The customer review analysis unit also uses the emotion estimation function to collect emotional reactions when users view the analysis results. For example, it prioritizes providing analysis results in which the user has positive emotions. The customer review analysis unit also uses the emotion estimation function to collect emotional reactions when users view the analysis results. For example, it prioritizes providing analysis results in which the user has positive emotions. In this way, by collecting users' emotional reactions, the accuracy of the analysis can be improved.

[0079] The post-purchase follow-up unit can estimate the user's current mood and emotions and customize the content of the follow-up based on that. In the post-purchase follow-up unit, for example, the generation AI estimates the user's current mood and emotions and customizes the content of the follow-up based on that. For example, if the user feels like relaxing, a follow-up of relaxation goods is provided. In addition, the post-purchase follow-up unit can estimate the user's current mood and emotions and customize the content of the follow-up based on that. For example, if the user feels like relaxing, a follow-up of relaxation goods is provided. In addition, the post-purchase follow-up unit can estimate the user's current mood and emotions and customize the content of the follow-up based on that. For example, if the user feels like relaxing, a follow-up of relaxation goods is provided. In this way, by customizing the content of the follow-up based on the user's current mood and emotions, more effective follow-up can be provided.

[0080] The post-purchase follow-up unit can customize the content of the follow-up by reflecting the user's social media activities and interests. In the post-purchase follow-up unit, for example, the generation AI analyzes the user's social media activities and interests and customizes the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user often talks about on social media is performed. In addition, the post-purchase follow-up unit can analyze the user's social media activities and interests and customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user often talks about on social media is performed. In addition, the post-purchase follow-up unit can analyze the user's social media activities and interests and customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user often talks about on social media is performed. In this way, more relevant follow-ups can be provided by reflecting the user's social media activities and interests.

[0081] The post-purchase follow-up unit uses the emotion estimation function to collect emotional responses when the user receives a follow-up and can reflect them in the next follow-up. The post-purchase follow-up unit, for example, uses the emotion estimation function to collect emotional responses when the user receives a follow-up. For example, it prioritizes providing follow-ups in which the user has positive emotions. The post-purchase follow-up unit also uses the emotion estimation function to collect emotional responses when the user receives a follow-up. For example, it prioritizes providing follow-ups in which the user has positive emotions. The post-purchase follow-up unit also uses the emotion estimation function to collect emotional responses when the user receives a follow-up. For example, it prioritizes providing follow-ups in which the user has positive emotions. In this way, by collecting the user's emotional responses, it is possible to reflect them in the next follow-up and provide a more effective follow-up.

[0082] The post-purchase follow-up unit can customize the content of the follow-up based on the purchase history and preferences of the user's friends and family. In the post-purchase follow-up unit, for example, the generation AI analyzes the purchase history and preferences of the user's friends and family and customizes the content of the follow-up based on that. For example, a follow-up related to a product purchased by a friend is performed. In addition, the post-purchase follow-up unit can customize the content of the follow-up based on that. For example, a follow-up related to a product purchased by a friend is performed. In addition, the post-purchase follow-up unit can analyze the purchase history and preferences of the user's friends and family and customize the content of the follow-up based on that. For example, a follow-up related to a product purchased by a friend is performed. In this way, by customizing the content of the follow-up based on the purchase history and preferences of the user's friends and family, it is possible to provide more relevant follow-ups.

[0083] The post-purchase follow-up unit can customize the content of the follow-up based on the user's past reviews and ratings. In the post-purchase follow-up unit, for example, the generation AI analyzes the user's past reviews and ratings and customizes the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user has given a high rating is performed. In addition, the post-purchase follow-up unit can customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user has given a high rating is performed. In addition, the post-purchase follow-up unit can customize the content of the follow-up based on the analysis. For example, a follow-up related to a product that the user has given a high rating is performed. In this way, by customizing the content of the follow-up based on the user's past reviews and ratings, it is possible to provide more relevant follow-ups.

[0084] The post-purchase follow-up unit uses the emotion estimation function to collect emotional responses when the user receives a follow-up and can reflect them in the next follow-up. The post-purchase follow-up unit, for example, uses the emotion estimation function to collect emotional responses when the user receives a follow-up. For example, it prioritizes providing follow-ups in which the user has positive emotions. The post-purchase follow-up unit also uses the emotion estimation function to collect emotional responses when the user receives a follow-up. For example, it prioritizes providing follow-ups in which the user has positive emotions. The post-purchase follow-up unit also uses the emotion estimation function to collect emotional responses when the user receives a follow-up. For example, it prioritizes providing follow-ups in which the user has positive emotions. In this way, by collecting the user's emotional responses, it is possible to reflect them in the next follow-up and provide a more effective follow-up.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The EC shopping support system can further include an incentive providing unit to increase users' purchasing motivation. The incentive providing unit has a function of, for example, awarding points when a user purchases a specific product. The incentive providing unit can also provide discount coupons when a user makes a purchase of more than a certain amount. Furthermore, the incentive providing unit can also provide benefits to both the introducer and the introduced person when a user introduces a friend. This can increase users' purchasing motivation and increase the number of repeat customers.

[0087] The EC shopping support system may further include a prediction unit that predicts a user's purchasing behavior. The prediction unit may, for example, analyze the user's past purchase history and browsing history to predict the next product that the user is likely to purchase. The prediction unit may also suggest products that the user is likely to be interested in, taking into account seasons and trends. Furthermore, the prediction unit may analyze the user's social media activity to predict products that the user is likely to be interested in. This allows for more personalized product suggestions to be made to the user.

[0088] The EC shopping support system may further include an analysis unit that analyzes users' purchasing behavior. The analysis unit may, for example, analyze users' purchasing patterns and identify the time periods during which purchases are most common. The analysis unit may also analyze which devices users use to make purchases. Furthermore, the analysis unit may also identify which categories of products users frequently purchase. This allows for the optimization of marketing strategies and promotions tailored to users' purchasing behavior.

[0089] The EC shopping support system can further include a customer support section to improve the user's purchasing experience. The customer support section has a function to answer user questions in real time using, for example, a chatbot. The customer support section can also respond quickly when a user reports a problem with a product. Furthermore, the customer support section can also have a follow-up function to check whether the user is satisfied with the product after purchase. This can improve user satisfaction and increase repeat customers.

[0090] The EC shopping support system may further include a promotion section for encouraging users to make purchases. The promotion section may, for example, provide a limited-time discount on specific products. The promotion section may also provide additional discounts when a user purchases products in a specific category. Furthermore, the promotion section may provide benefits to both the introducer and the introduced user when the user introduces a friend. This may encourage users to make purchases and increase sales.

[0091] The EC shopping support system may further include a suggestion unit that estimates the user's emotions and makes customized product suggestions based on the estimated emotions. For example, the suggestion unit may suggest relaxation goods when the user is feeling stressed. The suggestion unit may also suggest celebratory gifts when the user is happy. Furthermore, the suggestion unit may also suggest refreshing items when the user is tired. This makes it possible to make product suggestions based on the user's emotions and provide a more personalized shopping experience.

[0092] The EC shopping support system may further include a message unit that estimates the user's emotions and transmits a customized message based on the estimated emotions. For example, the message unit may transmit an encouraging message when the user is sad. The message unit may also transmit a congratulatory message when the user is happy. Furthermore, the message unit may transmit a message encouraging the user to relax when the user is feeling stressed. This allows the system to transmit a message based on the user's emotions and increase engagement with the user.

[0093] The EC shopping support system may further include an advertising unit that estimates the user's emotions and displays customized advertisements based on the estimated emotions. For example, if the user feels like relaxing, the advertising unit may display advertisements for relaxation goods. If the user feels like being active, the advertising unit may also display advertisements for sports goods. Furthermore, if the user feels tired, the advertising unit may display advertisements for refreshment items. This allows advertisements to be displayed based on the user's emotions, thereby increasing the effectiveness of the advertisements.

[0094] The EC shopping support system may further include a feedback unit that estimates the user's emotions and provides customized feedback based on the estimated emotions. For example, the feedback unit may provide feedback encouraging further purchases when the user has positive emotions. The feedback unit may also provide support for problem resolution when the user has negative emotions. Furthermore, the feedback unit may suggest products that may interest the user when the user has neutral emotions. This allows for providing feedback based on the user's emotions and improves user satisfaction.

[0095] The EC shopping support system may further include a reminder unit that estimates the user's emotions and sends customized reminders based on the estimated emotions. For example, the reminder unit may send a reminder for relaxation goods when the user is busy. The reminder unit may also send a reminder for important products when the user tends to forget. Furthermore, the reminder unit may also send a reminder for related products when the user is preparing for a specific event. This allows the system to send reminders based on the user's emotions and support the user's purchasing behavior.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The review summarization unit collects and summarizes review data. For example, the review summarization unit collects review data in text format, and the generation AI summarizes its content. The review summarization unit can also collect review data in audio format, and the generation AI can convert the audio data into text and summarize it. Furthermore, the review summarization unit can collect review data with images, and the generation AI can analyze the images, convert them into text data, and summarize them. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to extract and summarize the important points of the review data. Step 2: The recommended product introduction unit analyzes the user's past purchase history and browsing history based on the review data summarized by the review summary unit, and recommends the most suitable products to the user. For example, the recommended product introduction unit uses generation AI to suggest recommended products based on the user's interests. It can also analyze a combination of the user's purchase history and browsing history to suggest the most suitable recommended products. The output unit displays the recommended products to the user via a web application or mobile application. If feedback is desired in paper form, the results are printed using a printer. Sending by email provides quick feedback by sending the results directly to the user.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 summarization unit that collects and summarizes review data; and a recommended product introduction unit that analyzes the user's past purchase history and browsing history based on the word-of-mouth data summarized by the word-of-mouth summary unit and introduces the most suitable recommended products to the user. A system characterized by:

2. The review summary section Conduct sentiment analysis of reviews and summarize positive and negative reviews separately 2. The system of claim 1.

3. The review summary section Evaluating the credibility of reviews and prioritizing summarizing the most credible reviews 2. The system of claim 1.

4. The review summary section Generate a summary of reviews that interest the user most and provide a summary based on the user's sentiment 2. The system of claim 1.

5. The review summary section It also has a feature that reads review summaries aloud, making it suitable for visually impaired users and those driving.

2. The system of claim 1.

6. The review summary section Automatically translate review summaries into different languages ​​to accommodate international users 2. The system of claim 1.

Images