system

Abstract

An object of a system according to an embodiment is to enable a user to specify a product worn by the user and efficiently search for and purchase the product from various EC sites.SOLUTION: A system includes a reception part, an analysis part, a retrieval part, a provision part, and a purchase part. The receiving unit receives an image input from a user. The analysis unit analyzes the image received by the reception unit and specifies the worn commodity. The search unit searches various EC sites for the product specified by the analysis unit. The providing unit provides the user with the search result obtained by the search unit. The purchase unit provides a link for purchasing the product selected by the user from the search result provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that the process of identifying the products a user is wearing, searching for them on various e-commerce sites, and purchasing them is time-consuming and inefficient.

[0005] The system according to the embodiment aims to enable a user to identify products worn by the user and efficiently search for and purchase the products from various e-commerce sites. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, a provision unit, and a purchase unit. The reception unit receives image input from a user. The analysis unit analyzes the image received by the reception unit and identifies the product being worn. The search unit searches various e-commerce sites for the product identified by the analysis unit. The provision unit provides the user with the search results obtained by the search unit. The purchase unit provides a link for purchasing the product selected by the user from the search results provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify products worn by a user and efficiently search for and purchase them from various e-commerce sites. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A product search system according to an embodiment of the present invention automatically identifies products worn by a user based on an image, searches for the products on various e-commerce sites, and allows the user to directly purchase the products. The product search system receives an image input from a user, and a generation AI analyzes the image to identify the products worn by the user. Based on the identified products, the system searches for relevant products on various e-commerce sites and provides the user with search results. The user can then directly purchase the products from the provided search results. For example, the product search system inputs a photo taken by the user with a smartphone or an image found on the Internet. This image is input into a generation AI. The product search system then uses the generation AI to analyze the input image and identify the products worn by the user. The generation AI can extract features from the image and identify clothing, accessories, shoes, etc. The product search system then searches the databases of various e-commerce sites based on the identified products to find matching products. For example, it can search which e-commerce sites sell the identified clothing or accessories. The product search system then displays the search results to the user, allowing the user to select the desired product from among them. For example, it can display a list of search results from multiple e-commerce sites, allowing the user to select the most suitable product. Finally, the product search system provides a link for the user to purchase the selected product, and the user can click on the link to directly access the purchase page. This allows the user to easily purchase the product. The product search system also allows the user to easily identify the product worn by the user based on the image and purchase it directly from various e-commerce sites. This improves the user's purchasing experience and makes shopping more convenient. For example, the user can take a photo of a nice piece of clothing or accessory they see on the street and purchase it right away. In addition, the user can easily search for and purchase products they find on the Internet.

[0029] A product search system according to an embodiment includes a reception unit, an analysis unit, a search unit, a provision unit, and a purchase unit. The reception unit receives image input from a user. The image input from the user includes, but is not limited to, photos taken with a smartphone or images found on the Internet. The reception unit, for example, takes an image using a smartphone camera and inputs the image into the system. The reception unit can also upload images found on the Internet. For example, a user uploads an image through a web browser. The analysis unit uses a generation AI to analyze the image received by the reception unit and identify the product worn. The analysis is performed, for example, based on an image recognition algorithm or a feature extraction method, but is not limited to, examples. For example, the generation AI extracts features of clothing, accessories, shoes, etc. in the image and identifies them. The analysis unit can also use the generation AI to identify the brand or design of the product in the image. For example, the generation AI analyzes logos and patterns in the image to identify the brand. The search unit searches various e-commerce sites for the products identified by the analysis unit. The search is performed based on, for example, the type of search engine and the method of generating a search query, but is not limited to such examples. For example, the search unit searches the databases of various e-commerce sites based on information about the identified product to find the corresponding product. The search unit can also use a generation AI to generate a search query and search the databases of the e-commerce sites. For example, the generation AI generates an optimal search query based on the characteristics of the identified product. The providing unit provides the search results obtained by the search unit to the user. The providing is performed based on, for example, the format of the user interface and the type of information to be provided, but is not limited to such examples. For example, the providing unit displays a list of search results from multiple e-commerce sites, allowing the user to select the most suitable product. The providing unit can also display the search results to the user using the generation AI. For example, the generation AI displays the search results in a visually easy-to-understand manner. The purchasing unit provides a link for the user to purchase the product selected by the user from the search results provided by the providing unit. The purchase is performed based on, for example, the format of the link and the flow of the purchase procedure, but is not limited to such examples.For example, the purchasing unit may provide a link for the user to purchase the product selected by the user, and the user may click on the link to directly access the purchase page. The purchasing unit may also use a generation AI to assist the user in the purchase process. For example, the generation AI may guide the user through the purchase process and smoothly complete the purchase. As a result, the product search system according to the embodiment allows the user to easily identify the product worn by the user based on the image and purchase it directly from various e-commerce sites.

[0030] The providing unit includes a comparison unit that performs price comparisons. The comparison unit allows a user to compare prices on multiple e-commerce sites and select the optimal product. The price comparison is performed based on, for example, selection criteria for comparison targets and a method for acquiring price information, but is not limited to such examples. For example, the comparison unit acquires and compares price information for the identified product from various e-commerce sites. The comparison unit can also use a generation AI to analyze the price information and provide information for selecting the optimal product. For example, the generation AI identifies the product with the best cost performance based on the price information. This allows a user to compare prices on multiple e-commerce sites and select the optimal product.

[0031] The search unit includes an inventory confirmation unit that checks the inventory status. The inventory confirmation unit allows the user to check the inventory status before purchasing a product. The inventory confirmation is performed, for example, based on a method for acquiring inventory information and a checking frequency, but is not limited to such examples. For example, the inventory confirmation unit acquires and checks inventory information for the identified product from various e-commerce sites. The inventory confirmation unit can also use the generation AI to analyze the inventory information and provide it to the user. For example, the generation AI updates the inventory status of the product in real time based on the inventory information. This allows the user to check the inventory status before purchasing the product.

[0032] The analysis unit includes a distinction unit that distinguishes similar products. The distinction unit enables the user to distinguish similar products and obtain accurate product information. The distinction between similar products is performed, for example, based on a similarity scale or distinction criteria, but is not limited to such examples. For example, the distinction unit analyzes the similarity of identified products and distinguishes between similar products. The distinction unit can also use a generation AI to analyze and distinguish between features of similar products. For example, the generation AI analyzes differences in product design and functionality and distinguishes between similar products. This allows the user to distinguish between similar products and obtain accurate product information.

[0033] The reception unit includes a quality improvement unit that improves the quality of the image. The quality improvement unit can improve the quality of the image input by the user and increase the analysis accuracy. The image quality improvement is performed, for example, based on an improvement in resolution or a noise removal method, but is not limited to such examples. For example, the quality improvement unit improves the resolution of the input image. The quality improvement unit can also remove noise from the image and improve the quality using a generation AI. For example, the generation AI detects and removes noise in the image. This can improve the quality of the image input by the user and increase the analysis accuracy.

[0034] The reception unit includes a protection unit that performs privacy protection. The protection unit allows the user to input an image while protecting the user's privacy. Privacy protection is performed, for example, based on data anonymization and access control, but is not limited to these examples. For example, the protection unit anonymizes personal information in the input image. The protection unit can also use a generation AI to detect and protect personal information in the image. For example, the generation AI detects and masks personal information such as faces and names in the image. This allows the user to input an image while protecting their privacy.

[0035] The reception unit can analyze the user's past image input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (camera, gallery, etc.) that the user has frequently used in the past. The reception unit can also automatically select input methods that the user has given high ratings to in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The reception unit can perform filtering based on the user's current environment and situation when inputting an image. For example, if the user is outdoors, the reception unit can apply a filter that removes background noise. The reception unit can also automatically adjust the brightness of the image if the user is in a dark place. The reception unit can also apply image stabilization if the user is moving. This enables optimal image input according to the user's environment. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI.

[0037] When an image is input, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit searches for images using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also search for related images from the text. Furthermore, if the user selects image input, the reception unit can also identify products using image analysis technology. This makes it possible to provide the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0038] When an image is input, the reception unit can automatically suggest candidate locations by referring to the user's past movement history. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. The reception unit can also analyze the user's past movement patterns and suggest optimal candidate locations. This makes it possible to suggest optimal candidate locations based on the user's past movement history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0039] When an image is input, the reception unit can refer to the user's calendar information and make suggestions based on the schedule. The reception unit, for example, refers to the schedule registered in the user's calendar and automatically sets the departure point and destination. The reception unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. The reception unit can also suggest an optimal route based on the schedule based on the user's calendar information. This makes it possible to make optimal suggestions based on the user's calendar information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0040] When an image is input, the reception unit can analyze the user's social media activity and suggest related places. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. This makes it possible to suggest optimal places based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit provides detailed analysis results for expensive products. The analysis unit can also provide concise analysis results for general products. The analysis unit can also provide detailed analysis results for products in which the user is particularly interested. This makes it possible to provide optimal analysis results according to the importance of the product. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. For example, for clothing, the analysis unit applies an analysis algorithm that emphasizes color and design features. For electronic devices, the analysis unit can also apply an analysis algorithm that emphasizes specifications and functional features. For food, the analysis unit can also apply an analysis algorithm that emphasizes ingredients and nutritional value features. This makes it possible to apply the optimal analysis algorithm depending on the product category. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user has given high ratings to in the past. The analysis unit can also improve the accuracy of the analysis by referring to analysis results that the user has been dissatisfied with in the past. The analysis unit can also analyze the user's past analysis results and apply an optimal analysis algorithm. This can improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of product submission. For example, the analysis unit prioritizes analysis of new products. The analysis unit can also prioritize analysis of products during sale periods. The analysis unit can also determine the priority of analysis based on the time of submission of products in which the user is particularly interested. This makes it possible to determine the optimal analysis priority based on the time of product submission. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly relevant products. The analysis unit can also postpone analysis of less relevant products. The analysis unit can also adjust the order of analysis based on the relevance of products in which the user is particularly interested. This makes it possible to provide an optimal analysis order based on the relevance of products. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis results according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0047] The search unit can improve search accuracy by taking into account the interrelationships between products during a search. The search unit provides search results by taking into account, for example, related products. The search unit can also provide search results by taking into account similar products. The search unit can also provide search results by taking into account the interrelationships between products previously searched for by the user. This makes it possible to provide optimal search results based on the interrelationships between products. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0048] When searching, the search unit can perform a search taking into account the attribute information of the person who submitted the product. The search unit provides search results taking into account, for example, the reliability of the person who submitted the product. The search unit can also provide search results taking into account the reputation of the person who submitted the product. The search unit can also provide search results taking into account the past transaction history of the person who submitted the product. This makes it possible to provide optimal search results based on the attribute information of the person who submitted the product. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The search unit can weight the search based on the frequency of submission of products during a search. For example, the search unit can prioritize products submitted more frequently in the search results. The search unit can also prioritize products submitted less frequently in the search results and display them later. The search unit can also weight the search based on the frequency of submission of products in which the user is particularly interested. This makes it possible to provide optimal search results based on the frequency of submission of products. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The search unit can perform a search taking into account the geographical distribution of products. For example, the search unit can prioritize displaying search results for products close to the user's current location. The search unit can also display search results for products in an area specified by the user. The search unit can also prioritize displaying search results for products with high geographical relevance. This makes it possible to provide optimal search results based on the geographical distribution of products. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The search unit can improve the accuracy of the search by referring to literature related to the product during the search. The search unit can provide detailed information about the product based on the related literature, for example. The search unit can also provide product evaluations based on the related literature. The search unit can also provide instructions for using the product based on the related literature. This makes it possible to provide optimal search results based on the literature related to the product. Some or all of the above-mentioned processing in the search unit can be performed using AI, for example, or without using AI.

[0052] The search unit can perform a search taking into account the market value of the product. For example, the search unit can prioritize and display search results for products with high market values. The search unit can also display search results for products with low market values ​​later. The search unit can also display search results based on the market value of products in which the user is particularly interested. This makes it possible to provide optimal search results based on the market value of the product. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The providing unit can optimize the current provision by referring to past provision data at the time of provision. The providing unit, for example, selects the optimal provision method based on the past provision data. The providing unit can also select a provision method that matches the user's preferences based on the past provision data. The providing unit can also improve the accuracy of provision based on the past provision data. This makes it possible to provide the optimal provision method based on the past provision data. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0054] The providing unit can apply different providing methods to each product category when providing the product. For example, for clothing, the providing unit applies a providing method that emphasizes color and design features. For electronic devices, the providing unit can also apply a providing method that emphasizes specifications and functional features. For food, the providing unit can also apply a providing method that emphasizes ingredients and nutritional value features. This makes it possible to provide the optimal providing method according to the product category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0055] The providing unit can provide the product by taking into consideration the attribute information of the submitter when providing the product. The providing unit can display the provision result by taking into consideration, for example, the reliability of the submitter. The providing unit can also display the provision result by taking into consideration the evaluation of the submitter. The providing unit can also display the provision result by taking into consideration the submitter's past transaction history. This makes it possible to provide the optimal provision result based on the attribute information of the submitter of the product. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The providing unit can analyze changes in the offers based on the submission time of the product at the time of offering. For example, the providing unit prioritizes the offering of new products. The providing unit can also prioritize the offering of products during sale periods. The providing unit can also analyze changes in the offers based on the submission time of products in which the user is particularly interested. This makes it possible to provide optimal offering results based on the submission time of the product. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0057] The provision unit can analyze the provision by referring to market data related to the product at the time of provision. The provision unit, for example, provides detailed information about the product based on the related market data. The provision unit can also provide an evaluation of the product based on the related market data. The provision unit can also provide a method of using the product based on the related market data. This makes it possible to provide optimal provision results based on the market data related to the product. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The providing unit can analyze the provision taking into account the technical maturity of the product when providing the product. For example, the providing unit can prioritize displaying the provision results of technologically mature products. The providing unit can also display the provision results of technologically immature products later. The providing unit can also display the provision results based on the technical maturity of products in which the user is particularly interested. This makes it possible to provide optimal provision results based on the technical maturity of the product. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0059] At the time of purchase, the purchasing unit can analyze the user's past purchasing behavior and select the optimal purchasing method. For example, the purchasing unit preferentially suggests purchasing methods that the user has used in the past. The purchasing unit can also select the optimal purchasing method based on the user's past purchasing behavior. The purchasing unit can also analyze the user's past purchasing history and suggest the most efficient purchasing method. This makes it possible to provide the optimal purchasing method based on the user's past purchasing behavior. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0060] The purchasing unit can customize the purchasing method based on the user's current living situation at the time of purchase. For example, if the user is busy, the purchasing unit can suggest a quick purchasing method. Also, if the user is relaxed, the purchasing unit can suggest a detailed purchasing method. The purchasing unit can also customize the optimal purchasing method based on the user's current living situation. This makes it possible to provide the optimal purchasing method based on the user's current living situation. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The purchasing unit can improve the purchasing method by reflecting user feedback at the time of purchase. The purchasing unit improves the purchasing method, for example, based on feedback provided by the user in the past. The purchasing unit can also improve the purchasing method by reflecting user feedback in real time. The purchasing unit can also analyze user feedback and suggest the optimal purchasing method. This makes it possible to provide the optimal purchasing method based on user feedback. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0062] At the time of purchase, the purchasing unit can select the optimal purchasing method taking into account the user's geographical location information. The purchasing unit can suggest the optimal purchasing method based on, for example, the user's current location. The purchasing unit can also provide purchasing options for a region specified by the user. The purchasing unit can also prioritize purchasing options that are highly geographically relevant. This makes it possible to provide the optimal purchasing method based on the user's geographical location information. Some or all of the above-described processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0063] At the time of purchase, the purchasing unit can analyze the user's social media activity to suggest a purchasing method. The purchasing unit can suggest purchasing options based on, for example, the location where the user checked in on social media. The purchasing unit can also analyze the content of the user's social media posts to suggest related purchasing options. The purchasing unit can also suggest purchasing options based on the activity of the user's friends on social media. This makes it possible to provide the optimal purchasing method based on the user's social media activity. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI or without using AI.

[0064] The purchasing unit can customize the purchasing method by reflecting the user's past feedback at the time of purchase. The purchasing unit customizes the purchasing method based on, for example, feedback provided by the user in the past. The purchasing unit can also customize the purchasing method by reflecting the user's feedback in real time. The purchasing unit can also analyze the user's feedback and suggest the optimal purchasing method. This makes it possible to provide the optimal purchasing method based on the user's past feedback. Some or all of the above-described processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0065] The comparison unit can improve the accuracy of price comparisons by taking into account the interrelationships between products. For example, the comparison unit performs price comparisons by taking into account related products. The comparison unit can also perform price comparisons by taking into account similar products. The comparison unit can also perform price comparisons by taking into account the interrelationships between products that the user has previously compared. This makes it possible to provide optimal price comparison results based on the interrelationships between products. Some or all of the above-described processing in the comparison unit may be performed using AI, for example, or may be performed without using AI.

[0066] When comparing prices, the comparison unit can take into account the attribute information of the person who submitted the product. The comparison unit can, for example, make price comparisons taking into account the reliability of the person who submitted the product. The comparison unit can also make price comparisons taking into account the evaluation of the person who submitted the product. The comparison unit can also make price comparisons taking into account the past transaction history of the person who submitted the product. This makes it possible to provide optimal price comparison results based on the attribute information of the person who submitted the product. Some or all of the above-mentioned processing in the comparison unit can be performed, for example, using AI, or can be performed without using AI.

[0067] The comparison unit can take the geographical distribution of products into consideration when comparing prices. For example, the comparison unit can prioritize displaying price comparison results for products close to the user's current location. The comparison unit can also display price comparison results for products in an area specified by the user. The comparison unit can also prioritize displaying price comparison results for products with high geographical relevance. This makes it possible to provide optimal price comparison results based on the geographical distribution of products. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The comparison unit can improve the accuracy of the comparison by referring to literature related to the product when comparing prices. For example, the comparison unit provides detailed information about the product based on the literature. The comparison unit can also provide product evaluations based on the literature. The comparison unit can also provide product usage instructions based on the literature. This makes it possible to provide optimal price comparison results based on the literature related to the product. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, for example, or may be performed without using AI.

[0069] The inventory confirmation unit can improve the accuracy of inventory confirmation by taking into account the interrelationships between products. The inventory confirmation unit, for example, performs inventory confirmation taking into account related products. The inventory confirmation unit can also perform inventory confirmation taking into account similar products. The inventory confirmation unit can also perform inventory confirmation taking into account the interrelationships between products that the user has previously checked. This makes it possible to provide optimal inventory confirmation results based on the interrelationships between products. Some or all of the above-described processing in the inventory confirmation unit may be performed using, for example, AI, or may be performed without using AI.

[0070] When checking inventory, the inventory confirmation unit can perform the check taking into consideration the attribute information of the person who submitted the product. The inventory confirmation unit can perform the stock confirmation taking into consideration, for example, the reliability of the person who submitted the product. The inventory confirmation unit can also perform the stock confirmation taking into consideration the evaluation of the person who submitted the product. The inventory confirmation unit can also perform the stock confirmation taking into consideration the past transaction history of the person who submitted the product. This makes it possible to provide optimal inventory confirmation results based on the attribute information of the person who submitted the product. Some or all of the above-mentioned processing in the inventory confirmation unit can be performed using AI, for example, or can be performed without using AI.

[0071] The inventory confirmation unit can take into consideration the geographical distribution of products when checking inventory. For example, the inventory confirmation unit can prioritize displaying inventory information for products close to the user's current location. The inventory confirmation unit can also display inventory information for products in an area specified by the user. The inventory confirmation unit can also prioritize displaying inventory information for products with high geographical relevance. This makes it possible to provide optimal inventory confirmation results based on the geographical distribution of products. Some or all of the above-described processing in the inventory confirmation unit may be performed using AI, for example, or may be performed without using AI.

[0072] The inventory confirmation unit can improve the accuracy of the confirmation by referring to literature related to the product when checking the inventory. The inventory confirmation unit provides detailed information about the product based on the related literature, for example. The inventory confirmation unit can also provide product evaluations based on the related literature. The inventory confirmation unit can also provide product usage instructions based on the related literature. This makes it possible to provide optimal inventory confirmation results based on the literature related to the product. Some or all of the above-mentioned processing in the inventory confirmation unit may be performed, for example, using AI, or may be performed without using AI.

[0073] The distinction unit can improve the accuracy of distinction by taking into account the interrelationships between products when distinguishing between them. The distinction unit, for example, makes distinctions by taking into account related products. The distinction unit can also make distinctions by taking into account similar products. The distinction unit can also make distinctions by taking into account the interrelationships between products that the user has previously distinguished. This makes it possible to provide optimal distinction results based on the interrelationships between products. Some or all of the above-mentioned processing in the distinction unit may be performed using AI, for example, or may be performed without using AI.

[0074] The classification unit can perform classification taking into consideration attribute information of the submitter of the product. The classification unit performs classification taking into consideration, for example, the reliability of the submitter. The classification unit can also perform classification taking into consideration the evaluation of the submitter. The classification unit can also perform classification taking into consideration the submitter's past transaction history. This makes it possible to provide optimal classification results based on the attribute information of the submitter of the product. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The differentiation unit can differentiate products taking into consideration the geographical distribution of the products. For example, the differentiation unit can prioritize displaying differentiation results for products close to the user's current location. The differentiation unit can also display differentiation results for products in an area specified by the user. The differentiation unit can also prioritize displaying differentiation results for products with high geographical relevance. This makes it possible to provide optimal differentiation results based on the geographical distribution of the products. Some or all of the above-described processing in the differentiation unit may be performed using AI, for example, or may be performed without using AI.

[0076] The distinction unit can improve the accuracy of the distinction by referring to literature related to the product when distinguishing. The distinction unit, for example, provides detailed information about the product based on the related literature. The distinction unit can also provide product evaluations based on the related literature. The distinction unit can also provide instructions for using the product based on the related literature. This makes it possible to provide optimal distinction results based on literature related to the product. Some or all of the above-mentioned processing in the distinction unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The quality improvement unit can improve the accuracy of quality improvement by taking into account the interrelationships between images when improving image quality. The quality improvement unit performs quality improvement by taking into account, for example, related images. The quality improvement unit can also perform quality improvement by taking into account similar images. The quality improvement unit can also perform quality improvement by taking into account the interrelationships between images for which the user has previously performed quality improvement. This makes it possible to provide optimal quality improvement results based on the interrelationships between images. Some or all of the above-mentioned processing in the quality improvement unit may be performed using, for example, AI, or may be performed without using AI.

[0078] When improving image quality, the quality improvement unit can improve the quality by taking into account attribute information of the image submitter. The quality improvement unit can improve the quality by taking into account, for example, the submitter's reliability. The quality improvement unit can also improve the quality by taking into account the submitter's reputation. The quality improvement unit can also improve the quality by taking into account the submitter's past transaction history. This makes it possible to provide optimal quality improvement results based on the attribute information of the image submitter. Some or all of the above-mentioned processing in the quality improvement unit may be performed, for example, using AI, or may be performed without using AI.

[0079] The quality improvement unit can improve image quality by taking into account the geographic distribution of images. For example, the quality improvement unit can prioritize displaying quality improvement results for images close to the user's current location. The quality improvement unit can also display quality improvement results for images in a region specified by the user. The quality improvement unit can also prioritize displaying quality improvement results for images with high geographic relevance. This makes it possible to provide optimal quality improvement results based on the geographic distribution of images. Some or all of the above-described processing in the quality improvement unit may be performed using AI, for example, or may be performed without using AI.

[0080] The quality improvement unit can improve the accuracy of quality improvement by referring to related literature of the image when improving image quality. The quality improvement unit provides detailed information about the image based on the related literature, for example. The quality improvement unit can also provide an evaluation of the image based on the related literature. The quality improvement unit can also provide a method for using the image based on the related literature. This makes it possible to provide optimal quality improvement results based on the related literature of the image. Some or all of the above-mentioned processing in the quality improvement unit may be performed using AI, for example, or may be performed without using AI.

[0081] The protection unit can improve the accuracy of privacy protection by taking into account the interrelationships between images. The protection unit performs privacy protection by taking into account, for example, related images. The protection unit can also perform privacy protection by taking into account similar images. The protection unit can also perform privacy protection by taking into account the interrelationships between images previously protected by the user. This makes it possible to provide optimal privacy protection results based on the interrelationships between images. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The protection unit can perform privacy protection by taking into account attribute information of the image submitter. The protection unit can perform privacy protection by taking into account, for example, the trustworthiness of the submitter. The protection unit can also perform privacy protection by taking into account the submitter's reputation. The protection unit can also perform privacy protection by taking into account the submitter's past transaction history. This makes it possible to provide optimal privacy protection results based on the attribute information of the image submitter. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without using AI.

[0083] The protection unit can perform privacy protection by taking into account the geographical distribution of images. The protection unit performs privacy protection based on, for example, the user's current location. The protection unit can also perform privacy protection for images in a region specified by the user. The protection unit can also perform privacy protection for images with high geographical relevance. This makes it possible to provide optimal privacy protection results based on the geographical distribution of images. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The protection unit can improve the accuracy of privacy protection by referring to related literature of the image during privacy protection. For example, the protection unit provides detailed information about the image based on the related literature. The protection unit can also provide an evaluation of the image based on the related literature. The protection unit can also provide a method for using the image based on the related literature. This makes it possible to provide optimal privacy protection results based on the related literature of the image. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI.

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

[0086] The reception unit may also include a voice recognition unit that accepts voice input from the user. The voice recognition unit allows the user to describe a product aloud, thereby identifying the product without inputting an image. For example, if the user voice-inputs "red dress," the voice recognition unit analyzes the voice and searches for the corresponding product. The voice recognition unit can also estimate the user's emotion from the user's voice and provide search results according to the emotion. For example, if the user is excited, visually stimulating search results are displayed. This allows the user to identify a product using voice input and obtain search results according to their emotion.

[0087] The providing unit may also include a recommendation unit that analyzes the user's purchasing history and recommends optimal products based on past purchasing trends. The recommendation unit recommends highly relevant products based on products the user has previously purchased or viewed. For example, if the user has purchased a lot of sporting goods in the past, the recommendation unit will recommend new sporting goods. The recommendation unit can also estimate the user's emotions from the user's purchasing history and recommend products according to the emotions. For example, if the user has previously purchased relaxation goods when feeling stressed, the recommendation unit will recommend relaxation goods when it is estimated that the user is feeling stressed. This makes it possible to recommend optimal products based on the user's purchasing history.

[0088] The search unit may also include a location information search unit that uses the user's current location information to search for products available for purchase in nearby stores. The location information search unit searches for products that are in stock in nearby stores based on the user's current location. For example, if the user is looking for a specific piece of clothing, the location information search unit displays which nearby stores the clothing can be purchased at. The location information search unit can also estimate the user's emotions from the user's location information and provide search results according to the emotion. For example, if the user is in a hurry, information about the nearest store will be displayed preferentially. This allows the user to search for the most suitable product based on their current location.

[0089] The analysis unit may also include a preference learning unit that learns the user's preferences and identifies products based on the preferences. The preference learning unit learns the user's preferences based on the products the user has previously selected and their ratings. For example, it may learn the color and design trends of products the user has previously selected and identify similar products. The preference learning unit may also infer emotions from the user's preferences and identify products that correspond to the emotions. For example, if the user feels like relaxing, it may identify products that will help them relax. This makes it possible to identify the optimal products based on the user's preferences.

[0090] The providing unit may also include a social media analysis unit that analyzes the user's social media activity and recommends related products. The social media analysis unit recommends related products based on the posts and "likes" the user has shared on social media. For example, if the user has given many "likes" to posts from a particular brand, the unit recommends products from that brand. The social media analysis unit can also estimate the user's emotions from the user's social media activity and recommend products according to the emotions. For example, if it is estimated that the user is having fun, the unit recommends entertainment-related products. This makes it possible to recommend optimal products based on the user's social media activity.

[0091] The processing flow of the first embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts image input from the user. Image input from the user includes photos taken with a smartphone and images found on the Internet. For example, an image can be taken using a smartphone camera and entered into the system. Images found on the Internet can also be uploaded. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit and identify the products worn. The analysis is performed based on image recognition algorithms and feature extraction methods. For example, the generation AI extracts features of the clothes, accessories, shoes, etc. in the image and identifies them. It can also identify the brand and design of the products in the image. For example, the generation AI analyzes logos and patterns in the image to identify the brand. Step 3: The search unit searches various e-commerce sites for the products identified by the analysis unit. The search is performed based on the type of search engine and the method of generating the search query. For example, based on the information about the identified product, the databases of various e-commerce sites are searched to find the corresponding product. It is also possible to use generation AI to generate a search query and search the databases of e-commerce sites. Step 4: The provision unit provides the search results obtained by the search unit to the user. The provision is based on the format of the user interface and the type of information to be provided. For example, search results from multiple e-commerce sites can be displayed as a list, allowing the user to select the most suitable product. In addition, the search results are displayed visually in an easy-to-understand manner using generation AI. Step 5: The purchasing unit provides a link for the user to purchase the product selected from the search results provided by the providing unit. The purchase is made based on the format of the link and the flow of the purchase procedure. For example, a link for purchasing the product selected by the user is provided, and the user can click on the link to directly access the purchase page. Generative AI can also be used to assist with the purchase procedure.

[0093] (Example 2) A product search system according to an embodiment of the present invention automatically identifies products worn by a user based on an image, searches for the products on various e-commerce sites, and allows the user to directly purchase the products. The product search system receives an image input from a user, and a generation AI analyzes the image to identify the products worn by the user. Based on the identified products, the system searches for relevant products on various e-commerce sites and provides the user with search results. The user can then directly purchase the products from the provided search results. For example, the product search system inputs a photo taken by the user with a smartphone or an image found on the Internet. This image is input into a generation AI. The product search system then uses the generation AI to analyze the input image and identify the products worn by the user. The generation AI can extract features from the image and identify clothing, accessories, shoes, etc. The product search system then searches the databases of various e-commerce sites based on the identified products to find matching products. For example, it can search which e-commerce sites sell the identified clothing or accessories. The product search system then displays the search results to the user, allowing the user to select the desired product from among them. For example, it can display a list of search results from multiple e-commerce sites, allowing the user to select the most suitable product. Finally, the product search system provides a link for the user to purchase the selected product, and the user can click on the link to directly access the purchase page. This allows the user to easily purchase the product. The product search system also allows the user to easily identify the product worn by the user based on the image and purchase it directly from various e-commerce sites. This improves the user's purchasing experience and makes shopping more convenient. For example, the user can take a photo of a nice piece of clothing or accessory they see on the street and purchase it right away. In addition, the user can easily search for and purchase products they find on the Internet.

[0094] A product search system according to an embodiment includes a reception unit, an analysis unit, a search unit, a provision unit, and a purchase unit. The reception unit receives image input from a user. The image input from the user includes, but is not limited to, photos taken with a smartphone or images found on the Internet. The reception unit, for example, takes an image using a smartphone camera and inputs the image into the system. The reception unit can also upload images found on the Internet. For example, a user uploads an image through a web browser. The analysis unit uses a generation AI to analyze the image received by the reception unit and identify the product worn. The analysis is performed, for example, based on an image recognition algorithm or a feature extraction method, but is not limited to, examples. For example, the generation AI extracts features of clothing, accessories, shoes, etc. in the image and identifies them. The analysis unit can also use the generation AI to identify the brand or design of the product in the image. For example, the generation AI analyzes logos and patterns in the image to identify the brand. The search unit searches various e-commerce sites for the products identified by the analysis unit. The search is performed based on, for example, the type of search engine and the method of generating a search query, but is not limited to such examples. For example, the search unit searches the databases of various e-commerce sites based on information about the identified product to find the corresponding product. The search unit can also use a generation AI to generate a search query and search the databases of the e-commerce sites. For example, the generation AI generates an optimal search query based on the characteristics of the identified product. The providing unit provides the search results obtained by the search unit to the user. The providing is performed based on, for example, the format of the user interface and the type of information to be provided, but is not limited to such examples. For example, the providing unit displays a list of search results from multiple e-commerce sites, allowing the user to select the most suitable product. The providing unit can also display the search results to the user using the generation AI. For example, the generation AI displays the search results in a visually easy-to-understand manner. The purchasing unit provides a link for the user to purchase the product selected by the user from the search results provided by the providing unit. The purchase is performed based on, for example, the format of the link and the flow of the purchase procedure, but is not limited to such examples.For example, the purchasing unit may provide a link for the user to purchase the product selected by the user, and the user may click on the link to directly access the purchase page. The purchasing unit may also use a generation AI to assist the user in the purchase process. For example, the generation AI may guide the user through the purchase process and smoothly complete the purchase. As a result, the product search system according to the embodiment allows the user to easily identify the product worn by the user based on the image and purchase it directly from various e-commerce sites.

[0095] The providing unit includes a comparison unit that performs price comparisons. The comparison unit allows a user to compare prices on multiple e-commerce sites and select the optimal product. The price comparison is performed based on, for example, selection criteria for comparison targets and a method for acquiring price information, but is not limited to such examples. For example, the comparison unit acquires and compares price information for the identified product from various e-commerce sites. The comparison unit can also use a generation AI to analyze the price information and provide information for selecting the optimal product. For example, the generation AI identifies the product with the best cost performance based on the price information. This allows a user to compare prices on multiple e-commerce sites and select the optimal product.

[0096] The search unit includes an inventory confirmation unit that checks the inventory status. The inventory confirmation unit allows the user to check the inventory status before purchasing a product. The inventory confirmation is performed, for example, based on a method for acquiring inventory information and a checking frequency, but is not limited to such examples. For example, the inventory confirmation unit acquires and checks inventory information for the identified product from various e-commerce sites. The inventory confirmation unit can also use the generation AI to analyze the inventory information and provide it to the user. For example, the generation AI updates the inventory status of the product in real time based on the inventory information. This allows the user to check the inventory status before purchasing the product.

[0097] The analysis unit includes a distinction unit that distinguishes similar products. The distinction unit enables the user to distinguish similar products and obtain accurate product information. The distinction between similar products is performed, for example, based on a similarity scale or distinction criteria, but is not limited to such examples. For example, the distinction unit analyzes the similarity of identified products and distinguishes between similar products. The distinction unit can also use a generation AI to analyze and distinguish between features of similar products. For example, the generation AI analyzes differences in product design and functionality and distinguishes between similar products. This allows the user to distinguish between similar products and obtain accurate product information.

[0098] The reception unit includes a quality improvement unit that improves the quality of the image. The quality improvement unit can improve the quality of the image input by the user and increase the analysis accuracy. The image quality improvement is performed, for example, based on an improvement in resolution or a noise removal method, but is not limited to such examples. For example, the quality improvement unit improves the resolution of the input image. The quality improvement unit can also remove noise from the image and improve the quality using a generation AI. For example, the generation AI detects and removes noise in the image. This can improve the quality of the image input by the user and increase the analysis accuracy.

[0099] The reception unit includes a protection unit that performs privacy protection. The protection unit allows the user to input an image while protecting the user's privacy. Privacy protection is performed, for example, based on data anonymization and access control, but is not limited to these examples. For example, the protection unit anonymizes personal information in the input image. The protection unit can also use a generation AI to detect and protect personal information in the image. For example, the generation AI detects and masks personal information such as faces and names in the image. This allows the user to input an image while protecting their privacy.

[0100] The reception unit can estimate the user's emotions and adjust the timing of image input based on the estimated user emotions. For example, if the user is excited, the reception unit can immediately send a notification prompting the user to input an image. Furthermore, if the user is relaxed, the reception unit can adjust the timing of image input to match the user's pace. Furthermore, if the user is feeling stressed, the reception unit can suggest postponing image input. This makes it possible to prompt the user to input an image at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The reception unit can analyze the user's past image input history and select the optimal input method. For example, the reception unit preferentially suggests input methods (camera, gallery, etc.) that the user has frequently used in the past. The reception unit can also automatically select input methods that the user has given high ratings to in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The reception unit can perform filtering based on the user's current environment and situation when inputting an image. For example, if the user is outdoors, the reception unit can apply a filter that removes background noise. The reception unit can also automatically adjust the brightness of the image if the user is in a dark place. The reception unit can also apply image stabilization if the user is moving. This enables optimal image input according to the user's environment. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI.

[0103] When an image is input, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit searches for images using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also search for related images from the text. Furthermore, if the user selects image input, the reception unit can also identify products using image analysis technology. This makes it possible to provide the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0104] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This makes it possible to provide an optimal interface according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] When an image is input, the reception unit can automatically suggest candidate locations by referring to the user's past movement history. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidate locations. The reception unit can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. The reception unit can also analyze the user's past movement patterns and suggest optimal candidate locations. This makes it possible to suggest optimal candidate locations based on the user's past movement history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0106] When an image is input, the reception unit can refer to the user's calendar information and make suggestions based on the schedule. The reception unit, for example, refers to the schedule registered in the user's calendar and automatically sets the departure point and destination. The reception unit can also suggest locations related to a specific event as candidate locations based on the user's calendar information. The reception unit can also suggest an optimal route based on the schedule based on the user's calendar information. This makes it possible to make optimal suggestions based on the user's calendar information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0107] When an image is input, the reception unit can analyze the user's social media activity and suggest related places. For example, the reception unit can suggest places where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related places as candidate locations. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. This makes it possible to suggest optimal places based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0108] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can display analysis results that proceed at a leisurely pace. If the user is in a hurry, the analysis unit can also display analysis results that emphasize the shortest route. If the user is excited, the analysis unit can also display analysis results that add visually stimulating effects. This makes it possible to provide optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit provides detailed analysis results for expensive products. The analysis unit can also provide concise analysis results for general products. The analysis unit can also provide detailed analysis results for products in which the user is particularly interested. This makes it possible to provide optimal analysis results according to the importance of the product. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0110] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. For example, for clothing, the analysis unit applies an analysis algorithm that emphasizes color and design features. For electronic devices, the analysis unit can also apply an analysis algorithm that emphasizes specifications and functional features. For food, the analysis unit can also apply an analysis algorithm that emphasizes ingredients and nutritional value features. This makes it possible to apply the optimal analysis algorithm depending on the product category. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0111] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user has given high ratings to in the past. The analysis unit can also improve the accuracy of the analysis by referring to analysis results that the user has been dissatisfied with in the past. The analysis unit can also analyze the user's past analysis results and apply an optimal analysis algorithm. This can improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI.

[0112] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. This makes it possible to provide an optimal length of analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] During analysis, the analysis unit can determine the priority of analysis based on the time of product submission. For example, the analysis unit prioritizes analysis of new products. The analysis unit can also prioritize analysis of products during sale periods. The analysis unit can also determine the priority of analysis based on the time of submission of products in which the user is particularly interested. This makes it possible to determine the optimal analysis priority based on the time of product submission. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0114] During analysis, the analysis unit can adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly relevant products. The analysis unit can also postpone analysis of less relevant products. The analysis unit can also adjust the order of analysis based on the relevance of products in which the user is particularly interested. This makes it possible to provide an optimal analysis order based on the relevance of products. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0115] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis results according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0116] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is relaxed, the search unit can provide a wide range of search results. If the user is in a hurry, the search unit can also prioritize the most relevant search results. If the user is excited, the search unit can also provide visually stimulating search results. This makes it possible to provide optimal search criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The search unit can improve search accuracy by taking into account the interrelationships between products during a search. The search unit provides search results by taking into account, for example, related products. The search unit can also provide search results by taking into account similar products. The search unit can also provide search results by taking into account the interrelationships between products previously searched for by the user. This makes it possible to provide optimal search results based on the interrelationships between products. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0118] When searching, the search unit can perform a search taking into account the attribute information of the person who submitted the product. The search unit provides search results taking into account, for example, the reliability of the person who submitted the product. The search unit can also provide search results taking into account the reputation of the person who submitted the product. The search unit can also provide search results taking into account the past transaction history of the person who submitted the product. This makes it possible to provide optimal search results based on the attribute information of the person who submitted the product. Some or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI.

[0119] The search unit can weight the search based on the frequency of submission of products during a search. For example, the search unit can prioritize products submitted more frequently in the search results. The search unit can also prioritize products submitted less frequently in the search results and display them later. The search unit can also weight the search based on the frequency of submission of products in which the user is particularly interested. This makes it possible to provide optimal search results based on the frequency of submission of products. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0120] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, when the user is relaxed, the search unit provides a wide range of search results. Furthermore, when the user is in a hurry, the search unit can prioritize displaying the most relevant search results. Furthermore, when the user is excited, the search unit can prioritize displaying visually stimulating search results. This makes it possible to provide an optimal order of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0121] The search unit can perform a search taking into account the geographical distribution of products. For example, the search unit can prioritize displaying search results for products close to the user's current location. The search unit can also display search results for products in an area specified by the user. The search unit can also prioritize displaying search results for products with high geographical relevance. This makes it possible to provide optimal search results based on the geographical distribution of products. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0122] The search unit can improve the accuracy of the search by referring to literature related to the product during the search. The search unit can provide detailed information about the product based on the related literature, for example. The search unit can also provide product evaluations based on the related literature. The search unit can also provide instructions for using the product based on the related literature. This makes it possible to provide optimal search results based on the literature related to the product. Some or all of the above-mentioned processing in the search unit can be performed using AI, for example, or without using AI.

[0123] The search unit can perform a search taking into account the market value of the product. For example, the search unit can prioritize and display search results for products with high market values. The search unit can also display search results for products with low market values ​​later. The search unit can also display search results based on the market value of products in which the user is particularly interested. This makes it possible to provide optimal search results based on the market value of the product. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI.

[0124] The providing unit can estimate the user's emotions and adjust the display method of the presentation based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to provide an optimal display method of the presentation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0125] The providing unit can optimize the current provision by referring to past provision data at the time of provision. The providing unit, for example, selects the optimal provision method based on the past provision data. The providing unit can also select a provision method that matches the user's preferences based on the past provision data. The providing unit can also improve the accuracy of provision based on the past provision data. This makes it possible to provide the optimal provision method based on the past provision data. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0126] The providing unit can apply different providing methods to each product category when providing the product. For example, for clothing, the providing unit applies a providing method that emphasizes color and design features. For electronic devices, the providing unit can also apply a providing method that emphasizes specifications and functional features. For food, the providing unit can also apply a providing method that emphasizes ingredients and nutritional value features. This makes it possible to provide the optimal providing method according to the product category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0127] The providing unit can provide the product by taking into consideration the attribute information of the submitter when providing the product. The providing unit can display the provision result by taking into consideration, for example, the reliability of the submitter. The providing unit can also display the provision result by taking into consideration the evaluation of the submitter. The providing unit can also display the provision result by taking into consideration the submitter's past transaction history. This makes it possible to provide the optimal provision result based on the attribute information of the submitter of the product. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0128] The providing unit can estimate the user's emotions and adjust the importance of the results provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit displays a wide range of results. Furthermore, when the user is in a hurry, the providing unit can prioritize displaying the most relevant results. Furthermore, when the user is excited, the providing unit can prioritize displaying visually stimulating results. This makes it possible to provide the optimal importance of results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0129] The providing unit can analyze changes in the offers based on the submission time of the product at the time of offering. For example, the providing unit prioritizes the offering of new products. The providing unit can also prioritize the offering of products during sale periods. The providing unit can also analyze changes in the offers based on the submission time of products in which the user is particularly interested. This makes it possible to provide optimal offering results based on the submission time of the product. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0130] The provision unit can analyze the provision by referring to market data related to the product at the time of provision. The provision unit, for example, provides detailed information about the product based on the related market data. The provision unit can also provide an evaluation of the product based on the related market data. The provision unit can also provide a method of using the product based on the related market data. This makes it possible to provide optimal provision results based on the market data related to the product. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI.

[0131] The providing unit can analyze the provision taking into account the technical maturity of the product when providing the product. For example, the providing unit can prioritize displaying the provision results of technologically mature products. The providing unit can also display the provision results of technologically immature products later. The providing unit can also display the provision results based on the technical maturity of products in which the user is particularly interested. This makes it possible to provide optimal provision results based on the technical maturity of the product. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0132] The purchasing unit can estimate the user's emotions and adjust the purchasing method based on the estimated user emotions. For example, if the user is relaxed, the purchasing unit can provide detailed purchasing instructions. If the user is in a hurry, the purchasing unit can also provide concise purchasing instructions. If the user is excited, the purchasing unit can also provide visually stimulating purchasing instructions. This makes it possible to provide an optimal purchasing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0133] At the time of purchase, the purchasing unit can analyze the user's past purchasing behavior and select the optimal purchasing method. For example, the purchasing unit preferentially suggests purchasing methods that the user has used in the past. The purchasing unit can also select the optimal purchasing method based on the user's past purchasing behavior. The purchasing unit can also analyze the user's past purchasing history and suggest the most efficient purchasing method. This makes it possible to provide the optimal purchasing method based on the user's past purchasing behavior. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0134] The purchasing unit can customize the purchasing method based on the user's current living situation at the time of purchase. For example, if the user is busy, the purchasing unit can suggest a quick purchasing method. Also, if the user is relaxed, the purchasing unit can suggest a detailed purchasing method. The purchasing unit can also customize the optimal purchasing method based on the user's current living situation. This makes it possible to provide the optimal purchasing method based on the user's current living situation. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0135] The purchasing unit can improve the purchasing method by reflecting user feedback at the time of purchase. The purchasing unit improves the purchasing method, for example, based on feedback provided by the user in the past. The purchasing unit can also improve the purchasing method by reflecting user feedback in real time. The purchasing unit can also analyze user feedback and suggest the optimal purchasing method. This makes it possible to provide the optimal purchasing method based on user feedback. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0136] The purchasing unit can estimate the user's emotions and determine purchase priorities based on the estimated user emotions. For example, if the user is relaxed, the purchasing unit can provide a wide range of purchasing options. If the user is in a hurry, the purchasing unit can also provide the most relevant purchasing options preferentially. If the user is excited, the purchasing unit can also provide the most visually stimulating purchasing options preferentially. This allows optimal purchasing priorities to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0137] At the time of purchase, the purchasing unit can select the optimal purchasing method taking into account the user's geographical location information. The purchasing unit can suggest the optimal purchasing method based on, for example, the user's current location. The purchasing unit can also provide purchasing options for a region specified by the user. The purchasing unit can also prioritize purchasing options that are highly geographically relevant. This makes it possible to provide the optimal purchasing method based on the user's geographical location information. Some or all of the above-described processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0138] At the time of purchase, the purchasing unit can analyze the user's social media activity to suggest a purchasing method. The purchasing unit can suggest purchasing options based on, for example, the location where the user checked in on social media. The purchasing unit can also analyze the content of the user's social media posts to suggest related purchasing options. The purchasing unit can also suggest purchasing options based on the activity of the user's friends on social media. This makes it possible to provide the optimal purchasing method based on the user's social media activity. Some or all of the above-mentioned processing in the purchasing unit may be performed, for example, using AI or without using AI.

[0139] The purchasing unit can customize the purchasing method by reflecting the user's past feedback at the time of purchase. The purchasing unit customizes the purchasing method based on, for example, feedback provided by the user in the past. The purchasing unit can also customize the purchasing method by reflecting the user's feedback in real time. The purchasing unit can also analyze the user's feedback and suggest the optimal purchasing method. This makes it possible to provide the optimal purchasing method based on the user's past feedback. Some or all of the above-described processing in the purchasing unit may be performed, for example, using AI, or may be performed without using AI.

[0140] The comparison unit can estimate the user's emotions and adjust the price comparison criteria based on the estimated user emotions. For example, if the user is relaxed, the comparison unit can provide a wide range of price options. If the user is in a hurry, the comparison unit can also prioritize providing the most relevant price options. If the user is excited, the comparison unit can also prioritize providing visually stimulating price options. This makes it possible to provide optimal price comparison criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0141] The comparison unit can improve the accuracy of price comparisons by taking into account the interrelationships between products. For example, the comparison unit performs price comparisons by taking into account related products. The comparison unit can also perform price comparisons by taking into account similar products. The comparison unit can also perform price comparisons by taking into account the interrelationships between products that the user has previously compared. This makes it possible to provide optimal price comparison results based on the interrelationships between products. Some or all of the above-described processing in the comparison unit may be performed using AI, for example, or may be performed without using AI.

[0142] When comparing prices, the comparison unit can take into account the attribute information of the person who submitted the product. The comparison unit can, for example, make price comparisons taking into account the reliability of the person who submitted the product. The comparison unit can also make price comparisons taking into account the evaluation of the person who submitted the product. The comparison unit can also make price comparisons taking into account the past transaction history of the person who submitted the product. This makes it possible to provide optimal price comparison results based on the attribute information of the person who submitted the product. Some or all of the above-mentioned processing in the comparison unit can be performed, for example, using AI, or can be performed without using AI.

[0143] The comparison unit can estimate the user's emotions during price comparison and adjust the order in which price comparison results are displayed based on the estimated user emotions. For example, if the user is relaxed, the comparison unit can provide a wide range of price options. If the user is in a hurry, the comparison unit can also prioritize displaying the most relevant price options. If the user is excited, the comparison unit can also prioritize displaying visually stimulating price options. This makes it possible to provide an optimal order of price comparison results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0144] The comparison unit can take the geographical distribution of products into consideration when comparing prices. For example, the comparison unit can prioritize displaying price comparison results for products close to the user's current location. The comparison unit can also display price comparison results for products in an area specified by the user. The comparison unit can also prioritize displaying price comparison results for products with high geographical relevance. This makes it possible to provide optimal price comparison results based on the geographical distribution of products. Some or all of the above-described processing in the comparison unit may be performed using, for example, AI, or may be performed without using AI.

[0145] The comparison unit can improve the accuracy of the comparison by referring to literature related to the product when comparing prices. For example, the comparison unit provides detailed information about the product based on the literature. The comparison unit can also provide product evaluations based on the literature. The comparison unit can also provide product usage instructions based on the literature. This makes it possible to provide optimal price comparison results based on the literature related to the product. Some or all of the above-mentioned processing in the comparison unit may be performed using AI, for example, or may be performed without using AI.

[0146] The inventory confirmation unit can estimate the user's emotions and adjust the inventory confirmation criteria based on the estimated user emotions. For example, when the user is relaxed, the inventory confirmation unit can provide a wide range of inventory information. Furthermore, when the user is in a hurry, the inventory confirmation unit can prioritize providing the most relevant inventory information. Furthermore, when the user is excited, the inventory confirmation unit can prioritize providing visually stimulating inventory information. This makes it possible to provide optimal inventory confirmation criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0147] The inventory confirmation unit can improve the accuracy of inventory confirmation by taking into account the interrelationships between products. The inventory confirmation unit, for example, performs inventory confirmation taking into account related products. The inventory confirmation unit can also perform inventory confirmation taking into account similar products. The inventory confirmation unit can also perform inventory confirmation taking into account the interrelationships between products that the user has previously checked. This makes it possible to provide optimal inventory confirmation results based on the interrelationships between products. Some or all of the above-described processing in the inventory confirmation unit may be performed using, for example, AI, or may be performed without using AI.

[0148] When checking inventory, the inventory confirmation unit can perform the check taking into consideration the attribute information of the person who submitted the product. The inventory confirmation unit can perform the stock confirmation taking into consideration, for example, the reliability of the person who submitted the product. The inventory confirmation unit can also perform the stock confirmation taking into consideration the evaluation of the person who submitted the product. The inventory confirmation unit can also perform the stock confirmation taking into consideration the past transaction history of the person who submitted the product. This makes it possible to provide optimal inventory confirmation results based on the attribute information of the person who submitted the product. Some or all of the above-mentioned processing in the inventory confirmation unit can be performed using AI, for example, or can be performed without using AI.

[0149] The inventory confirmation unit can estimate the user's emotions and adjust the order in which inventory confirmation results are displayed based on the estimated user emotions. For example, when the user is relaxed, the inventory confirmation unit can provide a wide range of inventory information. Furthermore, when the user is in a hurry, the inventory confirmation unit can prioritize displaying the most relevant inventory information. Furthermore, when the user is excited, the inventory confirmation unit can prioritize displaying visually stimulating inventory information. This makes it possible to provide an optimal order of inventory confirmation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0150] The inventory confirmation unit can take into consideration the geographical distribution of products when checking inventory. For example, the inventory confirmation unit can prioritize displaying inventory information for products close to the user's current location. The inventory confirmation unit can also display inventory information for products in an area specified by the user. The inventory confirmation unit can also prioritize displaying inventory information for products with high geographical relevance. This makes it possible to provide optimal inventory confirmation results based on the geographical distribution of products. Some or all of the above-described processing in the inventory confirmation unit may be performed using AI, for example, or may be performed without using AI.

[0151] The inventory confirmation unit can improve the accuracy of the confirmation by referring to literature related to the product when checking the inventory. The inventory confirmation unit provides detailed information about the product based on the related literature, for example. The inventory confirmation unit can also provide product evaluations based on the related literature. The inventory confirmation unit can also provide product usage instructions based on the related literature. This makes it possible to provide optimal inventory confirmation results based on the literature related to the product. Some or all of the above-mentioned processing in the inventory confirmation unit may be performed, for example, using AI, or may be performed without using AI.

[0152] The differentiation unit can estimate the user's emotions and adjust differentiation criteria for similar products based on the estimated user emotions. For example, when the user is relaxed, the differentiation unit can provide a wide range of similar products. Furthermore, when the user is in a hurry, the differentiation unit can prioritize providing the most relevant similar products. Furthermore, when the user is excited, the differentiation unit can prioritize providing visually stimulating similar products. This makes it possible to provide optimal differentiation criteria for similar products according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0153] The distinction unit can improve the accuracy of distinction by taking into account the interrelationships between products when distinguishing between them. The distinction unit, for example, makes distinctions by taking into account related products. The distinction unit can also make distinctions by taking into account similar products. The distinction unit can also make distinctions by taking into account the interrelationships between products that the user has previously distinguished. This makes it possible to provide optimal distinction results based on the interrelationships between products. Some or all of the above-mentioned processing in the distinction unit may be performed using AI, for example, or may be performed without using AI.

[0154] The classification unit can perform classification taking into consideration attribute information of the submitter of the product. The classification unit performs classification taking into consideration, for example, the reliability of the submitter. The classification unit can also perform classification taking into consideration the evaluation of the submitter. The classification unit can also perform classification taking into consideration the submitter's past transaction history. This makes it possible to provide optimal classification results based on the attribute information of the submitter of the product. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI.

[0155] The classification unit can estimate the user's emotions and adjust the order in which classification results are displayed based on the estimated user's emotions. For example, when the user is relaxed, the classification unit provides a wide range of classification results. Furthermore, when the user is in a hurry, the classification unit can prioritize displaying the most relevant classification results. Furthermore, when the user is excited, the classification unit can prioritize displaying the most visually stimulating classification results. This makes it possible to provide an optimal order of classification results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0156] The differentiation unit can differentiate products taking into consideration the geographical distribution of the products. For example, the differentiation unit can prioritize displaying differentiation results for products close to the user's current location. The differentiation unit can also display differentiation results for products in an area specified by the user. The differentiation unit can also prioritize displaying differentiation results for products with high geographical relevance. This makes it possible to provide optimal differentiation results based on the geographical distribution of the products. Some or all of the above-described processing in the differentiation unit may be performed using AI, for example, or may be performed without using AI.

[0157] The distinction unit can improve the accuracy of the distinction by referring to literature related to the product when distinguishing. The distinction unit, for example, provides detailed information about the product based on the related literature. The distinction unit can also provide product evaluations based on the related literature. The distinction unit can also provide instructions for using the product based on the related literature. This makes it possible to provide optimal distinction results based on literature related to the product. Some or all of the above-mentioned processing in the distinction unit may be performed, for example, using AI, or may be performed without using AI.

[0158] The quality improvement unit can estimate the user's emotions and adjust the image quality improvement criteria based on the estimated user emotions. For example, if the user is relaxed, the quality improvement unit can provide a wide range of quality improvement options. If the user is in a hurry, the quality improvement unit can also prioritize providing the most relevant quality improvement options. If the user is excited, the quality improvement unit can also prioritize providing visually stimulating quality improvement options. This makes it possible to provide optimal image quality improvement criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0159] The quality improvement unit can improve the accuracy of quality improvement by taking into account the interrelationships between images when improving image quality. The quality improvement unit performs quality improvement by taking into account, for example, related images. The quality improvement unit can also perform quality improvement by taking into account similar images. The quality improvement unit can also perform quality improvement by taking into account the interrelationships between images for which the user has previously performed quality improvement. This makes it possible to provide optimal quality improvement results based on the interrelationships between images. Some or all of the above-mentioned processing in the quality improvement unit may be performed using, for example, AI, or may be performed without using AI.

[0160] When improving image quality, the quality improvement unit can improve the quality by taking into account attribute information of the image submitter. The quality improvement unit can improve the quality by taking into account, for example, the submitter's reliability. The quality improvement unit can also improve the quality by taking into account the submitter's reputation. The quality improvement unit can also improve the quality by taking into account the submitter's past transaction history. This makes it possible to provide optimal quality improvement results based on the attribute information of the image submitter. Some or all of the above-mentioned processing in the quality improvement unit may be performed, for example, using AI, or may be performed without using AI.

[0161] The quality improvement unit can estimate the user's emotions and adjust the order in which the image quality improvement results are displayed based on the estimated user's emotions. For example, when the user is relaxed, the quality improvement unit can provide a wide range of quality improvement results. Furthermore, when the user is in a hurry, the quality improvement unit can prioritize displaying the most relevant quality improvement results. Furthermore, when the user is excited, the quality improvement unit can prioritize displaying the most visually stimulating quality improvement results. This makes it possible to provide an optimal order of image quality improvement results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0162] The quality improvement unit can improve image quality by taking into account the geographic distribution of images. For example, the quality improvement unit can prioritize displaying quality improvement results for images close to the user's current location. The quality improvement unit can also display quality improvement results for images in a region specified by the user. The quality improvement unit can also prioritize displaying quality improvement results for images with high geographic relevance. This makes it possible to provide optimal quality improvement results based on the geographic distribution of images. Some or all of the above-described processing in the quality improvement unit may be performed using AI, for example, or may be performed without using AI.

[0163] The quality improvement unit can improve the accuracy of quality improvement by referring to related literature of the image when improving image quality. The quality improvement unit provides detailed information about the image based on the related literature, for example. The quality improvement unit can also provide an evaluation of the image based on the related literature. The quality improvement unit can also provide a method for using the image based on the related literature. This makes it possible to provide optimal quality improvement results based on the related literature of the image. Some or all of the above-mentioned processing in the quality improvement unit may be performed using AI, for example, or may be performed without using AI.

[0164] The protection unit can estimate the user's emotions and adjust the privacy protection standard based on the estimated user's emotions. For example, the protection unit can apply a standard privacy protection standard when the user is relaxed. The protection unit can also apply an enhanced privacy protection standard when the user is nervous. The protection unit can also apply a privacy protection standard that visually provides a sense of security when the user is excited. This makes it possible to provide an optimal privacy protection standard according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0165] The protection unit can improve the accuracy of privacy protection by taking into account the interrelationships between images. The protection unit performs privacy protection by taking into account, for example, related images. The protection unit can also perform privacy protection by taking into account similar images. The protection unit can also perform privacy protection by taking into account the interrelationships between images previously protected by the user. This makes it possible to provide optimal privacy protection results based on the interrelationships between images. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI.

[0166] The protection unit can perform privacy protection by taking into account attribute information of the image submitter. The protection unit can perform privacy protection by taking into account, for example, the trustworthiness of the submitter. The protection unit can also perform privacy protection by taking into account the submitter's reputation. The protection unit can also perform privacy protection by taking into account the submitter's past transaction history. This makes it possible to provide optimal privacy protection results based on the attribute information of the image submitter. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without using AI.

[0167] The protection unit can estimate the user's emotions and adjust the order in which privacy protection results are displayed based on the estimated user emotions. For example, if the user is relaxed, the protection unit can provide standard privacy protection results. If the user is nervous, the protection unit can also prioritize displaying enhanced privacy protection results. If the user is excited, the protection unit can also prioritize displaying privacy protection results that visually provide a sense of security. This makes it possible to provide an optimal order of privacy protection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0168] The protection unit can perform privacy protection by taking into account the geographical distribution of images. The protection unit performs privacy protection based on, for example, the user's current location. The protection unit can also perform privacy protection for images in a region specified by the user. The protection unit can also perform privacy protection for images with high geographical relevance. This makes it possible to provide optimal privacy protection results based on the geographical distribution of images. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI.

[0169] The protection unit can improve the accuracy of privacy protection by referring to related literature of the image during privacy protection. For example, the protection unit provides detailed information about the image based on the related literature. The protection unit can also provide an evaluation of the image based on the related literature. The protection unit can also provide a method for using the image based on the related literature. This makes it possible to provide optimal privacy protection results based on the related literature of the image. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, provision unit, and purchase unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive image input from a user using the camera 42 of the smart device 14 or the reception device 38. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generation AI to identify the product being worn. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches various e-commerce sites for the identified product. The provision unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the search results to the user. The purchase unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides a link for purchasing the product selected by the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search unit, provision unit, and purchase unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive image input from a user using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generation AI to identify the product being worn. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches various e-commerce sites for the identified product. The provision unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the search results to the user. The purchase unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides a link for purchasing the product selected by the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, provision unit, and purchase unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can receive image input from the user using the camera 42 or microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generation AI to identify the product being worn. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches various e-commerce sites for the identified product. The provision unit is realized by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the search results to the user. The purchase unit is realized by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides a link for purchasing the product selected by the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, provision unit, and purchase unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive image input from a user using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the image using a generation AI to identify the product being worn. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches various e-commerce sites for the identified product. The provision unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides the search results to the user. The purchase unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides a link for purchasing the product selected by the user.

[0170] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0171] The reception unit may also include a voice recognition unit that accepts voice input from the user. The voice recognition unit allows the user to describe a product aloud, thereby identifying the product without inputting an image. For example, if the user voice-inputs "red dress," the voice recognition unit analyzes the voice and searches for the corresponding product. The voice recognition unit can also estimate the user's emotion from the user's voice and provide search results according to the emotion. For example, if the user is excited, visually stimulating search results are displayed. This allows the user to identify a product using voice input and obtain search results according to their emotion.

[0172] The providing unit may also include a recommendation unit that analyzes the user's purchasing history and recommends optimal products based on past purchasing trends. The recommendation unit recommends highly relevant products based on products the user has previously purchased or viewed. For example, if the user has purchased a lot of sporting goods in the past, the recommendation unit will recommend new sporting goods. The recommendation unit can also estimate the user's emotions from the user's purchasing history and recommend products according to the emotions. For example, if the user has previously purchased relaxation goods when feeling stressed, the recommendation unit will recommend relaxation goods when it is estimated that the user is feeling stressed. This makes it possible to recommend optimal products based on the user's purchasing history.

[0173] The search unit may also include a location information search unit that uses the user's current location information to search for products available for purchase in nearby stores. The location information search unit searches for products that are in stock in nearby stores based on the user's current location. For example, if the user is looking for a specific piece of clothing, the location information search unit displays which nearby stores the clothing can be purchased at. The location information search unit can also estimate the user's emotions from the user's location information and provide search results according to the emotion. For example, if the user is in a hurry, information about the nearest store will be displayed preferentially. This allows the user to search for the most suitable product based on their current location.

[0174] The analysis unit may also include a preference learning unit that learns the user's preferences and identifies products based on the preferences. The preference learning unit learns the user's preferences based on the products the user has previously selected and their ratings. For example, it may learn the color and design trends of products the user has previously selected and identify similar products. The preference learning unit may also infer emotions from the user's preferences and identify products that correspond to the emotions. For example, if the user feels like relaxing, it may identify products that will help them relax. This makes it possible to identify the optimal products based on the user's preferences.

[0175] The providing unit may also include a social media analysis unit that analyzes the user's social media activity and recommends related products. The social media analysis unit recommends related products based on the posts and "likes" the user has shared on social media. For example, if the user has given many "likes" to posts from a particular brand, the unit recommends products from that brand. The social media analysis unit can also estimate the user's emotions from the user's social media activity and recommend products according to the emotions. For example, if it is estimated that the user is having fun, the unit recommends entertainment-related products. This makes it possible to recommend optimal products based on the user's social media activity.

[0176] The reception unit may also include a voice recognition unit that accepts voice input from the user. The voice recognition unit allows the user to describe a product aloud, thereby identifying the product without inputting an image. For example, if the user voice-inputs "red dress," the voice recognition unit analyzes the voice and searches for the corresponding product. The voice recognition unit can also estimate the user's emotion from the user's voice and provide search results according to the emotion. For example, if the user is excited, visually stimulating search results are displayed. This allows the user to identify a product using voice input and obtain search results according to their emotion.

[0177] The providing unit may also include a recommendation unit that analyzes the user's purchasing history and recommends optimal products based on past purchasing trends. The recommendation unit recommends highly relevant products based on products the user has previously purchased or viewed. For example, if the user has purchased a lot of sporting goods in the past, the recommendation unit will recommend new sporting goods. The recommendation unit can also estimate the user's emotions from the user's purchasing history and recommend products according to the emotions. For example, if the user has previously purchased relaxation goods when feeling stressed, the recommendation unit will recommend relaxation goods when it is estimated that the user is feeling stressed. This makes it possible to recommend optimal products based on the user's purchasing history.

[0178] The search unit may also include a location information search unit that uses the user's current location information to search for products available for purchase in nearby stores. The location information search unit searches for products that are in stock in nearby stores based on the user's current location. For example, if the user is looking for a specific piece of clothing, the location information search unit displays which nearby stores the clothing can be purchased at. The location information search unit can also estimate the user's emotions from the user's location information and provide search results according to the emotion. For example, if the user is in a hurry, information about the nearest store will be displayed preferentially. This allows the user to search for the most suitable product based on their current location.

[0179] The analysis unit may also include a preference learning unit that learns the user's preferences and identifies products based on the preferences. The preference learning unit learns the user's preferences based on the products the user has previously selected and their ratings. For example, it may learn the color and design trends of products the user has previously selected and identify similar products. The preference learning unit may also infer emotions from the user's preferences and identify products that correspond to the emotions. For example, if the user feels like relaxing, it may identify products that will help them relax. This makes it possible to identify the optimal products based on the user's preferences.

[0180] The providing unit may also include a social media analysis unit that analyzes the user's social media activity and recommends related products. The social media analysis unit recommends related products based on the posts and "likes" the user has shared on social media. For example, if the user has given many "likes" to posts from a particular brand, the unit recommends products from that brand. The social media analysis unit can also estimate the user's emotions from the user's social media activity and recommend products according to the emotions. For example, if it is estimated that the user is having fun, the unit recommends entertainment-related products. This makes it possible to recommend optimal products based on the user's social media activity.

[0181] The processing flow of the second embodiment will be briefly explained below.

[0182] Step 1: The reception unit accepts image input from the user. Image input from the user includes photos taken with a smartphone and images found on the Internet. For example, an image can be taken using a smartphone camera and entered into the system. Images found on the Internet can also be uploaded. Step 2: The analysis unit uses the generation AI to analyze the image received by the reception unit and identify the products worn. The analysis is performed based on image recognition algorithms and feature extraction methods. For example, the generation AI extracts features of the clothes, accessories, shoes, etc. in the image and identifies them. It can also identify the brand and design of the products in the image. For example, the generation AI analyzes logos and patterns in the image to identify the brand. Step 3: The search unit searches various e-commerce sites for the products identified by the analysis unit. The search is performed based on the type of search engine and the method of generating the search query. For example, based on the information about the identified product, the databases of various e-commerce sites are searched to find the corresponding product. It is also possible to use generation AI to generate a search query and search the databases of e-commerce sites. Step 4: The provision unit provides the search results obtained by the search unit to the user. The provision is based on the format of the user interface and the type of information to be provided. For example, search results from multiple e-commerce sites can be displayed as a list, allowing the user to select the most suitable product. In addition, the search results are displayed visually in an easy-to-understand manner using generation AI. Step 5: The purchasing unit provides a link for the user to purchase the product selected from the search results provided by the providing unit. The purchase is made based on the format of the link and the flow of the purchase procedure. For example, a link for purchasing the product selected by the user is provided, and the user can click on the link to directly access the purchase page. Generative AI can also be used to assist with the purchase procedure.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0213] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0219] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0230] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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."

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] [Explanation of symbols]

[0255] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image input from a user; an analysis unit that analyzes the image received by the reception unit and identifies the product being worn; a search unit that searches various e-commerce sites for the products identified by the analysis unit; a providing unit that provides a user with search results obtained by the searching unit; a purchasing unit that provides a link for the user to purchase the product selected from the search results provided by the providing unit. A system characterized by:

2. The providing unit Equipped with a comparison section for price comparison 2. The system of claim 1.

3. The search unit Equipped with an inventory check section to check stock status 2. The system of claim 1.

4. The analysis unit Equipped with a distinguishing section to distinguish similar products 2. The system of claim 1.

5. The reception unit Equipped with a quality improvement unit that improves image quality 2. The system of claim 1.

6. The reception unit Equipped with a privacy protection section 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of image input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past image input history and select the input method 2. The system of claim 1.

9. The reception unit When capturing an image, filtering is performed based on the user's current environment and situation.

2. The system of claim 1.

Images