System

Abstract

An object of a system according to an embodiment is to efficiently find a product desired by a user.SOLUTION: A system includes a reception unit, a collection unit, an organization unit, and a provision unit. The reception unit registers a product desired by a user. The collection unit collects related information from each shop site and the FURIMA site based on the product information registered by the reception unit. The organization unit organizes the information collected by the collection unit. The providing unit provides the user with the information organized by the organizing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to efficiently find the products they want, and search results are insufficient.

[0005] The system according to the embodiment aims to enable users to efficiently find products they want. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an organization unit, and a provision unit. The reception unit registers products that users want. The collection unit collects related information from various shopping sites and flea market sites based on the product information registered by the reception unit. The organization unit organizes the information collected by the collection unit. The provision unit provides the information organized by the organization unit to users. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently find products they want. [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 recommendation system according to an embodiment of the present invention automatically registers, collects, organizes, and provides products desired by users. In this recommendation system, users register desired products in a shopping list, and a generation AI selects, organizes, and presents information from various shopping sites and flea market sites. For example, if a user registers "red sneakers" or "latest smartphones" in the shopping list, the generation AI collects related information from each shopping site and flea market site based on these products. The generation AI takes into account differences in color notation and product names on each site to prevent search results from being null. The collected information is organized by the generation AI and presented to the user. This information includes, for example, price comparisons, stock availability, and seller ratings. This allows users to find products they truly desire. This allows the recommendation system to efficiently shop. For example, it can quickly and accurately process shopping lists written by users, improving user satisfaction. Furthermore, by taking into account differences in notation on each site, search results from being null and improving user satisfaction.

[0029] A recommendation system according to an embodiment includes a reception unit, a collection unit, an organization unit, and a providing unit. The reception unit registers products desired by a user. Products desired by a user include, but are not limited to, electronic devices, clothing, and food. The reception unit allows the user to input specific product names and characteristics, such as "red sneakers" or "latest smartphone." The collection unit collects related information from various shopping sites and flea market sites based on the product information registered by the reception unit. The collection unit collects related information from various shopping sites and flea market sites based on the registered product information, for example, using a generation AI. The collection unit takes into account differences in color descriptions and product names on each site to prevent search results from being negative. For example, the collection unit recognizes "red sneakers" and "red sneakers" as the same product and collects information. The organization unit organizes the information collected by the collection unit. The organization unit organizes the information in the form of, for example, price comparisons, stock availability, and seller ratings. For example, the organizing unit organizes the collected information by criteria such as price, rating, or stock status. The providing unit provides the information organized by the organizing unit to the user. The providing unit provides the information in, for example, list format or graph format. For example, the providing unit provides price comparisons, stock status, seller ratings, etc. to the user in list format or graph format. This allows the recommendation system according to the embodiment to enable users to shop efficiently. For example, the recommendation system can quickly and accurately process shopping lists written by users, improving user satisfaction. Furthermore, by taking into account differences in notation between sites, cases where searches do not return hits can be prevented, improving user satisfaction.

[0030] The collection unit includes a recognition unit that recognizes differences in how colors are written or differences in product names between sites. The recognition unit, for example, recognizes differences in how colors are written between sites. For example, the recognition unit recognizes different ways of expressing colors, such as RGB values, color codes, and color names. The recognition unit also recognizes differences in product names between sites. For example, the recognition unit recognizes different ways of expressing product names, such as abbreviations, aliases, and model numbers. This improves the accuracy of information collection by recognizing differences in how product names are written between sites. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input differences in how colors are written or product names between sites into a generation AI to improve the accuracy of recognition.

[0031] The organizing unit can organize the collected information in the form of price comparisons, stock status, and seller ratings. The organizing unit, for example, organizes the collected information in the form of price comparisons. For example, the organizing unit compares prices based on criteria such as lowest price, average price, and price range. The organizing unit can also organize the collected information in the form of stock status. For example, the organizing unit organizes stock status based on criteria such as in stock, out of stock, and expected arrival. The organizing unit can also organize the collected information in the form of seller ratings. For example, the organizing unit organizes seller ratings based on criteria such as star rating, number of reviews, and reliability. Organizing the collected information makes it easier for users to compare. Some or all of the above-described processing in the organizing unit may be performed using, or without, AI. For example, the organizing unit can input the collected information into a generation AI and organize it in the form of price comparisons, stock status, and seller ratings.

[0032] The providing unit can provide information in a format that is easy for the user to view. The providing unit provides information in a format that is easy for the user to view, for example. For example, the providing unit provides information based on criteria such as font size, layout, and color usage. The providing unit can also provide information in list format or graph format. For example, the providing unit provides price comparisons, stock status, seller ratings, etc. to the user in list format or graph format. This improves user convenience by providing information in a format that is easy for the user to view. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information into a generation AI and provide the information in a format that is easy for the user to view.

[0033] The providing unit can provide information in list format or graph format. The providing unit, for example, provides information in list format. For example, the providing unit provides information in bullet points, numbered lists, or the like. The providing unit can also provide information in graph format. For example, the providing unit provides information in bar graphs, line graphs, pie charts, or the like. By providing information in list format or graph format, the user can easily understand the information visually. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information to a generation AI and provide the information in list format or graph format.

[0034] The reception unit can analyze the user's past registration history and select the optimal registration method. The reception unit, for example, analyzes the user's past registration history. For example, the reception unit analyzes the user's past registration history based on criteria such as registration date and time, registration content, and frequency. The reception unit also selects the optimal registration method. For example, the reception unit prioritizes suggesting registration methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze the categories of products the user has previously registered and enable the user to easily register products in similar categories. The reception unit can also analyze the user's tendency to register during specific time periods based on the user's past registration history and encourage registration during those time periods. In this way, the optimal registration method can be suggested by analyzing the user's past registration history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past registration history into a generation AI to select the optimal registration method.

[0035] The reception unit may filter products based on the user's current purchasing intent and areas of interest when registering products. The reception unit, for example, evaluates the user's current purchasing intent. For example, the reception unit evaluates the user's purchasing intent based on criteria such as past purchase history, browsing history, and number of clicks. The reception unit may also identify the user's areas of interest. For example, the reception unit may identify the user's areas of interest based on criteria such as search history, social media activity, and survey results. The reception unit may also filter products based on the user's current purchasing intent and areas of interest when registering products. For example, the reception unit may suggest that the user prioritize registering products in categories in which the user is currently interested. If the user has a high purchasing intent, the reception unit may also suggest multiple related products and encourage the user to register them. The reception unit may also prioritize displaying products from specific brands or shops based on the user's areas of interest. This allows highly relevant products to be suggested by filtering based on the user's purchasing intent and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's purchasing intentions and areas of interest into the generation AI and have the generation AI perform filtering.

[0036] The reception unit can select the optimal registration means depending on the user's input method when registering a product. The reception unit, for example, identifies the user's input method. For example, the reception unit identifies the user's input method based on criteria such as voice input, text input, or image input. The reception unit also selects the optimal registration means depending on the user's input method. For example, if the user uses voice input, the reception unit automatically registers product information using voice recognition technology. If the user uses text input, the reception unit can analyze the entered text and automatically register product information. If the user uses image input, the reception unit can automatically register product information using image recognition technology. This improves the convenience of registration by selecting the optimal registration means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input data to a generation AI and have the generation AI execute the optimal registration means.

[0037] The reception unit can prioritize registering highly relevant products by taking into account the user's geographical location information when registering products. The reception unit, for example, identifies the user's geographical location information. For example, the reception unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, the reception unit prioritizes registering highly relevant products by taking into account the user's geographical location information when registering products. For example, if the user is in a specific area, the reception unit can prioritize registering products that can be purchased in that area. Furthermore, if the user is traveling, the reception unit can prioritize registering products that can be purchased at the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize registering products that can be purchased at a nearby store. This allows highly relevant products to be suggested by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to execute a process of preferentially registering highly relevant products.

[0038] The reception unit can analyze the user's social media activity and register related products when registering a product. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. The reception unit can also analyze the user's social media activity and register related products when registering a product. For example, the reception unit automatically registers products that the user has "liked" on social media. The reception unit can also analyze the content of the user's social media posts and suggest related products. The reception unit can also preferentially register products shared by the user's friends on social media. This makes it possible to suggest highly relevant products by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to execute a process of registering related products.

[0039] The reception unit can customize the registration method by reflecting the user's past feedback when registering a product. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The reception unit also customizes the registration method by reflecting the user's past feedback when registering a product. For example, the reception unit preferentially suggests registration methods that the user has previously preferred. The reception unit can also customize the registration interface based on the user's past feedback. The reception unit can also avoid registration methods that the user has previously been dissatisfied with. In this way, the registration method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback into a generation AI and cause the generation AI to execute processing to customize the registration method.

[0040] The collection unit can improve the accuracy of information collection by taking into account the update frequency of each site when collecting information. The collection unit, for example, identifies the update frequency of each site. For example, the collection unit identifies the update frequency of each site based on criteria such as daily updates, weekly updates, or real-time updates. The collection unit also improves the accuracy of information collection by taking into account the update frequency of each site when collecting information. For example, the collection unit prioritizes collecting information from sites with high update frequencies. The collection unit can also periodically check sites with low update frequencies to collect the latest information. The collection unit can also analyze the update frequency of each site and collect information at the optimal timing. In this way, the accuracy of information collection is improved by taking into account the update frequency of each site. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the update frequency of each site into the generation AI and cause the generation AI to execute processing to improve the accuracy of collection.

[0041] When collecting information, the collection unit can select a collection target by referring to the user's past purchase history. The collection unit, for example, refers to the user's past purchase history. For example, the collection unit refers to the user's past purchase history based on criteria such as purchase date and time, purchased products, and purchase frequency. Furthermore, the collection unit selects a collection target by referring to the user's past purchase history when collecting information. For example, the collection unit prioritizes collecting information related to products the user has previously purchased. Furthermore, the collection unit can collect product information that the user is likely to be interested in from the user's past purchase history. Furthermore, the collection unit can analyze the user's purchase history and select an optimal collection target. In this way, highly relevant information can be collected by referring to the user's past purchase history. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data on the user's past purchase history into a generation AI and cause the generation AI to execute a process of selecting a collection target.

[0042] The collection unit can determine collection priorities based on the user's current purchasing intent when collecting information. The collection unit, for example, evaluates the user's current purchasing intent. For example, the collection unit evaluates the user's purchasing intent based on criteria such as past purchase history, browsing history, and number of clicks. Furthermore, the collection unit determines collection priorities based on the user's current purchasing intent when collecting information. For example, the collection unit prioritizes collecting related information when the user's purchasing intent is high. Furthermore, the collection unit can refrain from collecting information when the user's purchasing intent is low. Furthermore, the collection unit can adjust the priority of collection targets according to the user's purchasing intent. In this way, highly relevant information can be collected by determining collection priorities based on the user's purchasing intent. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's purchasing intent into a generation AI and cause the generation AI to execute a process of determining collection priorities.

[0043] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, identifies the user's geographical location information. For example, the collection unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting product information available in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting product information available at the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting product information available at nearby stores. In this way, highly relevant information can be collected by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the user's geographical location information into a generation AI and cause the generation AI to execute a process of preferentially collecting highly relevant information.

[0044] The collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. Furthermore, the collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit prioritizes collecting product information that the user has "liked" on social media. Furthermore, the collection unit can analyze the content of the user's social media posts and collect related product information. Furthermore, the collection unit can prioritize collecting product information shared by the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's social media activity into a generation AI and cause the generation AI to execute a process of collecting related information.

[0045] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit, for example, analyzes the user's past feedback. For example, the collection unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The collection unit also customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially uses collection methods that the user has previously preferred. The collection unit can also customize the collection interface based on the user's past feedback. The collection unit can also avoid collection methods that the user has previously been dissatisfied with. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI and cause the generation AI to execute processing to customize the collection method.

[0046] The organizing unit can adjust the level of detail of the organizing based on the importance of the collected information when organizing information. The organizing unit, for example, evaluates the importance of the collected information. For example, the organizing unit evaluates the importance of the collected information based on criteria such as the user's interest, the newness of the information, and the reliability of the information. Furthermore, the organizing unit adjusts the level of detail of the organizing based on the importance of the collected information when organizing information. For example, the organizing unit prioritizes organizing information of high importance in detail. Furthermore, the organizing unit can also organize information of low importance in a concise manner. Furthermore, the organizing unit can adjust the level of detail of the organizing based on the importance of the information. In this way, by adjusting the level of detail of the organizing based on the importance of the collected information, the user can prioritize checking important information. Some or all of the above-described processing in the organizing unit may be performed, for example, using AI or may be performed without using AI. For example, the organizing unit can input data on the importance of the collected information to a generation AI and cause the generation AI to perform a process of adjusting the level of detail of the organizing.

[0047] The organizing unit can apply different organizing algorithms depending on the category of information when organizing information. The organizing unit, for example, classifies information categories. For example, the organizing unit classifies information categories based on criteria such as product category, information type, and user area of ​​interest. Furthermore, the organizing unit applies different organizing algorithms depending on the category of information when organizing information. For example, the organizing unit applies different organizing algorithms for each product category. Furthermore, the organizing unit can also apply different organizing algorithms depending on the type of information (price, stock status, rating, etc.). Furthermore, the organizing unit can select an optimal organizing algorithm based on the category of information. In this way, applying different organizing algorithms depending on the category of information improves the accuracy of information organization. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data on the category of information to a generation AI and cause the generation AI to execute processing to apply the optimal organizing algorithm.

[0048] The organizing unit can improve the accuracy of the information organization by referring to the user's past organization results when organizing information. The organizing unit, for example, refers to the user's past organization results. For example, the organizing unit refers to the user's past organization results based on criteria such as past organization patterns and user feedback. Furthermore, the organizing unit improves the accuracy of the information organization by referring to the user's past organization results when organizing information. For example, the organizing unit prioritizes the use of an organization method that the user has previously preferred. Furthermore, the organizing unit can optimize the organization algorithm based on the user's past organization results. Furthermore, the organizing unit can improve the accuracy of the organization by reflecting the user's past feedback. In this way, the accuracy of the organization is improved by referring to the user's past organization results. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data of the user's past organization results into the generation AI and cause the generation AI to execute processing to improve the accuracy of the organization.

[0049] The organizing unit can determine the priorities of organizing information based on the time of submission of the information when organizing the information. The organizing unit, for example, considers the time of submission of the information. For example, the organizing unit considers the time of submission of the information based on criteria such as the submission date and time, the frequency of submission, and the timing of submission. Furthermore, the organizing unit determines the priorities of organizing information based on the time of submission of the information when organizing the information. For example, the organizing unit prioritizes organizing the most recent information. Furthermore, the organizing unit can postpone information that was submitted earlier. Furthermore, the organizing unit can adjust the priorities of organizing based on the time of submission. In this way, by determining the priorities of organizing based on the time of submission of the information, the most recent information can be checked preferentially. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data on the time of submission of the information to the generation AI and cause the generation AI to execute a process of determining the priorities of organizing.

[0050] The organizing unit can adjust the order of organizing information based on the relevance of the information when organizing the information. The organizing unit, for example, evaluates the relevance of the information. For example, the organizing unit evaluates the relevance of the information based on criteria such as common keywords, related topics, and the user's level of interest. Furthermore, the organizing unit adjusts the order of organizing information based on the relevance of the information when organizing the information. For example, the organizing unit prioritizes organizing highly relevant information. Furthermore, the organizing unit can postpone less relevant information. Furthermore, the organizing unit can adjust the order of organizing information based on the relevance of the information. In this way, by adjusting the order of organizing based on the relevance of the information, highly relevant information can be prioritized for review. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data on the relevance of the information to the generation AI and cause the generation AI to execute processing to adjust the order of organizing.

[0051] The organizing unit can adjust the use of technical terms in organizing information according to the user's level of expertise. The organizing unit, for example, evaluates the user's level of expertise. For example, the organizing unit evaluates the user's level of expertise based on criteria such as survey results, past behavioral history, and the user's profile information. Furthermore, the organizing unit can adjust the use of technical terms in organizing information according to the user's level of expertise. For example, if the user has technical expertise, the organizing unit organizes the information using a lot of technical terms. Furthermore, if the user does not have technical expertise, the organizing unit can organize the information using simpler terms. Furthermore, the organizing unit can adjust the use of technical terms in organizing according to the user's level of expertise. This makes it easier to understand the information by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without AI. For example, the organizing unit can input data on the user's level of expertise into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terms.

[0052] The providing unit can select the optimal delivery method by referring to the user's past purchase history when providing information. The providing unit, for example, refers to the user's past purchase history. For example, the providing unit refers to the user's past purchase history based on criteria such as purchase date and time, purchased items, and purchase frequency. The providing unit also selects the optimal delivery method by referring to the user's past purchase history when providing information. For example, the providing unit prioritizes the use of a delivery method that the user has previously preferred. The providing unit can also select the optimal delivery method based on the user's past purchase history. The providing unit can also optimize the delivery method by reflecting the user's past feedback. In this way, the optimal delivery method can be selected by referring to the user's past purchase history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's past purchase history into the generation AI and cause the generation AI to execute a process of selecting the optimal delivery method.

[0053] The providing unit can determine the priority of information provision based on the user's current purchasing willingness when providing information. The providing unit, for example, evaluates the user's current purchasing willingness. For example, the providing unit evaluates the user's purchasing willingness based on criteria such as past purchase history, browsing history, and number of clicks. Furthermore, the providing unit determines the priority of information provision based on the user's current purchasing willingness when providing information. For example, the providing unit prioritizes providing related information when the user's purchasing willingness is high. Furthermore, the providing unit can refrain from providing information when the user's purchasing willingness is low. Furthermore, the providing unit can adjust the priority of information to be provided according to the user's purchasing willingness. In this way, by determining the priority of information provision based on the user's purchasing willingness, highly relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's purchasing willingness to a generation AI and cause the generation AI to execute a process of determining the priority of information provision.

[0054] The providing unit can improve the provision method by reflecting user feedback when providing information. The providing unit, for example, analyzes user feedback. For example, the providing unit analyzes user feedback based on criteria such as reviews, ratings, and comments. Furthermore, the providing unit improves the provision method by reflecting user feedback when providing information. For example, the providing unit customizes the provision interface based on user feedback. Furthermore, the providing unit can optimize the provision method by reflecting user feedback in the past. Furthermore, the providing unit can avoid provision methods that the user is dissatisfied with. In this way, the provision method can be optimized by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to execute processing to improve the provision method.

[0055] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing information. The providing unit, for example, identifies the user's geographical location information. For example, the providing unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, the providing unit can prioritize providing product information available in that area. Furthermore, if the user is traveling, the providing unit can prioritize providing product information available at the user's travel destination. Furthermore, if the user is at home, the providing unit can prioritize providing product information available at nearby stores. In this way, by taking the user's geographical location information into account, highly relevant information can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into a generating AI and cause the generating AI to execute a process of selecting the optimal providing method.

[0056] The providing unit can customize the information providing method by analyzing the user's social media activity when providing information. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. Furthermore, the providing unit customizes the information providing method when providing information. For example, the providing unit prioritizes providing product information that the user has "liked" on social media. Furthermore, the providing unit can analyze the content of the user's social media posts and provide related product information. Furthermore, the providing unit can prioritize providing product information shared by the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant information can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to execute processing to customize the information providing method.

[0057] The providing unit can customize the delivery method by reflecting the user's past feedback when providing information. The providing unit, for example, analyzes the user's past feedback. For example, the providing unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The providing unit also customizes the delivery method by reflecting the user's past feedback when providing information. For example, the providing unit prioritizes the use of a delivery method that the user has previously preferred. The providing unit can also customize the delivery interface based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the delivery method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the user's past feedback into the generation AI and cause the generation AI to execute processing to customize the delivery method.

[0058] The recognition unit can optimize the recognition algorithm by taking into account differences in spelling between sites during recognition. The recognition unit, for example, takes into account differences in spelling between sites. For example, the recognition unit considers differences in spelling between sites based on criteria such as differences in format, terminology, and spelling style. The recognition unit also optimizes the recognition algorithm by taking into account differences in spelling between sites during recognition. For example, the recognition unit optimizes the recognition algorithm by taking into account differences in color writing between sites. The recognition unit can also optimize the recognition algorithm by taking into account differences in product names between sites. The recognition unit can also analyze differences in spelling between sites and select the optimal recognition algorithm. This improves recognition accuracy by taking into account differences in spelling between sites. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on differences in spelling between sites into the generation AI and cause the generation AI to execute processing to optimize the recognition algorithm.

[0059] The recognition unit can improve the accuracy of recognition by referring to the user's past purchase history during recognition. The recognition unit, for example, refers to the user's past purchase history. For example, the recognition unit refers to the user's past purchase history based on criteria such as purchase date and time, purchased items, and purchase frequency. The recognition unit also improves the accuracy of recognition by referring to the user's past purchase history during recognition. For example, the recognition unit prioritizes recognizing information related to items the user has previously purchased. The recognition unit can also prioritize recognizing product information that is likely to be of interest to the user based on the user's past purchase history. The recognition unit can also analyze the user's purchase history and select the optimal recognition target. This improves the accuracy of recognition by referring to the user's past purchase history. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the user's past purchase history into the generation AI and cause the generation AI to execute processing to improve the accuracy of recognition.

[0060] The recognition unit can determine the recognition priority based on the user's current purchasing intent during recognition. The recognition unit, for example, evaluates the user's current purchasing intent. For example, the recognition unit evaluates the user's purchasing intent based on criteria such as past purchase history, browsing history, and number of clicks. The recognition unit also determines the recognition priority based on the user's current purchasing intent during recognition. For example, the recognition unit prioritizes recognizing related information when the user's purchasing intent is high. The recognition unit can also refrain from recognition when the user's purchasing intent is low. The recognition unit can also adjust the priority of recognition targets according to the user's purchasing intent. In this way, highly relevant information can be recognized by determining the recognition priority based on the user's purchasing intent. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the user's purchasing intent to the generation AI and cause the generation AI to execute a process of determining the recognition priority.

[0061] The recognition unit can improve the accuracy of recognition by taking into account the user's geographical location information during recognition. The recognition unit, for example, identifies the user's geographical location information. For example, the recognition unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, the recognition unit improves the accuracy of recognition by taking into account the user's geographical location information during recognition. For example, when the user is in a specific area, the recognition unit may prioritize recognizing product information available in that area. Furthermore, when the user is traveling, the recognition unit may prioritize recognizing product information available at the user's travel destination. Furthermore, when the user is at home, the recognition unit may prioritize recognizing product information available at nearby stores. In this way, by taking the user's geographical location information into account, highly relevant information can be recognized. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit may input the user's geographical location information data into the generation AI and cause the generation AI to execute processing to improve the accuracy of recognition.

[0062] The recognition unit can improve the accuracy of recognition by analyzing the user's social media activity during recognition. The recognition unit, for example, analyzes the user's social media activity. For example, the recognition unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. The recognition unit can also improve the accuracy of recognition by analyzing the user's social media activity during recognition. For example, the recognition unit prioritizes recognizing product information that the user has "liked" on social media. The recognition unit can also analyze the content of the user's social media posts to recognize related product information. The recognition unit can also prioritize recognizing product information shared by the user's friends on social media. In this way, highly relevant information can be recognized by analyzing the user's social media activity. Some or all of the above-described processing in the recognition unit may be performed using, or without, AI. For example, the recognition unit can input data on the user's social media activity into the generation AI and cause the generation AI to perform processing to improve the accuracy of recognition.

[0063] The recognition unit can customize the recognition algorithm by reflecting the user's past feedback during recognition. The recognition unit, for example, analyzes the user's past feedback. For example, the recognition unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The recognition unit also customizes the recognition algorithm by reflecting the user's past feedback during recognition. For example, the recognition unit preferentially uses a recognition method that the user previously preferred. The recognition unit can also optimize the recognition algorithm based on the user's past feedback. The recognition unit can also avoid a recognition method that the user previously dissatisfied with. In this way, the recognition algorithm can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input data of the user's past feedback to the generation AI and cause the generation AI to perform a process of customizing the recognition algorithm.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's purchasing history and automatically suggest new products related to products the user has previously purchased. For example, it can suggest accessories or upgraded versions of electronic devices the user has previously purchased. It can also suggest new collections of clothing and related fashion items the user has previously purchased. It can also suggest new recipes and related ingredients for food the user has previously purchased. This can improve the user's purchasing experience by suggesting highly relevant products based on the user's purchasing history.

[0066] The collection unit can analyze the user's social media activity and collect related information based on the products the user has "liked" and posts the user has shared. For example, the collection unit can collect the latest information and discount information on products the user has "liked" on social media. It can also collect product information related to posts the user has shared. It can also preferentially collect product information shared by the user's friends. This allows the collection of highly relevant information based on the user's social media activity to provide information that matches the user's interests.

[0067] The organizing unit can organize the collected information according to the user's level of expertise. For example, if the user has specialized knowledge, it can provide an organization method that includes detailed technical information and technical terminology. Alternatively, if the user does not have specialized knowledge, it can provide an organization method that uses simple words and illustrations. Furthermore, it can adjust the display format of the information according to the user's level of expertise. This makes it easier to understand the information by organizing it according to the user's level of expertise.

[0068] The providing unit can provide product information available at nearby stores with priority, taking into account the user's geographical location information. For example, when the user is in a specific area, product information available at that area can be provided in list form. Also, when the user is traveling, product information available at the user's travel destination can be provided in map form. Furthermore, when the user is at home, product information available at nearby stores can be notified. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0069] The providing unit can customize the information providing method by reflecting the user's past feedback. For example, the providing unit can give priority to using a providing method (list format, graph format, etc.) that the user has previously preferred. The providing interface can also be customized based on the user's past feedback. Furthermore, it can also avoid providing methods that the user has previously been dissatisfied with. In this way, the information providing method can be optimized by reflecting the user's past feedback.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit registers the product the user wants. The user can enter a specific product name and characteristics, such as "red sneakers" or "latest smartphone." Step 2: The collection unit collects related information from various shopping sites and flea market sites based on the product information registered by the reception unit. The collection unit, for example, uses generation AI to take into account differences in notation and prevent cases where the search does not return any hits. Step 3: The organizer organizes the information collected by the collector, for example, in the form of price comparisons, stock availability, vendor ratings, and the like. Step 4: The providing unit provides the information organized by the organizing unit to the user. The providing unit provides the information in, for example, a list format or a graph format.

[0072] (Example 2) A recommendation system according to an embodiment of the present invention automatically registers, collects, organizes, and provides products desired by users. In this recommendation system, users register desired products in a shopping list, and a generation AI selects, organizes, and presents information from various shopping sites and flea market sites. For example, if a user registers "red sneakers" or "latest smartphones" in the shopping list, the generation AI collects related information from each shopping site and flea market site based on these products. The generation AI takes into account differences in color notation and product names on each site to prevent search results from being null. The collected information is organized by the generation AI and presented to the user. This information includes, for example, price comparisons, stock availability, and seller ratings. This allows users to find products they truly desire. This allows the recommendation system to efficiently shop. For example, it can quickly and accurately process shopping lists written by users, improving user satisfaction. Furthermore, by taking into account differences in notation on each site, search results from being null and improving user satisfaction.

[0073] A recommendation system according to an embodiment includes a reception unit, a collection unit, an organization unit, and a providing unit. The reception unit registers products desired by a user. Products desired by a user include, but are not limited to, electronic devices, clothing, and food. The reception unit allows the user to input specific product names and characteristics, such as "red sneakers" or "latest smartphone." The collection unit collects related information from various shopping sites and flea market sites based on the product information registered by the reception unit. The collection unit collects related information from various shopping sites and flea market sites based on the registered product information, for example, using a generation AI. The collection unit takes into account differences in color descriptions and product names on each site to prevent search results from being negative. For example, the collection unit recognizes "red sneakers" and "red sneakers" as the same product and collects information. The organization unit organizes the information collected by the collection unit. The organization unit organizes the information in the form of, for example, price comparisons, stock availability, and seller ratings. For example, the organizing unit organizes the collected information by criteria such as price, rating, or stock status. The providing unit provides the information organized by the organizing unit to the user. The providing unit provides the information in, for example, list format or graph format. For example, the providing unit provides price comparisons, stock status, seller ratings, etc. to the user in list format or graph format. This allows the recommendation system according to the embodiment to enable users to shop efficiently. For example, the recommendation system can quickly and accurately process shopping lists written by users, improving user satisfaction. Furthermore, by taking into account differences in notation between sites, cases where searches do not return hits can be prevented, improving user satisfaction.

[0074] The collection unit includes a recognition unit that recognizes differences in how colors are written or differences in product names between sites. The recognition unit, for example, recognizes differences in how colors are written between sites. For example, the recognition unit recognizes different ways of expressing colors, such as RGB values, color codes, and color names. The recognition unit also recognizes differences in product names between sites. For example, the recognition unit recognizes different ways of expressing product names, such as abbreviations, aliases, and model numbers. This improves the accuracy of information collection by recognizing differences in how product names are written between sites. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input differences in how colors are written or product names between sites into a generation AI to improve the accuracy of recognition.

[0075] The organizing unit can organize the collected information in the form of price comparisons, stock status, and seller ratings. The organizing unit, for example, organizes the collected information in the form of price comparisons. For example, the organizing unit compares prices based on criteria such as lowest price, average price, and price range. The organizing unit can also organize the collected information in the form of stock status. For example, the organizing unit organizes stock status based on criteria such as in stock, out of stock, and expected arrival. The organizing unit can also organize the collected information in the form of seller ratings. For example, the organizing unit organizes seller ratings based on criteria such as star rating, number of reviews, and reliability. Organizing the collected information makes it easier for users to compare. Some or all of the above-described processing in the organizing unit may be performed using, or without, AI. For example, the organizing unit can input the collected information into a generation AI and organize it in the form of price comparisons, stock status, and seller ratings.

[0076] The providing unit can provide information in a format that is easy for the user to view. The providing unit provides information in a format that is easy for the user to view, for example. For example, the providing unit provides information based on criteria such as font size, layout, and color usage. The providing unit can also provide information in list format or graph format. For example, the providing unit provides price comparisons, stock status, seller ratings, etc. to the user in list format or graph format. This improves user convenience by providing information in a format that is easy for the user to view. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information into a generation AI and provide the information in a format that is easy for the user to view.

[0077] The providing unit can provide information in list format or graph format. The providing unit, for example, provides information in list format. For example, the providing unit provides information in bullet points, numbered lists, or the like. The providing unit can also provide information in graph format. For example, the providing unit provides information in bar graphs, line graphs, pie charts, or the like. By providing information in list format or graph format, the user can easily understand the information visually. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input information to a generation AI and provide the information in list format or graph format.

[0078] The reception unit can estimate the user's emotions and adjust the timing of product registration based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The reception unit also adjusts the timing of product registration based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit may send a notification encouraging the user to register the product during a time when the user is able to relax. If the user is excited, the reception unit may also send a notification encouraging the user to register the product immediately. If the user is tired, the reception unit may also suggest registering the product the next day. This reduces the user's stress by adjusting the timing of product registration according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The reception unit can analyze the user's past registration history and select the optimal registration method. The reception unit, for example, analyzes the user's past registration history. For example, the reception unit analyzes the user's past registration history based on criteria such as registration date and time, registration content, and frequency. The reception unit also selects the optimal registration method. For example, the reception unit prioritizes suggesting registration methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze the categories of products the user has previously registered and enable the user to easily register products in similar categories. The reception unit can also analyze the user's tendency to register during specific time periods based on the user's past registration history and encourage registration during those time periods. In this way, the optimal registration method can be suggested by analyzing the user's past registration history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past registration history into a generation AI to select the optimal registration method.

[0080] The reception unit may filter products based on the user's current purchasing intent and areas of interest when registering products. The reception unit, for example, evaluates the user's current purchasing intent. For example, the reception unit evaluates the user's purchasing intent based on criteria such as past purchase history, browsing history, and number of clicks. The reception unit may also identify the user's areas of interest. For example, the reception unit may identify the user's areas of interest based on criteria such as search history, social media activity, and survey results. The reception unit may also filter products based on the user's current purchasing intent and areas of interest when registering products. For example, the reception unit may suggest that the user prioritize registering products in categories in which the user is currently interested. If the user has a high purchasing intent, the reception unit may also suggest multiple related products and encourage the user to register them. The reception unit may also prioritize displaying products from specific brands or shops based on the user's areas of interest. This allows highly relevant products to be suggested by filtering based on the user's purchasing intent and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's purchasing intentions and areas of interest into the generation AI and have the generation AI perform filtering.

[0081] The reception unit can select the optimal registration means depending on the user's input method when registering a product. The reception unit, for example, identifies the user's input method. For example, the reception unit identifies the user's input method based on criteria such as voice input, text input, or image input. The reception unit also selects the optimal registration means depending on the user's input method. For example, if the user uses voice input, the reception unit automatically registers product information using voice recognition technology. If the user uses text input, the reception unit can analyze the entered text and automatically register product information. If the user uses image input, the reception unit can automatically register product information using image recognition technology. This improves the convenience of registration by selecting the optimal registration means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input data to a generation AI and have the generation AI execute the optimal registration means.

[0082] The reception unit can estimate the user's emotions and determine the priority of products to be registered based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The reception unit also determines the priority of products to be registered based on the estimated user emotions. For example, if the user is excited, the reception unit may preferentially register the latest products or popular products. If the user is relaxed, the reception unit may also preferentially register products that match the user's preferences. If the user is stressed, the reception unit may also preferentially register products that have a relaxing effect. This improves user satisfaction by determining the priority of products based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input user emotion data into the generation AI and cause the generation AI to execute a process for determining the priority of products.

[0083] The reception unit can prioritize registering highly relevant products by taking into account the user's geographical location information when registering products. The reception unit, for example, identifies the user's geographical location information. For example, the reception unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, the reception unit prioritizes registering highly relevant products by taking into account the user's geographical location information when registering products. For example, if the user is in a specific area, the reception unit can prioritize registering products that can be purchased in that area. Furthermore, if the user is traveling, the reception unit can prioritize registering products that can be purchased at the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize registering products that can be purchased at a nearby store. This allows highly relevant products to be suggested by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to execute a process of preferentially registering highly relevant products.

[0084] The reception unit can analyze the user's social media activity and register related products when registering a product. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. The reception unit can also analyze the user's social media activity and register related products when registering a product. For example, the reception unit automatically registers products that the user has "liked" on social media. The reception unit can also analyze the content of the user's social media posts and suggest related products. The reception unit can also preferentially register products shared by the user's friends on social media. This makes it possible to suggest highly relevant products by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to execute a process of registering related products.

[0085] The reception unit can customize the registration method by reflecting the user's past feedback when registering a product. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The reception unit also customizes the registration method by reflecting the user's past feedback when registering a product. For example, the reception unit preferentially suggests registration methods that the user has previously preferred. The reception unit can also customize the registration interface based on the user's past feedback. The reception unit can also avoid registration methods that the user has previously been dissatisfied with. In this way, the registration method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's past feedback into a generation AI and cause the generation AI to execute processing to customize the registration method.

[0086] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The collection unit also adjusts the timing of information collection based on the estimated user emotions. For example, the collection unit immediately starts information collection when the user is relaxed. Furthermore, if the user is feeling stressed, the collection unit can collect information during a time when the user is able to relax. Furthermore, the collection unit can immediately start information collection when the user is excited. In this way, the timing of information collection is adjusted according to the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to execute a process to adjust the timing of information collection.

[0087] The collection unit can improve the accuracy of information collection by taking into account the update frequency of each site when collecting information. The collection unit, for example, identifies the update frequency of each site. For example, the collection unit identifies the update frequency of each site based on criteria such as daily updates, weekly updates, or real-time updates. The collection unit also improves the accuracy of information collection by taking into account the update frequency of each site when collecting information. For example, the collection unit prioritizes collecting information from sites with high update frequencies. The collection unit can also periodically check sites with low update frequencies to collect the latest information. The collection unit can also analyze the update frequency of each site and collect information at the optimal timing. In this way, the accuracy of information collection is improved by taking into account the update frequency of each site. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the update frequency of each site into the generation AI and cause the generation AI to execute processing to improve the accuracy of collection.

[0088] When collecting information, the collection unit can select a collection target by referring to the user's past purchase history. The collection unit, for example, refers to the user's past purchase history. For example, the collection unit refers to the user's past purchase history based on criteria such as purchase date and time, purchased products, and purchase frequency. Furthermore, the collection unit selects a collection target by referring to the user's past purchase history when collecting information. For example, the collection unit prioritizes collecting information related to products the user has previously purchased. Furthermore, the collection unit can collect product information that the user is likely to be interested in from the user's past purchase history. Furthermore, the collection unit can analyze the user's purchase history and select an optimal collection target. In this way, highly relevant information can be collected by referring to the user's past purchase history. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data on the user's past purchase history into a generation AI and cause the generation AI to execute a process of selecting a collection target.

[0089] The collection unit can determine collection priorities based on the user's current purchasing intent when collecting information. The collection unit, for example, evaluates the user's current purchasing intent. For example, the collection unit evaluates the user's purchasing intent based on criteria such as past purchase history, browsing history, and number of clicks. Furthermore, the collection unit determines collection priorities based on the user's current purchasing intent when collecting information. For example, the collection unit prioritizes collecting related information when the user's purchasing intent is high. Furthermore, the collection unit can refrain from collecting information when the user's purchasing intent is low. Furthermore, the collection unit can adjust the priority of collection targets according to the user's purchasing intent. In this way, highly relevant information can be collected by determining collection priorities based on the user's purchasing intent. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's purchasing intent into a generation AI and cause the generation AI to execute a process of determining collection priorities.

[0090] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The collection unit also determines the priority of information to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting the latest product information. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting information that matches the user's preferences. Furthermore, if the user is stressed, the collection unit can also prioritize collecting product information that has a relaxing effect. This improves user satisfaction by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to perform a process of determining the priority of information.

[0091] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, identifies the user's geographical location information. For example, the collection unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting product information available in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting product information available at the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting product information available at nearby stores. In this way, highly relevant information can be collected by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the user's geographical location information into a generation AI and cause the generation AI to execute a process of preferentially collecting highly relevant information.

[0092] The collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit, for example, analyzes the user's social media activity. For example, the collection unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. Furthermore, the collection unit can analyze the user's social media activity and collect related information when collecting information. For example, the collection unit prioritizes collecting product information that the user has "liked" on social media. Furthermore, the collection unit can analyze the content of the user's social media posts and collect related product information. Furthermore, the collection unit can prioritize collecting product information shared by the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's social media activity into a generation AI and cause the generation AI to execute a process of collecting related information.

[0093] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit, for example, analyzes the user's past feedback. For example, the collection unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The collection unit also customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially uses collection methods that the user has previously preferred. The collection unit can also customize the collection interface based on the user's past feedback. The collection unit can also avoid collection methods that the user has previously been dissatisfied with. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into the generation AI and cause the generation AI to execute processing to customize the collection method.

[0094] The organizing unit can estimate the user's emotions and adjust the information organization method based on the estimated user's emotions. The organizing unit, for example, estimates the user's emotions. For example, the organizing unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The organizing unit also adjusts the information organization method based on the estimated user's emotions. For example, the organizing unit provides an organization method that includes detailed information when the user is relaxed. The organizing unit can also provide a simple and highly visible organization method when the user is stressed. The organizing unit can also provide a visually stimulating organization method when the user is excited. This reduces the user's stress by adjusting the information organization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the organizing unit can input the user's emotional data into the generation AI and cause the generation AI to execute a process to adjust the method of organizing information.

[0095] The organizing unit can adjust the level of detail of the organizing based on the importance of the collected information when organizing information. The organizing unit, for example, evaluates the importance of the collected information. For example, the organizing unit evaluates the importance of the collected information based on criteria such as the user's interest, the newness of the information, and the reliability of the information. Furthermore, the organizing unit adjusts the level of detail of the organizing based on the importance of the collected information when organizing information. For example, the organizing unit prioritizes organizing information of high importance in detail. Furthermore, the organizing unit can also organize information of low importance in a concise manner. Furthermore, the organizing unit can adjust the level of detail of the organizing based on the importance of the information. In this way, by adjusting the level of detail of the organizing based on the importance of the collected information, the user can prioritize checking important information. Some or all of the above-described processing in the organizing unit may be performed, for example, using AI or may be performed without using AI. For example, the organizing unit can input data on the importance of the collected information to a generation AI and cause the generation AI to perform a process of adjusting the level of detail of the organizing.

[0096] The organizing unit can apply different organizing algorithms depending on the category of information when organizing information. The organizing unit, for example, classifies information categories. For example, the organizing unit classifies information categories based on criteria such as product category, information type, and user area of ​​interest. Furthermore, the organizing unit applies different organizing algorithms depending on the category of information when organizing information. For example, the organizing unit applies different organizing algorithms for each product category. Furthermore, the organizing unit can also apply different organizing algorithms depending on the type of information (price, stock status, rating, etc.). Furthermore, the organizing unit can select an optimal organizing algorithm based on the category of information. In this way, applying different organizing algorithms depending on the category of information improves the accuracy of information organization. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data on the category of information to a generation AI and cause the generation AI to execute processing to apply the optimal organizing algorithm.

[0097] The organizing unit can improve the accuracy of the information organization by referring to the user's past organization results when organizing information. The organizing unit, for example, refers to the user's past organization results. For example, the organizing unit refers to the user's past organization results based on criteria such as past organization patterns and user feedback. Furthermore, the organizing unit improves the accuracy of the information organization by referring to the user's past organization results when organizing information. For example, the organizing unit prioritizes the use of an organization method that the user has previously preferred. Furthermore, the organizing unit can optimize the organization algorithm based on the user's past organization results. Furthermore, the organizing unit can improve the accuracy of the organization by reflecting the user's past feedback. In this way, the accuracy of the organization is improved by referring to the user's past organization results. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data of the user's past organization results into the generation AI and cause the generation AI to execute processing to improve the accuracy of the organization.

[0098] The organizing unit can estimate the user's emotions and determine the priority of information to be organized based on the estimated user's emotions. The organizing unit, for example, estimates the user's emotions. For example, the organizing unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The organizing unit also determines the priority of information to be organized based on the estimated user's emotions. For example, if the user is excited, the organizing unit prioritizes organizing the latest information. Furthermore, if the user is relaxed, the organizing unit can prioritize organizing information that matches the user's preferences. Furthermore, if the user is stressed, the organizing unit can prioritize organizing information that has a relaxing effect. This improves user satisfaction by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the organizing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the organizing unit can input the user's emotional data into the generation AI and cause the generation AI to execute a process of determining the priority of information.

[0099] The organizing unit can determine the priorities of organizing information based on the time of submission of the information when organizing the information. The organizing unit, for example, considers the time of submission of the information. For example, the organizing unit considers the time of submission of the information based on criteria such as the submission date and time, the frequency of submission, and the timing of submission. Furthermore, the organizing unit determines the priorities of organizing information based on the time of submission of the information when organizing the information. For example, the organizing unit prioritizes organizing the most recent information. Furthermore, the organizing unit can postpone information that was submitted earlier. Furthermore, the organizing unit can adjust the priorities of organizing based on the time of submission. In this way, by determining the priorities of organizing based on the time of submission of the information, the most recent information can be checked preferentially. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data on the time of submission of the information to the generation AI and cause the generation AI to execute a process of determining the priorities of organizing.

[0100] The organizing unit can adjust the order of organizing information based on the relevance of the information when organizing the information. The organizing unit, for example, evaluates the relevance of the information. For example, the organizing unit evaluates the relevance of the information based on criteria such as common keywords, related topics, and the user's level of interest. Furthermore, the organizing unit adjusts the order of organizing information based on the relevance of the information when organizing the information. For example, the organizing unit prioritizes organizing highly relevant information. Furthermore, the organizing unit can postpone less relevant information. Furthermore, the organizing unit can adjust the order of organizing information based on the relevance of the information. In this way, by adjusting the order of organizing based on the relevance of the information, highly relevant information can be prioritized for review. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the organizing unit can input data on the relevance of the information to the generation AI and cause the generation AI to execute processing to adjust the order of organizing.

[0101] The organizing unit can adjust the use of technical terms in organizing information according to the user's level of expertise. The organizing unit, for example, evaluates the user's level of expertise. For example, the organizing unit evaluates the user's level of expertise based on criteria such as survey results, past behavioral history, and the user's profile information. Furthermore, the organizing unit can adjust the use of technical terms in organizing information according to the user's level of expertise. For example, if the user has technical expertise, the organizing unit organizes the information using a lot of technical terms. Furthermore, if the user does not have technical expertise, the organizing unit can organize the information using simpler terms. Furthermore, the organizing unit can adjust the use of technical terms in organizing according to the user's level of expertise. This makes it easier to understand the information by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the organizing unit may be performed using, for example, AI, or may be performed without AI. For example, the organizing unit can input data on the user's level of expertise into the generation AI and cause the generation AI to perform a process of adjusting the use of technical terms.

[0102] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. The providing unit also adjusts the method of providing information based on the estimated user's emotion. For example, if the user is relaxed, the providing unit selects a method of providing information that includes detailed information. If the user is stressed, the providing unit can select a simple and highly visible method of providing information. If the user is excited, the providing unit can select a visually stimulating method of providing information. In this way, the method of providing information is adjusted according to the user's emotion, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to execute a process to adjust the method of providing information.

[0103] The providing unit can select the optimal delivery method by referring to the user's past purchase history when providing information. The providing unit, for example, refers to the user's past purchase history. For example, the providing unit refers to the user's past purchase history based on criteria such as purchase date and time, purchased items, and purchase frequency. The providing unit also selects the optimal delivery method by referring to the user's past purchase history when providing information. For example, the providing unit prioritizes the use of a delivery method that the user has previously preferred. The providing unit can also select the optimal delivery method based on the user's past purchase history. The providing unit can also optimize the delivery method by reflecting the user's past feedback. In this way, the optimal delivery method can be selected by referring to the user's past purchase history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's past purchase history into the generation AI and cause the generation AI to execute a process of selecting the optimal delivery method.

[0104] The providing unit can determine the priority of information provision based on the user's current purchasing willingness when providing information. The providing unit, for example, evaluates the user's current purchasing willingness. For example, the providing unit evaluates the user's purchasing willingness based on criteria such as past purchase history, browsing history, and number of clicks. Furthermore, the providing unit determines the priority of information provision based on the user's current purchasing willingness when providing information. For example, the providing unit prioritizes providing related information when the user's purchasing willingness is high. Furthermore, the providing unit can refrain from providing information when the user's purchasing willingness is low. Furthermore, the providing unit can adjust the priority of information to be provided according to the user's purchasing willingness. In this way, by determining the priority of information provision based on the user's purchasing willingness, highly relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's purchasing willingness to a generation AI and cause the generation AI to execute a process of determining the priority of information provision.

[0105] The providing unit can improve the provision method by reflecting user feedback when providing information. The providing unit, for example, analyzes user feedback. For example, the providing unit analyzes user feedback based on criteria such as reviews, ratings, and comments. Furthermore, the providing unit improves the provision method by reflecting user feedback when providing information. For example, the providing unit customizes the provision interface based on user feedback. Furthermore, the providing unit can optimize the provision method by reflecting user feedback in the past. Furthermore, the providing unit can avoid provision methods that the user is dissatisfied with. In this way, the provision method can be optimized by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to execute processing to improve the provision method.

[0106] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The providing unit also determines the priority of information provision based on the estimated user's emotions. For example, if the user is excited, the providing unit can prioritize providing the latest information. Furthermore, if the user is relaxed, the providing unit can prioritize providing information that matches the user's preferences. Furthermore, if the user is stressed, the providing unit can prioritize providing information that has a relaxing effect. This improves user satisfaction by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to execute a process for determining the priority of information provision.

[0107] The providing unit can select the optimal providing method by taking into account the user's geographical location information when providing information. The providing unit, for example, identifies the user's geographical location information. For example, the providing unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, the providing unit selects the optimal providing method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, the providing unit can prioritize providing product information available in that area. Furthermore, if the user is traveling, the providing unit can prioritize providing product information available at the user's travel destination. Furthermore, if the user is at home, the providing unit can prioritize providing product information available at nearby stores. In this way, by taking the user's geographical location information into account, highly relevant information can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into a generating AI and cause the generating AI to execute a process of selecting the optimal providing method.

[0108] The providing unit can customize the information providing method by analyzing the user's social media activity when providing information. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. Furthermore, the providing unit customizes the information providing method when providing information. For example, the providing unit prioritizes providing product information that the user has "liked" on social media. Furthermore, the providing unit can analyze the content of the user's social media posts and provide related product information. Furthermore, the providing unit can prioritize providing product information shared by the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant information can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to execute processing to customize the information providing method.

[0109] The providing unit can customize the delivery method by reflecting the user's past feedback when providing information. The providing unit, for example, analyzes the user's past feedback. For example, the providing unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The providing unit also customizes the delivery method by reflecting the user's past feedback when providing information. For example, the providing unit prioritizes the use of a delivery method that the user has previously preferred. The providing unit can also customize the delivery interface based on the user's past feedback. The providing unit can also avoid delivery methods that the user has previously been dissatisfied with. In this way, the delivery method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the user's past feedback into the generation AI and cause the generation AI to execute processing to customize the delivery method.

[0110] The recognition unit can estimate the user's emotion and adjust the accuracy of recognition based on the estimated user's emotion. The recognition unit, for example, estimates the user's emotion. For example, the recognition unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. The recognition unit also adjusts the accuracy of recognition based on the estimated user's emotion. For example, if the user is relaxed, the recognition unit selects a recognition method that includes detailed information. If the user is stressed, the recognition unit can select a simple, highly visible recognition method. If the user is excited, the recognition unit can select a visually stimulating recognition method. This improves the recognition accuracy by adjusting the recognition accuracy according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input user emotion data into the generation AI and cause the generation AI to perform a process to adjust the accuracy of recognition.

[0111] The recognition unit can optimize the recognition algorithm by taking into account differences in spelling between sites during recognition. The recognition unit, for example, takes into account differences in spelling between sites. For example, the recognition unit considers differences in spelling between sites based on criteria such as differences in format, terminology, and spelling style. The recognition unit also optimizes the recognition algorithm by taking into account differences in spelling between sites during recognition. For example, the recognition unit optimizes the recognition algorithm by taking into account differences in color writing between sites. The recognition unit can also optimize the recognition algorithm by taking into account differences in product names between sites. The recognition unit can also analyze differences in spelling between sites and select the optimal recognition algorithm. This improves recognition accuracy by taking into account differences in spelling between sites. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on differences in spelling between sites into the generation AI and cause the generation AI to execute processing to optimize the recognition algorithm.

[0112] The recognition unit can improve the accuracy of recognition by referring to the user's past purchase history during recognition. The recognition unit, for example, refers to the user's past purchase history. For example, the recognition unit refers to the user's past purchase history based on criteria such as purchase date and time, purchased items, and purchase frequency. The recognition unit also improves the accuracy of recognition by referring to the user's past purchase history during recognition. For example, the recognition unit prioritizes recognizing information related to items the user has previously purchased. The recognition unit can also prioritize recognizing product information that is likely to be of interest to the user based on the user's past purchase history. The recognition unit can also analyze the user's purchase history and select the optimal recognition target. This improves the accuracy of recognition by referring to the user's past purchase history. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the user's past purchase history into the generation AI and cause the generation AI to execute processing to improve the accuracy of recognition.

[0113] The recognition unit can determine the recognition priority based on the user's current purchasing intent during recognition. The recognition unit, for example, evaluates the user's current purchasing intent. For example, the recognition unit evaluates the user's purchasing intent based on criteria such as past purchase history, browsing history, and number of clicks. The recognition unit also determines the recognition priority based on the user's current purchasing intent during recognition. For example, the recognition unit prioritizes recognizing related information when the user's purchasing intent is high. The recognition unit can also refrain from recognition when the user's purchasing intent is low. The recognition unit can also adjust the priority of recognition targets according to the user's purchasing intent. In this way, highly relevant information can be recognized by determining the recognition priority based on the user's purchasing intent. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input data on the user's purchasing intent to the generation AI and cause the generation AI to execute a process of determining the recognition priority.

[0114] The recognition unit can estimate a user's emotion and determine the priority of information to be recognized based on the estimated user's emotion. The recognition unit, for example, estimates the user's emotion. For example, the recognition unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. The recognition unit also determines the priority of information to be recognized based on the estimated user's emotion. For example, if the user is excited, the recognition unit may prioritize recognizing the latest product information. If the user is relaxed, the recognition unit may also prioritize recognizing information that matches the user's preferences. If the user is stressed, the recognition unit may also prioritize recognizing information that has a relaxing effect. This improves user satisfaction by determining the priority of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input user emotion data into the generation AI and have the generation AI perform a process to determine the priority of information.

[0115] The recognition unit can improve the accuracy of recognition by taking into account the user's geographical location information during recognition. The recognition unit, for example, identifies the user's geographical location information. For example, the recognition unit identifies the user's geographical location information based on criteria such as GPS data, IP address, and location information services. Furthermore, the recognition unit improves the accuracy of recognition by taking into account the user's geographical location information during recognition. For example, when the user is in a specific area, the recognition unit may prioritize recognizing product information available in that area. Furthermore, when the user is traveling, the recognition unit may prioritize recognizing product information available at the user's travel destination. Furthermore, when the user is at home, the recognition unit may prioritize recognizing product information available at nearby stores. In this way, by taking the user's geographical location information into account, highly relevant information can be recognized. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit may input the user's geographical location information data into the generation AI and cause the generation AI to execute processing to improve the accuracy of recognition.

[0116] The recognition unit can improve the accuracy of recognition by analyzing the user's social media activity during recognition. The recognition unit, for example, analyzes the user's social media activity. For example, the recognition unit analyzes the user's social media activity based on criteria such as the content of posts, the number of likes, and the number of followers. The recognition unit can also improve the accuracy of recognition by analyzing the user's social media activity during recognition. For example, the recognition unit prioritizes recognizing product information that the user has "liked" on social media. The recognition unit can also analyze the content of the user's social media posts to recognize related product information. The recognition unit can also prioritize recognizing product information shared by the user's friends on social media. In this way, highly relevant information can be recognized by analyzing the user's social media activity. Some or all of the above-described processing in the recognition unit may be performed using, or without, AI. For example, the recognition unit can input data on the user's social media activity into the generation AI and cause the generation AI to perform processing to improve the accuracy of recognition.

[0117] The recognition unit can customize the recognition algorithm by reflecting the user's past feedback during recognition. The recognition unit, for example, analyzes the user's past feedback. For example, the recognition unit analyzes the user's past feedback based on criteria such as reviews, ratings, and comments. The recognition unit also customizes the recognition algorithm by reflecting the user's past feedback during recognition. For example, the recognition unit preferentially uses a recognition method that the user previously preferred. The recognition unit can also optimize the recognition algorithm based on the user's past feedback. The recognition unit can also avoid a recognition method that the user previously dissatisfied with. In this way, the recognition algorithm can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input data of the user's past feedback to the generation AI and cause the generation AI to perform a process of customizing the recognition algorithm. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and registers products that the user wants. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information from various shopping sites and flea market sites. The organization unit is realized by the specific processing unit 290 of the data processing device 12 and organizes the collected information. The provision unit is realized by the control unit 46A of the smart device 14 and provides the organized information to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and registers products that the user wants. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information from various shopping sites and flea market sites. The organization unit is realized by the specific processing unit 290 of the data processing device 12 and organizes the collected information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the organized information to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and registers products that the user wants. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information from various shopping sites and flea market sites. The organization unit is realized by the specific processing unit 290 of the data processing device 12 and organizes the collected information. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the organized information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and registers products that the user wants. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information from various shopping sites and flea market sites. The organization unit is realized by the specific processing unit 290 of the data processing device 12 and organizes the collected information. The provision unit is realized by the control unit 46A of the robot 414 and provides the organized information to the user.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The reception unit can analyze the user's purchasing history and automatically suggest new products related to products the user has previously purchased. For example, it can suggest accessories or upgraded versions of electronic devices the user has previously purchased. It can also suggest new collections of clothing and related fashion items the user has previously purchased. It can also suggest new recipes and related ingredients for food the user has previously purchased. This can improve the user's purchasing experience by suggesting highly relevant products based on the user's purchasing history.

[0120] The collection unit can analyze the user's social media activity and collect related information based on the products the user has "liked" and posts the user has shared. For example, the collection unit can collect the latest information and discount information on products the user has "liked" on social media. It can also collect product information related to posts the user has shared. It can also preferentially collect product information shared by the user's friends. This allows the collection of highly relevant information based on the user's social media activity to provide information that matches the user's interests.

[0121] The organizing unit can organize the collected information according to the user's level of expertise. For example, if the user has specialized knowledge, it can provide an organization method that includes detailed technical information and technical terminology. Alternatively, if the user does not have specialized knowledge, it can provide an organization method that uses simple words and illustrations. Furthermore, it can adjust the display format of the information according to the user's level of expertise. This makes it easier to understand the information by organizing it according to the user's level of expertise.

[0122] The providing unit can provide product information available at nearby stores with priority, taking into account the user's geographical location information. For example, when the user is in a specific area, product information available at that area can be provided in list form. Also, when the user is traveling, product information available at the user's travel destination can be provided in map form. Furthermore, when the user is at home, product information available at nearby stores can be notified. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0123] The providing unit can customize the information providing method by reflecting the user's past feedback. For example, the providing unit can give priority to using a providing method (list format, graph format, etc.) that the user has previously preferred. The providing interface can also be customized based on the user's past feedback. Furthermore, it can also avoid providing methods that the user has previously been dissatisfied with. In this way, the information providing method can be optimized by reflecting the user's past feedback.

[0124] The reception unit can estimate the user's emotions and adjust the timing of product registration based on the estimated user emotions. For example, if the user is feeling stressed, a notification urging the user to register the product during a time when the user is able to relax can be sent. Also, if the user is excited, a notification urging the user to register the product immediately can be sent. Furthermore, if the user is tired, a suggestion can be made to register the product the next day. In this way, adjusting the timing of product registration according to the user's emotions can reduce the user's stress.

[0125] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is relaxed, information collection can be started immediately. Also, if the user is feeling stressed, information collection can be performed during a time when the user is able to relax. Furthermore, if the user is excited, information collection can be started immediately. In this way, by adjusting the timing of information collection according to the user's emotions, the user's stress can be reduced.

[0126] The organizing unit can estimate the user's emotions and adjust the information organization method based on the estimated user's emotions. For example, if the user is relaxed, an organization method including detailed information can be provided. If the user is stressed, a simple organization method with high visibility can be provided. Furthermore, if the user is excited, a visually stimulating organization method can be provided. In this way, by adjusting the information organization method according to the user's emotions, the user's stress can be reduced.

[0127] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, a method of providing information that includes detailed information can be selected. If the user is stressed, a simple and highly visible method of providing information can be selected. Furthermore, if the user is excited, a visually stimulating method of providing information can be selected. In this way, by adjusting the method of providing information according to the user's emotions, it is possible to reduce the user's stress.

[0128] The recognition unit can estimate the user's emotion and adjust the recognition accuracy based on the estimated user's emotion. For example, if the user is relaxed, a recognition method including detailed information can be selected. If the user is stressed, a simple and highly visible recognition method can be selected. Furthermore, if the user is excited, a visually stimulating recognition method can be selected. In this way, the recognition accuracy can be improved by adjusting the recognition accuracy according to the user's emotion.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit registers the product the user wants. The user can enter a specific product name and characteristics, such as "red sneakers" or "latest smartphone." Step 2: The collection unit collects related information from various shopping sites and flea market sites based on the product information registered by the reception unit. The collection unit, for example, uses generation AI to take into account differences in notation and prevent cases where the search does not return any hits. Step 3: The organizer organizes the information collected by the collector, for example, in the form of price comparisons, stock availability, vendor ratings, and the like. Step 4: The providing unit provides the information organized by the organizing unit to the user. The providing unit provides the information in, for example, a list format or a graph format.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 where users register the products they want; a collection unit that collects related information from various shopping sites and flea market sites based on the product information registered by the reception unit; an organizing unit that organizes the information collected by the collecting unit; a providing unit that provides the information organized by the organizing unit to a user; Equipped with A system characterized by:

2. The collecting unit Equipped with a recognition unit that recognizes different color writing styles or product names for each site 2. The system of claim 1.

3. The organizing unit Organize the collected information in the form of price comparisons, availability, and vendor ratings 2. The system of claim 1.

4. The providing unit Provide information to users in an easy-to-read format 2. The system of claim 1.

5. The providing unit Present information in list or graph format 2. The system of claim 1.

6. The reception unit Estimate user emotions and adjust the timing of product registration based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze users' past registration history and select the optimal registration method 2. The system of claim 1.

8. The reception unit When registering products, filter based on the user's current purchasing intent and areas of interest.

2. The system of claim 1.

9. The reception unit When registering a product, select the most appropriate registration method depending on the user's input method.

2. The system of claim 1.

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