System

Abstract

An object of a system according to an embodiment is to clarify a reason why a product is recommended to a user and to increase purchase motivation.SOLUTION: A system according to an embodiment includes a reason generation unit, an image generation unit, and a proposal unit. The reason generation unit analyzes the user's past purchase history, browsing history, and profile information, and generates a "reason for recommending the product" in accordance with individual user needs. The image generation unit generates a usage scene image according to the user's needs based on the reason generated by the reason generation unit. The suggestion unit suggests a "more recommended product" or a "product recommended to be used together" from the product being displayed, together with the using scene image generated by the image generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies lack explanations of why products are recommended to users in e-commerce, and there is room for improvement in terms of increasing their motivation to purchase.

[0005] The system according to the embodiment aims to clarify the reasons for recommending a product to a user and increase the user's willingness to purchase the product. [Means for solving the problem]

[0006] The system according to the embodiment includes a reason generation unit, an image generation unit, and a suggestion unit. The reason generation unit analyzes a user's past purchase history, browsing history, and profile information, and generates "reasons for recommending a product" tailored to individual user needs. The image generation unit generates a usage scene image tailored to the user's needs based on the reason generated by the reason generation unit. The suggestion unit suggests "more recommended products" and "products recommended for use together" with the currently displayed product, along with the usage scene image generated by the image generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can clarify the reason why a product is recommended to a user, thereby increasing the user's desire to purchase the 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 e-commerce system according to the embodiment of the present invention is a system that individually explains the reasons for recommending a product to a user, provides visual images of usage scenarios, and suggests related products, thereby providing the user with a shopping experience that leaves them satisfied and satisfied.

[0029] An e-commerce system according to an embodiment includes a reason generation unit, an image generation unit, and a suggestion unit. The reason generation unit analyzes a user's past purchase history, browsing history, and profile information to generate "reasons for recommending a product" tailored to individual user needs. For example, the reason generation unit may explain, "This product is recommended based on your past purchase history," based on the user's past purchases and browsing. The reason generation unit may also generate recommendation reasons tailored to the user's interests and concerns based on the user's profile information. The image generation unit generates usage scene images tailored to the user's needs based on the reasons generated by the reason generation unit. For example, if a user is purchasing outdoor equipment, the image generation unit generates a scene in which the product is being used and displays it to the user. The image generation unit may also generate different scenes tailored to the user's needs. The suggestion unit, along with the usage scene image generated by the image generation unit, suggests "more recommended products" or "products recommended for use together" with the currently displayed product. For example, if a user is browsing cameras, the suggestion unit may suggest lenses and accessories that go well with the camera. The suggestion unit can also suggest related products based on the user's past purchase history and browsing history. This allows the e-commerce system according to the embodiment to provide the user with a shopping experience that is convincing and satisfying. For example, the suggestion unit can explain the reason for recommending a product based on the user's past purchases and visually visualize how the product will be used after purchase, thereby increasing the user's motivation to purchase. Furthermore, suggesting related products can further enhance the user's purchasing experience.

[0030] The reason generation unit can analyze the user's real-time behavioral data and generate the optimal recommendation reason on the spot. The reason generation unit, for example, analyzes the page the user is currently viewing and click history in real time and generates the optimal recommendation reason based on that data. For example, if the user frequently clicks on products from a specific brand, it generates a reason to recommend products from that brand. The reason generation unit can also analyze the user's stay time and scrolling behavior, and generate recommendation reasons that reflect the user's interests. This makes it possible to analyze the user's real-time behavioral data and generate the optimal recommendation reason on the spot.

[0031] The reason generation unit can analyze a user's social media activity and generate a recommendation reason that reflects the user's interests and concerns. The reason generation unit, for example, analyzes a user's social media activity and generates a recommendation reason that reflects the user's interests and concerns. For example, if a user frequently mentions a particular brand or product on social media, the reason generation unit generates a reason to recommend that brand or product. The reason generation unit can also analyze the user's number of followers and number of likes, and generate a recommendation reason that takes the user's influence into consideration. This makes it possible to analyze a user's social media activity and generate a recommendation reason that reflects the user's interests and concerns.

[0032] The reason generation unit can analyze reviews and ratings from other users and generate a recommendation reason based on the results. The reason generation unit, for example, analyzes reviews and ratings from other users and generates a recommendation reason based on the results. For example, if a specific product has received high ratings, the recommendation reason is generated based on the ratings. The reason generation unit can also take into account the content of the reviews and the reliability of the ratings to generate a recommendation reason that is reliable for the user. This makes it possible to analyze reviews and ratings from other users and generate a recommendation reason based on the results.

[0033] The image generation unit can analyze the user's past photos and image data and generate the usage scene image that is most relevant to each individual user. The image generation unit, for example, analyzes the user's past photos and image data and generates the usage scene image that is most relevant to each individual user. For example, the image generation unit generates a usage scene image of outdoor equipment based on outdoor photos taken by the user in the past. The image generation unit can also generate a more relevant usage scene image by taking into account the date, time, and location of the user's photo. This allows the user's past photos and image data to be analyzed and the usage scene image that is most relevant to each individual user.

[0034] The image generation unit can generate an optimal usage scene image by taking into account the season and weather information. The image generation unit generates an optimal usage scene image by taking into account, for example, seasonal information. For example, in winter, a usage scene image is generated that shows warm clothing and a winter landscape in the background. The image generation unit can also generate usage scene images that are suitable for sunny days and rainy days by taking into account weather information. In this way, an optimal usage scene image can be generated by taking into account the season and weather information.

[0035] The image generation unit can display the usage scene image using a 3D model or AR technology, providing the user with an experience of actually using the product. For example, the image generation unit can display the usage scene image as a 3D model, allowing the user to view the product from 360 degrees. For example, the image generation unit can simulate furniture placement using a 3D model. The image generation unit can also use AR technology to provide the user with an experience of actually using the product. For example, the AR technology can allow the user to place the product in an actual environment via a smartphone. This allows the usage scene image to be displayed using a 3D model or AR technology, providing the user with an experience of actually using the product.

[0036] The image generation unit can analyze usage scene images posted by other users and generate new usage scene images based on the analysis. For example, the image generation unit can analyze usage scene images posted by other users and generate new usage scene images based on the analysis. For example, a similar usage scene image can be generated based on an outdoor photo posted by a user. The image generation unit can also generate more reliable usage scene images by taking into account the content and ratings posted by other users. This allows the image generation unit to analyze usage scene images posted by other users and generate new usage scene images based on the analysis.

[0037] The suggestion unit can analyze the user's purchase history, browsing history, and search history to suggest optimal related products. The suggestion unit, for example, analyzes the user's purchase history, browsing history, and search history to suggest optimal related products. For example, it can suggest products related to products the user has previously purchased. The suggestion unit can also consider the user's search keywords and search frequency to suggest related products that match the user's interests. This makes it possible to analyze the user's purchase history, browsing history, and search history to suggest optimal related products.

[0038] The suggestion unit, when suggesting related products, can suggest them as bundled products or set products, thereby increasing added value for the user. For example, when suggesting related products, the suggestion unit suggests them as bundled products or set products. For example, the suggestion unit suggests a set of a camera, lens, and tripod. The suggestion unit can also provide special prices or limited offers to increase added value for the user. This allows the suggestion unit to suggest related products as bundled products or set products, thereby increasing added value for the user.

[0039] The suggestion unit can analyze the purchasing patterns of other users and suggest related products based on the results. The suggestion unit, for example, analyzes the purchasing patterns of other users and suggests related products based on the results. For example, it suggests products that a user who has purchased a specific product will likely purchase next. The suggestion unit can also consider the purchase history and ratings of other users to suggest highly reliable related products. This allows the suggestion unit to analyze the purchasing patterns of other users and suggest related products based on the results.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The e-commerce system further includes a voice assistant unit. The voice assistant unit can provide real-time answers when a user asks a question about a product by voice. For example, if a user asks, "What are the features of this camera?", the voice assistant unit will explain the main features of the camera. The voice assistant unit can also analyze the user's past question history and prepare information that the user may be interested in in advance. This allows the user to easily obtain product information by voice, enabling a smoother purchasing experience.

[0042] The e-commerce system also includes a health management unit that analyzes the user's health data. The health management unit can recommend healthy products based on the user's health condition. For example, if the user uses a fitness tracker, the data can be analyzed to recommend health foods and supplements that suit the user's exercise habits. The health management unit can also consider the user's dietary history and sleep data to suggest products that support comprehensive health management. This makes it possible to recommend products based on the user's health condition, thereby supporting the user's health maintenance.

[0043] The e-commerce system further includes a subscription suggestion unit based on the user's purchase history. The subscription suggestion unit can analyze the user's past purchase history and suggest products that are purchased regularly in a subscription format. For example, consumables and food that the user purchases regularly can be provided in a subscription format. The subscription suggestion unit can also suggest an optimal subscription plan taking into account the user's purchasing patterns. This allows the user to purchase the products they need regularly without any hassle.

[0044] The e-commerce system also includes a region-specific suggestion unit that analyzes the user's geographic information. The region-specific suggestion unit can suggest products and services that are specific to the region based on the user's current location and past location information. For example, if the user is in a specific region, the region-specific suggestion unit can suggest products and services that are popular in that region. The region-specific suggestion unit can also suggest products related to the region, taking into account seasonal and event information. This allows users to easily find products and services that are specific to the region.

[0045] The e-commerce system further includes a recycling suggestion unit that uses the user's purchasing history as a basis. The recycling suggestion unit can suggest recycling methods for products the user has previously purchased and recyclable products. For example, the recycling suggestion unit can suggest recycling methods for electronic devices purchased by the user. Furthermore, if the user is interested in recycling, the recycling suggestion unit can also prioritize recyclable products. This allows the user to take environmentally conscious purchasing actions.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The reason generator analyzes the user's past purchase history, browsing history, and profile information to generate "reasons for recommending the product" tailored to the individual user's needs. For example, based on the products the user has previously purchased or viewed, the generator may explain, "This product is recommended based on your past purchase history." It can also generate recommendation reasons tailored to the user's interests and concerns based on the user's profile information. Step 2: The image generation unit generates a usage scene image that matches the user's needs based on the reason generated by the reason generation unit. For example, if the user is trying to purchase outdoor equipment, a scene in which the product is being used is generated and displayed to the user. Different scenes can also be generated depending on the user's needs. Step 3: The suggestion unit suggests "more recommended products" or "products that are recommended to be used together" with the currently displayed product, along with the usage scene image generated by the image generation unit. For example, if the user is browsing cameras, the suggestion unit suggests lenses and accessories that go well with the camera. It can also suggest related products based on the user's past purchase history and browsing history.

[0048] (Example 2) The e-commerce system according to the embodiment of the present invention is a system that individually explains the reasons for recommending a product to a user, provides visual images of usage scenarios, and suggests related products, thereby providing the user with a shopping experience that leaves them satisfied and satisfied.

[0049] An e-commerce system according to an embodiment includes a reason generation unit, an image generation unit, and a suggestion unit. The reason generation unit analyzes a user's past purchase history, browsing history, and profile information to generate "reasons for recommending a product" tailored to individual user needs. For example, the reason generation unit may explain, "This product is recommended based on your past purchase history," based on the user's past purchases and browsing. The reason generation unit may also generate recommendation reasons tailored to the user's interests and concerns based on the user's profile information. The image generation unit generates usage scene images tailored to the user's needs based on the reasons generated by the reason generation unit. For example, if a user is purchasing outdoor equipment, the image generation unit generates a scene in which the product is being used and displays it to the user. The image generation unit may also generate different scenes tailored to the user's needs. The suggestion unit, along with the usage scene image generated by the image generation unit, suggests "more recommended products" or "products recommended for use together" with the currently displayed product. For example, if a user is browsing cameras, the suggestion unit may suggest lenses and accessories that go well with the camera. The suggestion unit can also suggest related products based on the user's past purchase history and browsing history. This allows the e-commerce system according to the embodiment to provide the user with a shopping experience that is convincing and satisfying. For example, the suggestion unit can explain the reason for recommending a product based on the user's past purchases and visually visualize how the product will be used after purchase, thereby increasing the user's motivation to purchase. Furthermore, suggesting related products can further enhance the user's purchasing experience.

[0050] The reason generation unit can analyze the user's real-time behavioral data and generate the optimal recommendation reason on the spot. The reason generation unit, for example, analyzes the page the user is currently viewing and click history in real time and generates the optimal recommendation reason based on that data. For example, if the user frequently clicks on products from a specific brand, it generates a reason to recommend products from that brand. The reason generation unit can also analyze the user's stay time and scrolling behavior, and generate recommendation reasons that reflect the user's interests. This makes it possible to analyze the user's real-time behavioral data and generate the optimal recommendation reason on the spot.

[0051] The reason generation unit can analyze a user's social media activity and generate a recommendation reason that reflects the user's interests and concerns. The reason generation unit, for example, analyzes a user's social media activity and generates a recommendation reason that reflects the user's interests and concerns. For example, if a user frequently mentions a particular brand or product on social media, the reason generation unit generates a reason to recommend that brand or product. The reason generation unit can also analyze the user's number of followers and number of likes, and generate a recommendation reason that takes the user's influence into consideration. This makes it possible to analyze a user's social media activity and generate a recommendation reason that reflects the user's interests and concerns.

[0052] The reason generation unit can use the emotion estimation function to estimate the user's current emotional state and generate a recommendation reason that matches that emotion. The reason generation unit, for example, uses the emotion estimation function to estimate the user's current emotional state and generate a recommendation reason that matches that emotion. For example, if the user is in a positive emotional state, the reason generation unit generates a recommendation reason that emphasizes that emotion. Furthermore, if the user is in a negative emotional state, the reason generation unit can also generate a recommendation reason that alleviates that emotion. In this way, the emotion estimation function can be used to estimate the user's current emotional state and generate a recommendation reason that matches that emotion.

[0053] The reason generation unit can analyze reviews and ratings from other users and generate a recommendation reason based on the results. The reason generation unit, for example, analyzes reviews and ratings from other users and generates a recommendation reason based on the results. For example, if a specific product has received high ratings, the recommendation reason is generated based on the ratings. The reason generation unit can also take into account the content of the reviews and the reliability of the ratings to generate a recommendation reason that is reliable for the user. This makes it possible to analyze reviews and ratings from other users and generate a recommendation reason based on the results.

[0054] The reason generation unit can use the emotion estimation function to generate a recommendation reason based on a product for which the user has previously expressed positive emotion. The reason generation unit, for example, uses the emotion estimation function to generate a recommendation reason based on a product for which the user has previously expressed positive emotion. For example, if the user has previously expressed positive emotion toward a product purchased, the unit generates a reason to recommend a product related to that product. The reason generation unit can also generate a recommendation reason to avoid a product for which the user has previously expressed negative emotion. In this way, the emotion estimation function can be used to generate a recommendation reason based on a product for which the user has previously expressed positive emotion.

[0055] The image generation unit can analyze the user's past photos and image data and generate the usage scene image that is most relevant to each individual user. The image generation unit, for example, analyzes the user's past photos and image data and generates the usage scene image that is most relevant to each individual user. For example, the image generation unit generates a usage scene image of outdoor equipment based on outdoor photos taken by the user in the past. The image generation unit can also generate a more relevant usage scene image by taking into account the date, time, and location of the user's photo. This allows the user's past photos and image data to be analyzed and the usage scene image that is most relevant to each individual user.

[0056] The image generation unit can generate an optimal usage scene image by taking into account the season and weather information. The image generation unit generates an optimal usage scene image by taking into account, for example, seasonal information. For example, in winter, a usage scene image is generated that shows warm clothing and a winter landscape in the background. The image generation unit can also generate usage scene images that are suitable for sunny days and rainy days by taking into account weather information. In this way, an optimal usage scene image can be generated by taking into account the season and weather information.

[0057] The image generation unit can use the emotion estimation function to generate a usage scene in which the user feels the most positive emotion. The image generation unit, for example, uses the emotion estimation function to generate a usage scene in which the user feels the most positive emotion. For example, based on a scene in which the user previously showed positive emotion, it generates a similar usage scene image. The image generation unit can also generate a usage scene image that avoids a scene in which the user showed negative emotion. In this way, the emotion estimation function can be used to generate a usage scene in which the user feels the most positive emotion.

[0058] The image generation unit can display the usage scene image using a 3D model or AR technology, providing the user with an experience of actually using the product. For example, the image generation unit can display the usage scene image as a 3D model, allowing the user to view the product from 360 degrees. For example, the image generation unit can simulate furniture placement using a 3D model. The image generation unit can also use AR technology to provide the user with an experience of actually using the product. For example, the AR technology can allow the user to place the product in an actual environment via a smartphone. This allows the usage scene image to be displayed using a 3D model or AR technology, providing the user with an experience of actually using the product.

[0059] The image generation unit can analyze usage scene images posted by other users and generate new usage scene images based on the analysis. For example, the image generation unit can analyze usage scene images posted by other users and generate new usage scene images based on the analysis. For example, a similar usage scene image can be generated based on an outdoor photo posted by a user. The image generation unit can also generate more reliable usage scene images by taking into account the content and ratings posted by other users. This allows the image generation unit to analyze usage scene images posted by other users and generate new usage scene images based on the analysis.

[0060] The image generation unit can use the emotion estimation function to identify the usage scene in which the user is most interested and generate an image that emphasizes that scene. The image generation unit, for example, can use the emotion estimation function to identify the usage scene in which the user is most interested and generate an image that emphasizes that scene. For example, the image generation unit generates a similar usage scene image based on a scene in which the user has previously shown interest. The image generation unit can also increase the user's purchasing motivation by emphasizing scenes that reflect the user's interests and concerns. In this way, the emotion estimation function can be used to identify the usage scene in which the user is most interested and generate an image that emphasizes that scene.

[0061] The suggestion unit can analyze the user's purchase history, browsing history, and search history to suggest optimal related products. The suggestion unit, for example, analyzes the user's purchase history, browsing history, and search history to suggest optimal related products. For example, it can suggest products related to products the user has previously purchased. The suggestion unit can also consider the user's search keywords and search frequency to suggest related products that match the user's interests. This makes it possible to analyze the user's purchase history, browsing history, and search history to suggest optimal related products.

[0062] The suggestion unit can use the emotion estimation function to suggest related products that the user feels the most positive about. The suggestion unit, for example, uses the emotion estimation function to suggest related products that the user feels the most positive about. For example, the suggestion unit can suggest products related to products for which the user has previously felt positive about. The suggestion unit can also suggest related products that avoid products for which the user has felt negative about. In this way, the emotion estimation function can be used to suggest related products for which the user feels the most positive about.

[0063] The suggestion unit, when suggesting related products, can suggest them as bundled products or set products, thereby increasing added value for the user. For example, when suggesting related products, the suggestion unit suggests them as bundled products or set products. For example, the suggestion unit suggests a set of a camera, lens, and tripod. The suggestion unit can also provide special prices or limited offers to increase added value for the user. This allows the suggestion unit to suggest related products as bundled products or set products, thereby increasing added value for the user.

[0064] The suggestion unit can analyze the purchasing patterns of other users and suggest related products based on the results. The suggestion unit, for example, analyzes the purchasing patterns of other users and suggests related products based on the results. For example, it suggests products that a user who has purchased a specific product will likely purchase next. The suggestion unit can also consider the purchase history and ratings of other users to suggest highly reliable related products. This allows the suggestion unit to analyze the purchasing patterns of other users and suggest related products based on the results.

[0065] The suggestion unit can use the emotion estimation function to identify related products in which the user is most interested and highlight and suggest those products. The suggestion unit, for example, can use the emotion estimation function to identify related products in which the user is most interested and highlight and suggest those products. For example, the suggestion unit can suggest products related to products in which the user has shown interest in the past. The suggestion unit can also increase the user's purchasing motivation by highlighting products that reflect the user's interests and concerns. In this way, the suggestion unit can use the emotion estimation function to identify related products in which the user is most interested and highlight and suggest those products.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The e-commerce system further includes a voice assistant unit. The voice assistant unit can provide real-time answers when a user asks a question about a product by voice. For example, if a user asks, "What are the features of this camera?", the voice assistant unit will explain the main features of the camera. The voice assistant unit can also analyze the user's past question history and prepare information that the user may be interested in in advance. This allows the user to easily obtain product information by voice, enabling a smoother purchasing experience.

[0068] The e-commerce system further includes an advertisement display unit that uses an emotion estimation function to estimate the user's current emotional state and display advertisements that match that emotion. For example, if the user is in a positive emotional state, a bright and cheerful advertisement can be displayed. On the other hand, if the user is in a negative emotional state, a relaxing advertisement can be displayed. This allows advertisements to be displayed that match the user's emotions, maximizing the effectiveness of the advertisements.

[0069] The e-commerce system also includes a health management unit that analyzes the user's health data. The health management unit can recommend healthy products based on the user's health condition. For example, if the user uses a fitness tracker, the data can be analyzed to recommend health foods and supplements that suit the user's exercise habits. The health management unit can also consider the user's dietary history and sleep data to suggest products that support comprehensive health management. This makes it possible to recommend products based on the user's health condition, thereby supporting the user's health maintenance.

[0070] The e-commerce system further includes a customer support unit that uses an emotion estimation function to estimate the user's current emotional state and provide support tailored to that emotion. For example, if the user is dissatisfied, the unit responds quickly and courteously to resolve the user's dissatisfaction. Also, if the user is satisfied, the unit can provide additional services that further enhance the user's emotion. This makes it possible to provide customer support tailored to the user's emotions and improve user satisfaction.

[0071] The e-commerce system further includes a subscription suggestion unit based on the user's purchase history. The subscription suggestion unit can analyze the user's past purchase history and suggest products that are purchased regularly in a subscription format. For example, consumables and food that the user purchases regularly can be provided in a subscription format. The subscription suggestion unit can also suggest an optimal subscription plan taking into account the user's purchasing patterns. This allows the user to purchase the products they need regularly without any hassle.

[0072] The e-commerce system further includes a review display unit that uses an emotion estimation function to estimate the user's current emotional state and display reviews that match that emotion. For example, if the user is in a positive emotional state, the unit highlights and displays positive reviews from other users. Also, if the user is in a negative emotional state, the unit displays more reliable reviews to alleviate the user's anxiety. This allows the display of reviews to match the user's emotions, thereby increasing purchasing motivation.

[0073] The e-commerce system also includes a region-specific suggestion unit that analyzes the user's geographic information. The region-specific suggestion unit can suggest products and services that are specific to the region based on the user's current location and past location information. For example, if the user is in a specific region, the region-specific suggestion unit can suggest products and services that are popular in that region. The region-specific suggestion unit can also suggest products related to the region, taking into account seasonal and event information. This allows users to easily find products and services that are specific to the region.

[0074] The e-commerce system further includes a personalized gift suggestion unit that uses the emotion estimation function to suggest a gift based on the user's emotions. The personalized gift suggestion unit can estimate the user's current emotional state and suggest a gift that matches that emotion. For example, if a user wants to show gratitude, the unit can suggest a gift that matches that emotion. Also, if a user wants to celebrate a special event, the unit can suggest a gift that is appropriate for that event. This makes it possible to suggest gifts that match the user's emotions, thereby increasing user satisfaction.

[0075] The e-commerce system further includes a recycling suggestion unit that uses the user's purchasing history as a basis. The recycling suggestion unit can suggest recycling methods for products the user has previously purchased and recyclable products. For example, the recycling suggestion unit can suggest recycling methods for electronic devices purchased by the user. Furthermore, if the user is interested in recycling, the recycling suggestion unit can also prioritize recyclable products. This allows the user to take environmentally conscious purchasing actions.

[0076] The e-commerce system further includes a feedback collection unit based on the user's emotions using an emotion estimation function. The feedback collection unit can estimate the user's current emotional state and collect feedback tailored to that emotion. For example, if the user is in a positive emotional state, it can collect feedback that reflects that emotion. Also, if the user is in a negative emotional state, it can collect feedback that alleviates that emotion. This makes it possible to collect feedback tailored to the user's emotions, which can be used to improve services.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The reason generator analyzes the user's past purchase history, browsing history, and profile information to generate "reasons for recommending the product" tailored to the individual user's needs. For example, based on the products the user has previously purchased or viewed, the generator may explain, "This product is recommended based on your past purchase history." It can also generate recommendation reasons tailored to the user's interests and concerns based on the user's profile information. Step 2: The image generation unit generates a usage scene image that matches the user's needs based on the reason generated by the reason generation unit. For example, if the user is trying to purchase outdoor equipment, a scene in which the product is being used is generated and displayed to the user. Different scenes can also be generated depending on the user's needs. Step 3: The suggestion unit suggests "more recommended products" or "products that are recommended to be used together" with the currently displayed product, along with the usage scene image generated by the image generation unit. For example, if the user is browsing cameras, the suggestion unit suggests lenses and accessories that go well with the camera. It can also suggest related products based on the user's past purchase history and browsing history.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 reason generation unit that analyzes a user's past purchase history, browsing history, and profile information to generate "reasons for recommending the product" according to individual user needs; an image generation unit that generates a usage scene image that meets the needs of a user based on the reason generated by the reason generation unit; a suggestion unit that suggests "more recommended products" and "products that are recommended to be used together" with the displayed product together with the usage scene image generated by the image generation unit. A system characterized by:

2. The reason generating unit Analyze the user's real-time behavior data and generate optimal recommendations on the spot 2. The system of claim 1.

3. The image generation unit Analyzing the user's past photos and image data, and generating the most relevant usage scene image for each individual user.

2. The system of claim 1.

4. The proposal unit Analyze the user's purchase history, browsing history, and search history to suggest the most suitable related products 2. The system of claim 1.

5. The reason generating unit Estimate the user's current emotional state and generate a recommendation reason that matches that emotional state.

2. The system of claim 1.

6. The image generation unit Generate a usage scenario that gives the user the most positive feelings 2. The system of claim 1.

7. The proposal unit Suggesting related products that the user feels most positive about 2. The system of claim 1.

8. The proposal unit Identifying related products that are of most interest to said user and highlighting and suggesting those products 2. The system of claim 1.

Images