System

Abstract

An object of the system according to the embodiment is to link an ambiguous image of a user to a specific product and to convey a request to a company when there is no corresponding product.SOLUTION: A system according to an embodiment includes an image collection unit, an image generation unit, a product search unit, and a demand communication unit. The image collection unit collects an ambiguous image of a user. The image generation unit converts the image collected by the image collection unit into an image. The product search unit searches for a product based on the image generated by the image generation unit. The demand transmission unit transmits the demand to the company side when the corresponding product is not found.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, it was difficult to link users' vague ideas to specific products, and there was a lack of means for users to communicate their needs to companies.

[0005] The system according to the embodiment aims to link a user's vague image to a specific product and to convey the user's request to a company if there is no corresponding product. [Means for solving the problem]

[0006] The system according to the embodiment includes an image collection unit, an image generation unit, a product search unit, and a request transmission unit. The image collection unit collects vague images from users. The image generation unit visualizes the images collected by the image collection unit. The product search unit searches for products based on the images generated by the image generation unit. The request transmission unit communicates the request to the company if a corresponding product cannot be found. [Effects of the Invention]

[0007] The system according to the embodiment links a user's vague image to a specific product, and if there is no corresponding product, the user can communicate their request to a company. [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 product search system according to an embodiment of the present invention visualizes a user's vague image and searches for products based on that image. Furthermore, if a corresponding product cannot be found, the system has a function to communicate the user's request to the company. This allows the product search system to connect the user's vague image with a specific product.

[0029] A product search system according to an embodiment includes an image collection unit, an image generation unit, a product search unit, and a request transmission unit. The image collection unit collects a user's vague image. For example, the user can describe their image using text or voice. For example, if the user explains, "I want a blue floral dress, but I haven't decided on a specific design," the generation AI analyzes the description and understands the user's image. The image generation unit visualizes the collected images. For example, the generation AI generates an image of a blue floral dress based on the user's description. The input to the generation AI is a prompt including the user's description, and the generation AI generates an image based on the prompt. The product search unit searches for products based on the generated images. For example, the image of a blue floral dress generated by the generation AI is used to search for matching products in online shops and product catalogs. The generation AI uses image recognition technology to search a product database and list matching products. If a matching product cannot be found, the request transmission unit communicates the request to the company. For example, if the generation AI returns a result such as "No blue floral dresses were found," the request transmission unit generates a message to communicate the request to the company. As a result, the product search system according to the embodiment can link a user's vague image to a specific product. For example, even if a user does not have a specific design in mind, the user can find a product based on their image. Furthermore, even if a corresponding product cannot be found, the user can communicate their request to the company, which can be used to develop or improve new products.

[0030] The image collection unit can derive a more specific image by referring to the user's past purchase history or browsing history. For example, the image collection unit uses a generation AI to analyze the user's past purchase history and understand the user's preferences for products and designs. For example, it concretizes the user's vague image based on the colors and designs of products purchased in the past. The image collection unit also refers to the user's past browsing history to understand the trends in products and designs in which the user is interested. For example, it concretizes the user's vague image based on the categories and features of products viewed in the past. This improves the accuracy of concretizing the user's vague image.

[0031] The image collection unit can ask questions in real time as the user explains, collecting additional information to concretely define the image. For example, the image collection unit uses the generation AI to analyze the user's explanation and ask questions in real time to fill in any missing information. For example, it asks questions such as, "What color do you like?" or "What kind of material would be good?" The image collection unit also uses the generation AI to ask questions in real time as the user explains, collecting additional information to concretely define the image. For example, if the user explains that they want a blue floral dress, the generation AI will ask questions such as, "What size and shape of floral pattern would be good?" This allows for efficient information collection to concretely define the user's vague image.

[0032] When collecting the user's vague image, the image collection unit can accept not only text or voice but also sketches or photos drawn by the user as input. In the image collection unit, for example, a generation AI analyzes a sketch drawn by the user and materializes the vague image. For example, the generation AI generates a detailed design based on a sketch of a dress drawn by the user. The image collection unit also accepts a photo taken by the user as input and materializes the vague image based on the photo. For example, the generation AI generates a floral design based on a photo of flowers taken by the user. This allows the user's vague image to be materialized using a variety of input means.

[0033] The image collection unit can collect images from users in different cultural spheres or regions and analyze them from a global perspective. For example, the image collection unit uses a generation AI to collect images from users in different cultural spheres or regions and analyze them from a global perspective. For example, it collects images from users in Asia, Europe, and America and analyzes similarities and differences. The image collection unit also collects images from users in different cultural spheres or regions and analyzes them from a global perspective. For example, it analyzes the design and color trends preferred by users in different cultural spheres and creates images based on that. This allows images from users in different cultural spheres or regions to be analyzed from a global perspective.

[0034] The image generation unit can provide an interface that allows the user to make corrections to the generated image in real time. The image generation unit provides an interface that allows the user to make corrections to the image generated by the generation AI in real time. For example, the color or design can be changed in real time. The image generation unit also provides an interface that allows the user to make corrections to the generated image in real time. For example, the user can select a part of the image and change the color or shape. This allows the user to make corrections in real time to create a more concrete image.

[0035] The image generation unit can analyze a user's social media posts and generate images that reflect the user's preferences and style. For example, the image generation unit uses a generation AI to analyze a user's social media posts and generate images that reflect the user's preferences and style. For example, it can reflect a user's preferred design based on photos and comments posted by the user. The image generation unit can also analyze a user's social media posts and generate images that reflect the user's preferences and style. For example, it can analyze trends in colors and designs frequently posted by the user and generate images based on that. In this way, by analyzing a user's social media posts, it can generate images that better suit the user's preferences.

[0036] The image generation unit can add variations from different viewpoints and angles to the generated image, thereby providing the user with multiple perspectives. For example, the image generation unit can add variations from different viewpoints and angles to the image generated by the generation AI, thereby providing the user with multiple perspectives. For example, it can generate images of the front, back, and side of a dress. The image generation unit can also add variations from different viewpoints and angles to the generated image, thereby providing the user with multiple perspectives. For example, it can generate images from a top view or an oblique angle. This allows the user to view multiple perspectives by providing variations from different viewpoints and angles.

[0037] The product search unit can search for inventory information not only in online shops but also in physical stores based on the image generated by the generation AI and provide it to the user. For example, the product search unit can search for inventory information not only in online shops but also in physical stores based on the image generated by the generation AI and provide it to the user. For example, it can display inventory information for a blue floral dress in online shops and physical stores. The product search unit can also search for inventory information in online shops and physical stores based on the image generated by the generation AI and provide it to the user. For example, it can display the product's inventory status and expected arrival date. This improves user convenience by providing inventory information for online shops and physical stores simultaneously.

[0038] The product search unit can compare and display search results for products in different price ranges and brands based on images generated by the generation AI. For example, the product search unit compares and displays search results for products in different price ranges and brands based on images generated by the generation AI. For example, it displays a list of blue floral dresses in different price ranges and brands. The product search unit also compares and displays search results for products in different price ranges and brands based on images generated by the generation AI. For example, it compares product prices and brand features. This makes it easier for users to select the most suitable product by comparing and displaying search results for products in different price ranges and brands.

[0039] When the generation AI conveys a request to the company, the request transmission unit can also convey the user's detailed feedback and desired conditions at the same time. For example, when the generation AI conveys a request to the company, the request transmission unit also conveys the user's detailed feedback and desired conditions at the same time. For example, it generates a message such as, "We were unable to find a blue floral dress, but the user would like a design like this." In addition, when the generation AI conveys a request to the company, the request transmission unit also conveys the user's detailed feedback and desired conditions at the same time. For example, it specifically conveys the user's opinions and areas for improvement. In this way, by conveying the user's detailed feedback and desired conditions to the company, the company can take more specific action.

[0040] When the generation AI communicates a request to a company, the request communication unit also provides information on similar past requests and trends, which the company can use as reference. For example, when the generation AI communicates a request to a company, the request communication unit also provides information on similar past requests and trends. For example, it provides information such as, "Similar requests have been made in the past." Furthermore, when the generation AI communicates a request to a company, the request communication unit also provides information on similar past requests and trends. For example, it refers to current market trends and the requests of other users. In this way, by providing information on similar past requests and trends, the company can respond more appropriately.

[0041] The request transmission unit can promote competition when the generation AI transmits a request to multiple companies simultaneously. For example, when the generation AI transmits a request to a company, the request transmission unit promotes competition by transmitting the request to multiple companies simultaneously. For example, the request transmission unit generates a message such as, "I couldn't find a blue floral dress, but I have transmitted the request to multiple companies." Furthermore, when the generation AI transmits a request to a company, the request transmission unit promotes competition by transmitting the request to multiple companies simultaneously. For example, by transmitting the request to multiple companies simultaneously, the possibility of better products being offered increases. By transmitting the request to multiple companies simultaneously, competition is promoted and the possibility of better products being offered increases.

[0042] The request transmission unit can protect the user's privacy by anonymizing the request content when the generation AI transmits the request to the company. For example, the request transmission unit anonymizes the request content when the generation AI transmits the request to the company, protecting the user's privacy. For example, it generates a message such as "This is a request from an anonymous user." The request transmission unit also anonymizes the request content when the generation AI transmits the request to the company, protecting the user's privacy. For example, it deletes personal information and masks identifying information. In this way, the user's privacy can be protected by anonymizing the request content.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The product search system may further include an incentive providing unit to increase users' purchasing motivation. For example, when a user searches for a specific product, the incentive providing unit provides coupons or discount information related to that product. The incentive providing unit may also award points or benefits when a user meets certain conditions. For example, a user who makes a purchase of more than a certain amount may be awarded points that can be used for the next purchase. This can increase users' purchasing motivation and encourage repeat purchases.

[0045] The product search system can further include a product suggestion unit that takes into account the user's health condition. The product suggestion unit can suggest health-conscious products based on the user's health data, for example. The product suggestion unit can also suggest products that do not contain allergenic ingredients based on the user's allergy information. For example, if the user has a specific food allergy, the product suggestion unit can suggest foods that do not contain that ingredient. This makes it possible to suggest products that take into account the user's health condition.

[0046] The product search system can further include a product suggestion unit that matches the user's lifestyle. The product suggestion unit, for example, suggests appropriate products based on the user's lifestyle data. The product suggestion unit can also suggest related products based on the user's hobbies and interests. For example, camping equipment and mountain climbing equipment can be suggested to a user who enjoys the outdoors. This makes it possible to suggest products that match the user's lifestyle.

[0047] The product search system can further include a prediction unit that predicts future purchases based on the user's purchase history. The prediction unit, for example, analyzes trends in products the user has purchased in the past and suggests products that the user is likely to purchase next. The prediction unit can also analyze the user's purchasing patterns and suggest products that the user is likely to purchase at a specific time. For example, if a user purchases a specific product at the same time every year, products will be suggested based on that time. This makes it possible to predict future purchases based on the user's purchase history and suggest appropriate products.

[0048] The product search system may further include a trend analysis unit that analyzes trends based on the user's social media activity. The trend analysis unit, for example, analyzes keywords and hashtags frequently mentioned by the user on social media to understand current trends. The trend analysis unit may also analyze the activities of the user's followers and friends to suggest related trends. For example, it may suggest products or services that are frequently mentioned by the user's friends. This makes it possible to analyze trends based on the user's social media activity and suggest appropriate products.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The image collection unit collects the user's vague image. For example, the user can describe their image in text or voice. The generation AI analyzes the user's description and understands the user's image. Step 2: The image generator visualizes the collected images. For example, the generator generates an image of a blue floral dress based on the user's description. The input to the generator is a prompt containing the user's description, and the generator generates an image based on that prompt. Step 3: The product search unit searches for products based on the generated image. For example, using an image of a blue floral dress generated by the generation AI, it searches for matching products in online shops and product catalogs. The generation AI uses image recognition technology to search a product database and list matching products. Step 4: If the corresponding product cannot be found, the request transmission unit communicates the request to the company. For example, if the generation AI returns the result "We could not find a blue floral dress," it will generate a message to communicate that request to the company.

[0051] (Example 2) The product search system according to an embodiment of the present invention visualizes a user's vague image and searches for products based on that image. Furthermore, if a corresponding product cannot be found, the system has a function to communicate the user's request to the company. This allows the product search system to connect the user's vague image with a specific product.

[0052] A product search system according to an embodiment includes an image collection unit, an image generation unit, a product search unit, and a request transmission unit. The image collection unit collects a user's vague image. For example, the user can describe their image using text or voice. For example, if the user explains, "I want a blue floral dress, but I haven't decided on a specific design," the generation AI analyzes the description and understands the user's image. The image generation unit visualizes the collected images. For example, the generation AI generates an image of a blue floral dress based on the user's description. The input to the generation AI is a prompt including the user's description, and the generation AI generates an image based on the prompt. The product search unit searches for products based on the generated images. For example, the image of a blue floral dress generated by the generation AI is used to search for matching products in online shops and product catalogs. The generation AI uses image recognition technology to search a product database and list matching products. If a matching product cannot be found, the request transmission unit communicates the request to the company. For example, if the generation AI returns a result such as "No blue floral dresses were found," the request transmission unit generates a message to communicate the request to the company. As a result, the product search system according to the embodiment can link a user's vague image to a specific product. For example, even if a user does not have a specific design in mind, the user can find a product based on their image. Furthermore, even if a corresponding product cannot be found, the user can communicate their request to the company, which can be used to develop or improve new products.

[0053] The image collection unit can derive a more specific image by referring to the user's past purchase history or browsing history. For example, the image collection unit uses a generation AI to analyze the user's past purchase history and understand the user's preferences for products and designs. For example, it concretizes the user's vague image based on the colors and designs of products purchased in the past. The image collection unit also refers to the user's past browsing history to understand the trends in products and designs in which the user is interested. For example, it concretizes the user's vague image based on the categories and features of products viewed in the past. This improves the accuracy of concretizing the user's vague image.

[0054] The image collection unit can ask questions in real time as the user explains, collecting additional information to concretely define the image. For example, the image collection unit uses the generation AI to analyze the user's explanation and ask questions in real time to fill in any missing information. For example, it asks questions such as, "What color do you like?" or "What kind of material would be good?" The image collection unit also uses the generation AI to ask questions in real time as the user explains, collecting additional information to concretely define the image. For example, if the user explains that they want a blue floral dress, the generation AI will ask questions such as, "What size and shape of floral pattern would be good?" This allows for efficient information collection to concretely define the user's vague image.

[0055] The image collection unit can use the emotion estimation function to analyze the user's emotions while explaining and generate questions to elicit positive emotions. For example, the image collection unit uses a generation AI to analyze the user's emotions while explaining and generate questions to elicit positive emotions. For example, if the user seems to be enjoying themselves, the image collection unit asks a question such as, "In what kind of occasion would you like to wear that design?" The image collection unit also uses the emotion estimation function to analyze the user's emotions while explaining and generate questions to elicit positive emotions. For example, if the user seems a little anxious while talking, the image collection unit asks a question such as, "What kind of design do you like best?" This elicits positive emotions from the user and enables better image collection.

[0056] When collecting the user's vague image, the image collection unit can accept not only text or voice but also sketches or photos drawn by the user as input. In the image collection unit, for example, a generation AI analyzes a sketch drawn by the user and materializes the vague image. For example, the generation AI generates a detailed design based on a sketch of a dress drawn by the user. The image collection unit also accepts a photo taken by the user as input and materializes the vague image based on the photo. For example, the generation AI generates a floral design based on a photo of flowers taken by the user. This allows the user's vague image to be materialized using a variety of input means.

[0057] The image collection unit can collect images from users in different cultural spheres or regions and analyze them from a global perspective. For example, the image collection unit uses a generation AI to collect images from users in different cultural spheres or regions and analyze them from a global perspective. For example, it collects images from users in Asia, Europe, and America and analyzes similarities and differences. The image collection unit also collects images from users in different cultural spheres or regions and analyzes them from a global perspective. For example, it analyzes the design and color trends preferred by users in different cultural spheres and creates images based on that. This allows images from users in different cultural spheres or regions to be analyzed from a global perspective.

[0058] The image generation unit can provide an interface that allows the user to make corrections to the generated image in real time. The image generation unit provides an interface that allows the user to make corrections to the image generated by the generation AI in real time. For example, the color or design can be changed in real time. The image generation unit also provides an interface that allows the user to make corrections to the generated image in real time. For example, the user can select a part of the image and change the color or shape. This allows the user to make corrections in real time to create a more concrete image.

[0059] The image generation unit can analyze the user's emotional response to the generated image using the emotion estimation function and suggest image modifications to elicit a positive response. The image generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the generated image and suggest image modifications to elicit a positive response. For example, if the user looks slightly dissatisfied, the image generation unit suggests changes to the color or design. The image generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated image and suggest image modifications to elicit a positive response. For example, if the user looks happy, the image generation unit suggests design changes to further elicit that emotion. In this way, by modifying the image based on the user's emotional response, it is possible to provide a more satisfying image.

[0060] The image generation unit can analyze a user's social media posts and generate images that reflect the user's preferences and style. For example, the image generation unit uses a generation AI to analyze a user's social media posts and generate images that reflect the user's preferences and style. For example, it can reflect a user's preferred design based on photos and comments posted by the user. The image generation unit can also analyze a user's social media posts and generate images that reflect the user's preferences and style. For example, it can analyze trends in colors and designs frequently posted by the user and generate images based on that. In this way, by analyzing a user's social media posts, it can generate images that better suit the user's preferences.

[0061] The image generation unit can add variations from different viewpoints and angles to the generated image, thereby providing the user with multiple perspectives. For example, the image generation unit can add variations from different viewpoints and angles to the image generated by the generation AI, thereby providing the user with multiple perspectives. For example, it can generate images of the front, back, and side of a dress. The image generation unit can also add variations from different viewpoints and angles to the generated image, thereby providing the user with multiple perspectives. For example, it can generate images from a top view or an oblique angle. This allows the user to view multiple perspectives by providing variations from different viewpoints and angles.

[0062] The image generation unit can analyze the user's emotional response to the image generated using the emotion estimation function in real time and select the optimal image. The image generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the generated image in real time and select the optimal image. For example, it selects the image to which the user has the most positive response. The image generation unit also uses the emotion estimation function to analyze the user's emotional response to the generated image in real time and selects the optimal image. For example, it selects the image that the user is most satisfied with. In this way, the user's emotional response can be analyzed in real time and the optimal image can be selected.

[0063] The product search unit can use the emotion estimation function to analyze the user's emotional response to the search results and prioritize display of search results that will elicit a positive response. The product search unit, for example, uses the emotion estimation function to analyze the user's emotional response to the search results and prioritize display of search results that will elicit a positive response. For example, it prioritizes display of products that the user is most interested in. The product search unit also uses the emotion estimation function to analyze the user's emotional response to the search results and prioritize display of search results that will elicit a positive response. For example, it prioritizes display of products that the user is most satisfied with. In this way, by prioritize display of search results based on the user's emotional response, user satisfaction is improved.

[0064] The product search unit can search for inventory information not only in online shops but also in physical stores based on the image generated by the generation AI and provide it to the user. For example, the product search unit can search for inventory information not only in online shops but also in physical stores based on the image generated by the generation AI and provide it to the user. For example, it can display inventory information for a blue floral dress in online shops and physical stores. The product search unit can also search for inventory information in online shops and physical stores based on the image generated by the generation AI and provide it to the user. For example, it can display the product's inventory status and expected arrival date. This improves user convenience by providing inventory information for online shops and physical stores simultaneously.

[0065] The product search unit can compare and display search results for products in different price ranges and brands based on images generated by the generation AI. For example, the product search unit compares and displays search results for products in different price ranges and brands based on images generated by the generation AI. For example, it displays a list of blue floral dresses in different price ranges and brands. The product search unit also compares and displays search results for products in different price ranges and brands based on images generated by the generation AI. For example, it compares product prices and brand features. This makes it easier for users to select the most suitable product by comparing and displaying search results for products in different price ranges and brands.

[0066] The product search unit can use the emotion estimation function to analyze the user's emotional response to the search results in real time and provide optimal search results. The product search unit, for example, uses the emotion estimation function to analyze the user's emotional response to the search results in real time and provide optimal search results. For example, the product to which the user has the most positive response is preferentially displayed. The product search unit also uses the emotion estimation function to analyze the user's emotional response to the search results in real time and provide optimal search results. For example, the product to which the user is most satisfied is preferentially displayed. This makes it possible to analyze the user's emotional response in real time and provide optimal search results.

[0067] When the generation AI conveys a request to the company, the request transmission unit can also convey the user's detailed feedback and desired conditions at the same time. For example, when the generation AI conveys a request to the company, the request transmission unit also conveys the user's detailed feedback and desired conditions at the same time. For example, it generates a message such as, "We were unable to find a blue floral dress, but the user would like a design like this." In addition, when the generation AI conveys a request to the company, the request transmission unit also conveys the user's detailed feedback and desired conditions at the same time. For example, it specifically conveys the user's opinions and areas for improvement. In this way, by conveying the user's detailed feedback and desired conditions to the company, the company can take more specific action.

[0068] When the generation AI communicates a request to a company, the request communication unit also provides information on similar past requests and trends, which the company can use as reference. For example, when the generation AI communicates a request to a company, the request communication unit also provides information on similar past requests and trends. For example, it provides information such as, "Similar requests have been made in the past." Furthermore, when the generation AI communicates a request to a company, the request communication unit also provides information on similar past requests and trends. For example, it refers to current market trends and the requests of other users. In this way, by providing information on similar past requests and trends, the company can respond more appropriately.

[0069] The request transmission unit can promote competition when the generation AI transmits a request to multiple companies simultaneously. For example, when the generation AI transmits a request to a company, the request transmission unit promotes competition by transmitting the request to multiple companies simultaneously. For example, the request transmission unit generates a message such as, "I couldn't find a blue floral dress, but I have transmitted the request to multiple companies." Furthermore, when the generation AI transmits a request to a company, the request transmission unit promotes competition by transmitting the request to multiple companies simultaneously. For example, by transmitting the request to multiple companies simultaneously, the possibility of better products being offered increases. By transmitting the request to multiple companies simultaneously, competition is promoted and the possibility of better products being offered increases.

[0070] The request transmission unit can protect the user's privacy by anonymizing the request content when the generation AI transmits the request to the company. For example, the request transmission unit anonymizes the request content when the generation AI transmits the request to the company, protecting the user's privacy. For example, it generates a message such as "This is a request from an anonymous user." The request transmission unit also anonymizes the request content when the generation AI transmits the request to the company, protecting the user's privacy. For example, it deletes personal information and masks identifying information. In this way, the user's privacy can be protected by anonymizing the request content.

[0071] The request transmission unit can use the emotion estimation function to analyze the user's emotion when transmitting a request in real time and select the optimal request transmission method. The request transmission unit, for example, uses the emotion estimation function to analyze the user's emotion when transmitting a request in real time and select the optimal request transmission method. For example, if the user is dissatisfied, it generates a polite message. Furthermore, the request transmission unit uses the emotion estimation function to analyze the user's emotion when transmitting a request in real time and select the optimal request transmission method. For example, if the user is satisfied, it generates a concise message. In this way, by analyzing the user's emotion in real time and selecting the optimal request transmission method, user satisfaction is improved.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The product search system may further include an incentive providing unit to increase users' purchasing motivation. For example, when a user searches for a specific product, the incentive providing unit provides coupons or discount information related to that product. The incentive providing unit may also award points or benefits when a user meets certain conditions. For example, a user who makes a purchase of more than a certain amount may be awarded points that can be used for the next purchase. This can increase users' purchasing motivation and encourage repeat purchases.

[0074] The product search system can further include a product suggestion unit that takes into account the user's health condition. The product suggestion unit can suggest health-conscious products based on the user's health data, for example. The product suggestion unit can also suggest products that do not contain allergenic ingredients based on the user's allergy information. For example, if the user has a specific food allergy, the product suggestion unit can suggest foods that do not contain that ingredient. This makes it possible to suggest products that take into account the user's health condition.

[0075] The product search system can further include a product suggestion unit that matches the user's lifestyle. The product suggestion unit, for example, suggests appropriate products based on the user's lifestyle data. The product suggestion unit can also suggest related products based on the user's hobbies and interests. For example, camping equipment and mountain climbing equipment can be suggested to a user who enjoys the outdoors. This makes it possible to suggest products that match the user's lifestyle.

[0076] The product search system can further include an emotion suggestion unit that estimates the user's emotion and suggests products based on the estimated emotion. For example, if the user is feeling stressed, the emotion suggestion unit suggests products that have a relaxing effect. Also, if the user is happy, the emotion suggestion unit can suggest products that will further enhance that emotion. For example, if the user seems to be having fun, entertainment-related products are suggested. This makes it possible to suggest products based on the user's emotion.

[0077] The product search system may further include an emotion filtering unit that estimates the user's emotion and filters search results based on the estimated emotion. For example, if the user is feeling anxious, the emotion filtering unit may preferentially display products that give a sense of security. Furthermore, if the user is excited, the emotion filtering unit may preferentially display products that will maintain that excitement. For example, if the user is excited, products that are suitable for an active lifestyle are displayed. This makes it possible to filter search results based on the user's emotion.

[0078] The product search system can further include an emotional advertising unit that estimates the user's emotions and displays advertisements based on the estimated emotions. For example, if the user is feeling down, the emotional advertising unit displays advertisements to lift the user's spirits. Furthermore, if the user is happy, the emotional advertising unit can display advertisements to further enhance the user's joy. For example, if the user is happy, entertainment or travel-related advertisements are displayed. This makes it possible to display advertisements based on the user's emotions.

[0079] The product search system may further include an emotion support unit that estimates the user's emotion and provides customer support based on the estimated emotion. For example, the emotion support unit provides prompt and courteous support when the user is dissatisfied. Furthermore, when the user is satisfied, the emotion support unit can provide support to maintain the user's satisfaction. For example, when the user is satisfied, additional benefits or services are provided. This makes it possible to provide customer support based on the user's emotion.

[0080] The product search system may further include an emotion review unit that estimates the user's emotion and filters product reviews based on the estimated emotion. For example, if the user is feeling anxious, the emotion review unit may preferentially display reviews that give a sense of security. Furthermore, if the user is excited, the emotion review unit may preferentially display reviews that help maintain that excitement. For example, if the user is excited, positive reviews may be displayed. This makes it possible to filter product reviews based on the user's emotion.

[0081] The product search system can further include a prediction unit that predicts future purchases based on the user's purchase history. The prediction unit, for example, analyzes trends in products the user has purchased in the past and suggests products that the user is likely to purchase next. The prediction unit can also analyze the user's purchasing patterns and suggest products that the user is likely to purchase at a specific time. For example, if a user purchases a specific product at the same time every year, products will be suggested based on that time. This makes it possible to predict future purchases based on the user's purchase history and suggest appropriate products.

[0082] The product search system may further include a trend analysis unit that analyzes trends based on the user's social media activity. The trend analysis unit, for example, analyzes keywords and hashtags frequently mentioned by the user on social media to understand current trends. The trend analysis unit may also analyze the activities of the user's followers and friends to suggest related trends. For example, it may suggest products or services that are frequently mentioned by the user's friends. This makes it possible to analyze trends based on the user's social media activity and suggest appropriate products.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The image collection unit collects the user's vague image. For example, the user can describe their image in text or voice. The generation AI analyzes the user's description and understands the user's image. Step 2: The image generator visualizes the collected images. For example, the generator generates an image of a blue floral dress based on the user's description. The input to the generator is a prompt containing the user's description, and the generator generates an image based on that prompt. Step 3: The product search unit searches for products based on the generated image. For example, using an image of a blue floral dress generated by the generation AI, it searches for matching products in online shops and product catalogs. The generation AI uses image recognition technology to search a product database and list matching products. Step 4: If the corresponding product cannot be found, the request transmission unit communicates the request to the company. For example, if the generation AI returns the result "We could not find a blue floral dress," it will generate a message to communicate that request to the company.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. an image collection unit that collects vague images of users; an image generating unit that generates an image from the images collected by the image collecting unit; a product search unit that searches for products based on the image generated by the image generation unit; and a request transmission unit that transmits the request to the company when the corresponding product cannot be found. A system characterized by:

2. The image acquisition unit Ask questions in real time as the user explains, gathering additional information to flesh out the image 2. The system of claim 1.

3. The image acquisition unit Collecting images from users in different cultures or regions and analyzing them from a global perspective 2. The system of claim 1.

4. The image generation unit Analyzing the user's social media posts and generating the image that reflects the user's preferences and style 2. The system of claim 1.

5. The product search unit When the generating AI searches for the product based on the generated image, detailed information about the product (material, size, price, etc.) is also displayed at the same time.

2. The system of claim 1.

6. The request transmission unit When the generating AI conveys the request to the company, it also conveys the user's detailed feedback and desired conditions.

2. The system of claim 1.

7. The image acquisition unit Analyzing the emotions expressed by the user and generating questions to elicit positive emotions 2. The system of claim 1.

8. The product search unit Analyzing the user's emotional response to the search results in real time and providing the most appropriate search results 2. The system of claim 1.

Images