System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to upload a photo of a home appliance; means for analyzing a model number, a manufacturer, and a date of manufacture from the photo of the home appliance on a server; means for collecting price information from a related web site based on the analyzed information; means for evaluating the collected information by an evaluation algorithm and listing optimal new products; and means for summarizing and presenting characteristics of the listed products to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When considering replacing home appliances, consumers typically need to gather and compare a large amount of information. This process is extremely time-consuming and laborious, making it particularly difficult for consumers with little technical knowledge. There are also problems, such as the risk of purchasing at an inappropriate price or being unable to make a decision due to being overwhelmed by the number of options. It is necessary to solve these problems and enable consumers to make more efficient and satisfying choices. [Means for solving the problem]

[0005] The present invention is a system that includes a means for users to upload photos of home appliances, a means for analyzing the model number, manufacturer, and manufacturing date from the photos of the home appliances on a server, a means for collecting price information from related websites based on the analyzed information, a means for evaluating the collected information using an evaluation algorithm to list optimal new products, and a means for summarizing the features of the listed products and presenting them to the user. Furthermore, the system also includes a means for making suggestions based on the user's desired criteria and a means for storing the collected information in a database, enabling consumers to easily select and purchase the optimal home appliances.

[0006] "Users" refer to consumers who are considering replacing their home appliances.

[0007] "Home appliances" is a general term for electronic devices and electrical appliances used in the home.

[0008] "Photos" refer to still image data of the exterior of home appliances.

[0009] "Server" refers to a remote computer system used for data processing and information analysis.

[0010] "Image analysis module" refers to a component consisting of software and hardware for extracting specific information (such as model number, manufacturer, and manufacturing date) from a photograph.

[0011] "Generative AI" refers to artificial intelligence that has the ability to generate and analyze information from large amounts of data.

[0012] "Model number" refers to an identification number assigned by a manufacturer to identify a specific product.

[0013] "Manufacturer" refers to the company or business that produced the home appliance.

[0014] "Date of manufacture" refers to information indicating the year and month when a home appliance was manufactured.

[0015] "Scraping" refers to a technique for automatically obtaining necessary information from a website.

[0016] "Price at time of new purchase" refers to the price at the time the home appliance was purchased as new.

[0017] "Used purchase price" refers to the price at which the home appliance is purchased on the used market.

[0018] "Lowest price on sale" refers to the lowest price at which the home appliance is currently being sold on the market.

[0019] "Evaluation algorithm" refers to the calculation procedures and methods used to evaluate and rank products based on collected information.

[0020] A "database" refers to a system that systematically stores collected information and retrieves and uses it as needed.

[0021] "Presentation" refers to materials and screen displays that convey evaluation results to users in an easy-to-understand manner.

[0022] "Online store" refers to a general term for a retailer's website that sells products over the Internet.

[0023] A "link" is a hypertext reference that allows a user to access a particular web page. [Brief explanation of the drawings]

[0024] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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, a 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), and an APU (Accelerated Processing Unit).

[0028] 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.

[0029] 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.

[0030] 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), Bluetooth (registered trademark), etc.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0036] 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.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. Specifically, users upload photos of the home appliances, and the system analyzes the images on a server, collects necessary information, and proposes optimal new products. This system is configured as follows:

[0046] User operations

[0047] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, television, etc.), and the photo is uploaded to the system via a dedicated application on a smartphone or PC.

[0048] Image analysis on the server

[0049] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[0050] Collection of information

[0051] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[0052] Information collection and evaluation

[0053] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0054] Generate a presentation

[0055] The generative AI summarizes the features of the listed products and creates a presentation for the user, which includes proposals based on the user's desired criteria.

[0056] Provision to users

[0057] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[0058] Specific examples

[0059] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." The server then uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products (e.g., "Company A's model that prioritizes price" and "Company B's model that prioritizes performance"). The AI ​​then summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user reviews the materials, selects the product they like, and purchases it from the online store.

[0060] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] Users take a photo of the home appliances they are using at home using a dedicated application on their smartphone or PC, and then press the upload button on the application.

[0064] Step 2:

[0065] The device sends the photo taken by the user from the application to the server, which includes the photo data as well as basic information about the user (such as the user ID and location information).

[0066] Step 3:

[0067] The server receives the photo data sent from the terminal and transfers it to the image analysis module.

[0068] Step 4:

[0069] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[0070] Step 5:

[0071] Based on the analyzed information, the server collects price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[0072] Step 6:

[0073] The server collects the following information:

[0074] New purchase price

[0075] Current second-hand purchase price

[0076] Lowest price on sale

[0077] Step 7:

[0078] The server stores the collected information in a database.

[0079] Step 8:

[0080] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0081] Step 9:

[0082] The server's generation AI summarizes the features of each product in the optimal new product list and creates presentation materials for users.

[0083] Step 10:

[0084] The server transmits the created presentation materials to the terminal.

[0085] Step 11:

[0086] The terminal receives the presentation materials sent from the server and displays them to the user.

[0087] Step 12:

[0088] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[0089] The above is a specific flow of program processing in the system of the present invention. The operations performed in each step allow the user to easily find and purchase the most suitable home appliance.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] Today's consumers are surrounded by a wide variety of home appliances, and are required to efficiently collect and evaluate vast amounts of information when replacing or purchasing new products. However, manually researching detailed information such as product model numbers and manufacturing dates is extremely time-consuming, and when price information is also collected and compared, it requires a considerable amount of time and effort. This makes it difficult for consumers to select the optimal home appliance.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date information from the images of the home appliances on the server, means for collecting price data from related websites based on the analyzed data, means for summarizing the listed product characteristics using a generative AI model and making recommendations, and means for storing the collected data in a database. This enables users to efficiently replace their home appliances and easily select and purchase the optimal new products.

[0095] "Users" refer to consumers who use the system to upload images of home appliances and receive new product suggestions.

[0096] "Server" refers to the central processing unit that receives and analyzes data sent by users, collects and evaluates information, and generates and sends optimal new product proposals.

[0097] "Home appliances" refers to electronic devices used in the home, such as refrigerators, washing machines, and televisions.

[0098] "Images" refers to photographs or pictures of home appliances.

[0099] "Model number" refers to the product identification number assigned by the manufacturer of a home appliance.

[0100] "Manufacturing company" refers to a corporation that manufactures and sells home appliances.

[0101] "Manufacturing date information" refers to data indicating the date and year when a home appliance was manufactured.

[0102] "Related websites" refers to internet pages that provide prices and information about home appliances, such as price comparison sites, online stores, and review sites.

[0103] "Price data" refers to price information about home appliances, such as the price when purchased new, the price at which a used item is purchased, and the lowest sale price.

[0104] A "generative AI model" refers to an artificial intelligence computational model that summarizes product characteristics based on large amounts of data and makes suggestions to users.

[0105] "Evaluation algorithm" refers to a calculation method for evaluating and selecting optimal new products based on collected data.

[0106] "Database" refers to a digital storage system for systematically storing and managing collected information.

[0107] "Means" refers to a method or apparatus for achieving a particular function.

[0108] "Suggestion" refers to the act of selecting the most suitable home appliance for the user and presenting the options.

[0109] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes optimal new products.

[0110] First, a user takes a picture of the home appliances they use at home using a smartphone or digital camera. This image is then uploaded to the system using a dedicated application on their smartphone or PC. The dedicated application has an upload function that allows the user to send the image to the server.

[0111] The server then receives the image sent by the user and passes it to an image analysis module, which uses tools such as Google Cloud Vision or Azure Computer Vision. This module uses a generative AI model (such as OpenAI's CLIP model or YOLOv5) to accurately extract information such as the appliance's model number, manufacturer, and manufacturing date from the image.

[0112] Based on the analyzed information, the server collects price data from related websites. Web scraping technologies (such as BeautifulSoup or Scrapy) are used to obtain information such as "new purchase price," "current used buy price," and "lowest sale price" from price comparison sites and online stores (e.g., price comparison sites and major online stores). The collected data is then stored in a database (e.g., MySQL or PostgreSQL) on the server.

[0113] The stored data is then fed into an evaluation algorithm (e.g., Linear Regression or Decision Trees) to generate a list of optimal new products based on criteria such as "price-first," "performance-first," and "balanced." A generative AI model (e.g., OpenAI's GPT-4) is used to summarize the features of each product from this list and create a presentation for users.

[0114] Finally, the server sends the generated presentation materials to the user's device, where the user can review the materials through a dedicated application, select the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase process at the online store.

[0115] As a concrete example, consider a user considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads the photo through a dedicated application. The server receives the photo and uses an image analysis module to extract information such as "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products based on the user's desired criteria (e.g., "price-first" or "performance-first"). The generation AI summarizes the features of the products on the list and creates presentation materials. Finally, the materials are sent to the user's device, where they can be viewed using a dedicated application. The user selects from the suggested products and clicks the purchase link to purchase them from the online store.

[0116] An example prompt might be, "I'm considering replacing my refrigerator, so I uploaded a photo of my current one. Can you suggest a suitable new product?"

[0117] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Users take pictures of home appliances and upload them to the system through a dedicated application.

[0121] Input: Image of a home appliance

[0122] Specific operation: A user takes pictures of the home appliances they use at home using a smartphone or digital camera. Then, they use a dedicated application to upload the pictures to the system. The application has an "Upload image" button, which the user clicks.

[0123] Step 2:

[0124] The server receives the uploaded images and passes them to the analysis module.

[0125] Input: User-submitted images of home appliances

[0126] Output: Passing image data to the analysis module

[0127] Specific operation: The server receives images uploaded by users via HTTP requests and passes the image data to an analysis module, which uses cloud services (e.g., Google Cloud Vision or Azure Computer Vision).

[0128] Step 3:

[0129] The server uses an image analysis module to extract model number, manufacturer, and manufacturing date information.

[0130] Input: Image data

[0131] Output: Extracted model number, manufacturer, and manufacturing date information

[0132] How it works: The server calls the image analysis module and uses an AI model (e.g., YOLOv5) to analyze the text and features in the image. The extracted data (model number, manufacturer, and manufacturing date information) is used for further processing.

[0133] Step 4:

[0134] The server collects price data from relevant websites based on the parsed information.

[0135] Input: Extracted model number, manufacturer, manufacturing date information

[0136] Output: Collected price data

[0137] Specific operation: The server uses web scraping technology (e.g., BeautifulSoup or Scrapy) to obtain price data such as "new purchase price," "used purchase price," and "lowest sale price" from price comparison sites and online stores.

[0138] Step 5:

[0139] The server stores the collected price data in a database.

[0140] Input: Collected price data

[0141] Output: Price data stored in a database

[0142] What it does: The server uses a database connectivity library (e.g., SQLAlchemy) to store the collected price data in a database (e.g., MySQL, PostgreSQL), making it available for subsequent use in the evaluation algorithm.

[0143] Step 6:

[0144] The server uses a rating algorithm to generate a list of optimal new products.

[0145] Input: Price data stored in a database

[0146] Output: Best New Product List

[0147] Specific operation: The server executes an evaluation algorithm (e.g., Linear Regression, Decision Trees) and analyzes the results based on the user's desired criteria (e.g., "price-oriented," "performance-oriented," "balanced," etc.). It generates a list of optimal new products and proceeds to the next step.

[0148] Step 7:

[0149] The server uses a generative AI model to summarize the features of the listed products and create presentation materials.

[0150] Input: Best New Product List

[0151] Output: Presentation materials

[0152] Specific operation: Using a generative AI model (e.g., GPT-4), the features of each product in the new product list are summarized. The summarized content is compiled into a presentation document for users and created as a proposal document.

[0153] Step 8:

[0154] The server transmits the generated presentation materials to the terminal.

[0155] Input: Presentation materials

[0156] Output: Proposal materials displayed on the user's device

[0157] Specific operation: The server sends the generated presentation materials to the user's device, where the user can view them through a dedicated application.

[0158] Step 9:

[0159] The user selects from the suggested products and clicks the purchase link to make a purchase at the online store.

[0160] Input: Product information selected by the user

[0161] Output: Purchase processing on the online store

[0162] Specific operation: The user checks the suggested products in the dedicated application and clicks the purchase link for the desired product, which redirects them to the purchase page of the online store and allows them to proceed with the purchase.

[0163] This allows users to efficiently and hassle-freely select and purchase the most suitable home appliances.

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] When modern consumers consider replacing their home appliances, the time and effort required is a major problem. The process of manually collecting information such as product model numbers, manufacturers, and manufacturing dates, and then selecting the optimal new product based on that information, is particularly tedious. Furthermore, it is necessary to individually collect price information from numerous online sites, compare and evaluate options, which requires advanced knowledge and time. This presents a challenge for consumers, making it difficult to select the optimal new product without hassle and purchase it efficiently.

[0167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0168] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date from the images of the home appliances on the server, means for collecting price information from related online sites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products using a generative AI model and presenting them to the user, and means for providing purchase links for the products selected by the user. This allows consumers to easily select and purchase the optimal home appliances.

[0169] "Users" are consumers who use the system to consider replacing their home appliances.

[0170] "Images of Home Appliances" are photographs of home appliances taken by users using smartphones or other image capture devices and uploaded to the system.

[0171] "Server" means a computer system on a network that performs functions such as image analysis, information gathering, evaluation algorithms, and presentation generation using generative AI models.

[0172] A "model number" is an identification number that indicates a specific model of a home appliance.

[0173] "Manufacturer" refers to the company that produced the home appliance.

[0174] "Date of manufacture" is the date or month when the home appliance was manufactured.

[0175] "Online site" refers to a website that provides product price information, including price comparison sites and mail order sites.

[0176] "Price information" refers to information about the price of home appliances, such as the purchase price, second-hand purchase price, and sale price.

[0177] An "evaluation algorithm" is a calculation method for selecting the best new product for a user based on collected pricing information and other data.

[0178] A "generative AI model" is an artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[0179] A "presentation" is a document generated by a generative AI model and presented to the user about the features of a new product and the reasons for its proposal.

[0180] A "purchase link" is a link that allows a user to purchase the selected product directly from the online store.

[0181] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes new products.

[0182] System Configuration

[0183] Hardware

[0184] User terminal: A device such as a smartphone or PC that allows users to take pictures of home appliances and upload them to the system.

[0185] Server: A computer system on a network that performs image analysis, information collection, and presentation generation using generative AI models.

[0186] software

[0187] Image analysis module: Software for analyzing the model number, manufacturer, and manufacturing date from images of home appliances.

[0188] Information collection module: Software to collect price information from related online sites based on the analyzed information. It uses scraping technology.

[0189] Evaluation algorithm: A calculation method for selecting the most suitable new product based on collected information.

[0190] Generative AI model: An artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[0191] Processing steps

[0192] 1. Image upload: Users take pictures of home appliances using their smartphones or PCs and upload them to the system via a dedicated application.

[0193] 2. Image analysis: The server processes the received images using an image analysis module to extract information such as the appliance model number, manufacturer, and manufacturing date.

[0194] 3. Information collection: Based on the extracted information, the server collects product price information (new purchase price, used purchase price, lowest sale price, etc.) from related online sites.

[0195] 4. Selection of optimal products: The server processes the collected price information with an evaluation algorithm to generate a list of optimal products based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[0196] 5. Presentation generation: The generative AI model summarizes the features of the listed products and creates a presentation to provide to the user.

[0197] 6. User presentation and purchase link provision: Finally, the presentation materials are sent from the server to the user's terminal, and the user can review the materials and proceed with purchasing the selected product at the online store via the purchase link.

[0198] Specific examples

[0199] When a user is considering replacing their refrigerator, they take a picture of their home refrigerator using their smartphone and upload it through a dedicated application. The server uses an image analysis module to extract the model number, manufacturer, and manufacturing date, and uses an information collection module to collect information from price comparison sites and online shopping sites, such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen." An evaluation algorithm then generates a list of optimal new products, such as "Company A's model that prioritizes price" and "Company B's model that prioritizes performance." The generative AI model summarizes the product features based on the list, creates presentation materials, and provides them to the user.

[0200] Prompt Sentence Examples

[0201] "Based on the model number, manufacturer, and production date extracted from the image, collect the best price information and generate the best new product proposals using an evaluation algorithm."

[0202] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0203] Step 1:

[0204] Users take pictures of home appliances using their smartphones or PCs and upload them to the system through a dedicated application. The input is the image of the home appliance, and the output is the uploaded image data. The device then sends this image data to the server.

[0205] Step 2:

[0206] The server receives the uploaded images and uses an image analysis module to analyze the model number, manufacturer, and manufacturing date from the images. The input is the uploaded image data, and the output is the extracted product information (model number, manufacturer, manufacturing date). The server temporarily stores the analysis results in a database.

[0207] Step 3:

[0208] The server uses the parsed product information to use scraping technology to collect price information from related online sites. The input is product information (model number, manufacturer, manufacturing date), and the output is collected price information (new purchase price, used buyback price, lowest sale price). The server stores the collected price information in a database.

[0209] Step 4:

[0210] The server uses an evaluation algorithm to generate an optimal list of new products based on the collected price information. The input is the collected price information, and the output is a list of optimal new products. This list is also based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[0211] Step 5:

[0212] The server uses a generative AI model to summarize the product features of the optimal new product list and create a presentation. The input is the optimal new product list, and the output is the presentation. The generative AI model summarizes the product's benefits and features in a format that is easy for users to understand.

[0213] Step 6:

[0214] The server sends the generated presentation materials to the user's terminal and displays them to the user. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can check the proposed new products, select the most suitable product, and click the purchase link.

[0215] This allows users to easily select and purchase the most suitable home appliances.

[0216] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0217] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is configured as follows.

[0218] User operations

[0219] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, TV, etc.). This photo is then uploaded to the system via a dedicated application on a smartphone or PC. The application can also capture the user's voice commands and facial expressions at the same time.

[0220] Image analysis on the server

[0221] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[0222] Collection of information

[0223] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[0224] Information collection and evaluation

[0225] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0226] Emotion Engine Operation

[0227] The server then uses an emotion engine to analyze the user's voice, facial expressions, and past behavioral data. This allows the server to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time. The emotion engine then adjusts the content of the recommendations based on the analysis results. For example, if the user shows a relieved expression, the server will strengthen price-focused recommendations.

[0228] Generate a presentation

[0229] The generative AI summarizes the features of the listed products and creates a presentation that takes into account the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state.

[0230] Provision to users

[0231] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[0232] Specific examples

[0233] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[0234] At the same time, the emotion engine analyzes the user's voice commands and facial expressions to detect if the user is concerned about price. The emotion engine reflects this result and strengthens price-focused recommendations. The generative AI summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user then reviews the materials, selects the product they like, and makes a purchase from the online store.

[0235] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[0236] The processing flow will be explained below.

[0237] The present invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is specifically implemented as follows.

[0238] Step 1:

[0239] Users take photos of their home appliances using a dedicated application on their smartphone or PC, press the upload button on the application, and simultaneously provide their emotional state to the system by using voice commands or capturing facial expressions with a webcam.

[0240] Step 2:

[0241] The terminal transmits the photos taken by the user, voice data, and facial expression capture data from the application to the server.

[0242] Step 3:

[0243] The server receives the photo data sent from the device and transfers it to the image analysis module. It also transfers the voice data and facial expression capture data to the emotion engine at the same time.

[0244] Step 4:

[0245] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[0246] Step 5:

[0247] The server's emotion engine analyzes the user's voice data and facial expression capture data to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time.

[0248] Step 6:

[0249] The server uses the analyzed information (model number, manufacturer, manufacturing date) to collect price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[0250] Step 7:

[0251] The server collects the following information:

[0252] New purchase price

[0253] Current second-hand purchase price

[0254] Lowest price on sale

[0255] Step 8:

[0256] The server stores the collected information in a database.

[0257] Step 9:

[0258] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0259] Step 10:

[0260] The server's AI then summarizes the features of each product in the list of optimal new products and creates a presentation for the user. However, it also adjusts the proposals based on the user's emotional state. For example, if the user expresses anxiety, it emphasizes price-focused proposals that will reassure the user.

[0261] Step 11:

[0262] The server transmits the created presentation materials to the terminal.

[0263] Step 12:

[0264] The terminal receives the presentation materials sent from the server and displays them to the user.

[0265] Step 13:

[0266] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[0267] The above is the specific flow of program processing in the system of the present invention. The operations performed at each step allow users to easily find the best home appliances, and by utilizing the emotion engine, they can receive personalized suggestions and have a highly satisfying purchasing experience.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] Today's consumers must refer to a large number of options and information when choosing the best home appliance from a wide variety of products, which requires a lot of time and effort. Furthermore, systems that make blanket recommendations without recognizing emotions have the problem of making optimal product recommendations that meet the user's true needs. Furthermore, a mechanism for appropriately evaluating and organizing the collected information is needed.

[0271] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for taking and uploading a photo of the home appliance used by the user, means for capturing the user's voice commands and facial expressions simultaneously with the photo, means for extracting the model number, manufacturer, and manufacturing date from the photo of the home appliance on the server, means for collecting price information from related websites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and generating presentation materials based on the user's emotional state, and means for transmitting the generated presentation materials to the user's terminal and displaying them. This allows the user to easily select the optimal home appliance, and by using an emotion engine, personalized suggestions based on the user's emotional state are possible.

[0272] "Means for taking and uploading photos" refers to a system in which users take photos of home appliances using their smartphones or PCs and send them to a server via a dedicated application.

[0273] "Means for capturing voice commands and facial expressions" refers to a mechanism that simultaneously captures voice instructions and facial expression data and sends them to a server when a user uploads a photo of a home appliance.

[0274] "Means for extracting model numbers, manufacturers, and manufacturing dates from photographs of home appliances on a server" refers to a system that analyzes photographs uploaded to a server using a generative AI model, etc., to identify the model number, manufacturer, and manufacturing date of the home appliance.

[0275] "Means for collecting price information" refers to a system in which the server collects price data from related websites (price comparison sites, online stores, review sites, etc.) based on the analyzed information.

[0276] "Means for evaluating using an evaluation algorithm and listing optimal new products" refers to a system in which the server selects and lists optimal new products according to specific evaluation criteria (e.g., price-oriented, performance-oriented, balanced, etc.) based on collected price information and product data.

[0277] The "means for generating presentation materials" is a mechanism by which the server summarizes the features of the listed products and generates presentation materials that take into account the user's emotional state.

[0278] The "means for transmitting and displaying presentation materials" refers to a mechanism for transmitting the generated presentation materials to a user's terminal, allowing the user to view the materials on the terminal.

[0279] The "emotion engine" is a system that analyzes the user's voice, facial expressions, and past behavioral data to recognize the user's emotional state (e.g., anxiety, satisfaction, excitement, etc.) in real time and adjusts the content of suggestions.

[0280] The present invention is a system for hassle-freely proposing optimal new products to consumers who are considering replacing their home appliances. In particular, by combining an emotion engine that recognizes the user's emotions, more personalized proposals can be made. This system utilizes various hardware and software to flexibly respond to user needs. This section describes specific embodiments of the system.

[0281] First, the user uses a smartphone or PC to take a photo of the home appliance they are currently using (e.g., refrigerator, washing machine, television, etc.). The user then launches a dedicated application and uploads the photo to the system. This application has the function of simultaneously capturing the user's voice commands and facial expressions. This data is then sent in bulk to the server.

[0282] The server receives the uploaded photos and voice and facial expression data. The image analysis module for photo analysis uses a generative AI model, which enables highly accurate extraction of information such as the model number, manufacturer, and manufacturing date. For example, the analysis results may yield information such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015." The information extracted in this way is used in the next processing step.

[0283] Next, the server uses this analysis information to collect price information from related websites. This information is collected using scraping technology, and data such as "price when purchased new," "current used purchase price," and "lowest sale price" are collected. For example, "price when purchased new: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" may be collected.

[0284] This collected information is stored in a database on the server. The server uses an evaluation algorithm to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced." For example, if price is the most important criterion, information such as "XYZ200 model, price: 70,000 yen" will be listed.

[0285] Furthermore, the server is equipped with an emotion engine that can analyze the user's voice, facial expressions, and past behavioral data. This allows the system to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time and adjust the content of its recommendations accordingly. For example, if the user is concerned about price, the system will strengthen price-focused recommendations.

[0286] The generative AI summarizes the features of the listed products and creates a presentation that reflects the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state. For example, the presentation might be, "Affordable refrigerator XYZ200, features: freezer included, price: 70,000 yen."

[0287] Finally, the server sends the generated presentation materials to the user's device, where the user can review them on their smartphone or PC. The user can then select the most suitable product from the presented products, and once they have decided to purchase, they can click the instant purchase link to proceed with the purchase at the online store.

[0288] As a concrete example of this system, consider a user considering replacing their refrigerator. The user takes a photo of their refrigerator at home and uploads it to the server using a dedicated application. The server then uses an image analysis module to extract information such as the refrigerator's model number, manufacturer, and manufacturing date, and collects price information from related websites. At the same time, the emotion engine analyzes the user's voice commands and facial expressions to understand their emotional state. Finally, the generative AI creates a presentation summarizing the plan and presents it to the user.

[0289] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[0290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0291] Step 1:

[0292] The user takes a photo of the home appliances in their home and uploads it using a dedicated application. The user takes a photo of the appliance using a smartphone or PC and imports the photo file into the application. At the same time, voice commands and facial expressions are also captured. This information is sent from the application to the server. The input is the photo of the appliance taken by the user, voice data, and facial expression data, and the output is data sent to the server.

[0293] Step 2:

[0294] The server inputs the photos received from the user into the image analysis module. This module uses a generative AI model to perform highly accurate image analysis. Specifically, it extracts information such as the home appliance model number, manufacturer, and manufacturing date from the photo. After analysis, data such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015" is obtained. The input is the uploaded photo, and the output is the analyzed product information.

[0295] Step 3:

[0296] The server collects price information from websites based on the analyzed product information. Using scraping technology, data such as "new purchase price," "current used purchase price," and "lowest sale price" are obtained from related sites (price comparison sites, online stores, reviews, etc.). For example, information such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" is collected. The input is product information, and the output is the collected price information.

[0297] Step 4:

[0298] The server stores the collected price information in a database, which provides a basis for subsequent processing and revaluation. The input is the collected price information, and the output is the data stored in the database.

[0299] Step 5:

[0300] The server uses an evaluation algorithm based on the stored information to generate an optimal list of new products. Evaluation criteria include "price-oriented," "performance-oriented," and "balanced." For example, a list such as "price-oriented refrigerator XYZ200, price: 70,000 yen" is generated. The input is existing data in the database, and the output is the listed product information.

[0301] Step 6:

[0302] The server activates an emotion engine and analyzes the user's voice commands and facial expression data. By analyzing past behavioral data as well, the user's emotional state is recognized. The content of suggestions is adjusted based on these results. For example, if the user is concerned about price, suggestions that emphasize price are strengthened. The input is voice data, facial expression data, and past behavioral data, and the output is the emotion analysis results.

[0303] Step 7:

[0304] The server uses generative AI to summarize the features of the listed products and create presentation materials that reflect the user's emotional state. The user's desired criteria are also taken into consideration. For example, a presentation might be generated that describes the "price-conscious refrigerator XYZ200, features: freezer included, price: 70,000 yen." The input is the listed product information and the results of the emotional analysis, and the output is the presentation materials.

[0305] Step 8:

[0306] The server sends the generated presentation materials to the user's device, where the user can view them on their smartphone or PC. The input is the presentation materials, and the output is the materials sent to the user's device.

[0307] Step 9:

[0308] The user reviews the provided presentation materials and selects the most suitable product. If they decide to purchase, they click the instant purchase link to complete the purchase process at the online store. The input is the provided presentation materials, and the output is the purchase process for the selected product.

[0309] As a result, users can easily select the most suitable home appliances and receive personalized suggestions from the emotion engine.

[0310] (Application example 2)

[0311] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] When considering replacing home appliances, modern consumers find it difficult to gather a large amount of information and select the most suitable product. Furthermore, conventional systems make uniform recommendations without considering the user's emotions or individual preferences, making it difficult to improve user satisfaction. Given this background, there is a growing need for a system that takes user emotions into account and makes more personalized product recommendations.

[0313] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload an image of a home appliance, means for analyzing the model number, manufacturer, and manufacturing date from the image of the home appliance on the server, means for collecting price information from related information sources based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and presenting them to the user, means for analyzing the user's facial expressions and voice, and means for adjusting the content of proposals based on the results of the emotion analysis. This makes it possible to select and propose optimal products based on the user's emotions and desired criteria, thereby improving user satisfaction.

[0314] "Means for users to upload images of home appliances" refers to an interface or function that allows users to send images of home appliances to the system using a smartphone or other device.

[0315] "Means for analyzing the model number, manufacturer, and manufacturing date from images of home appliances on a server" refers to a function that uses image analysis technology on a server to automatically extract information such as the model number, manufacturer, and manufacturing date from images of home appliances.

[0316] "Means for collecting price information from related sources based on the analyzed information" refers to a technology for using the extracted product information to collect related price information from external sources such as price comparison sites and online stores.

[0317] "Means for evaluating collected information using an evaluation algorithm and listing optimal new products" refers to a function that uses an evaluation algorithm to select and list optimal new products based on collected price information and product information.

[0318] The "means for summarizing the features of the listed products and presenting them to the user" is a function for aggregating the features of the listed products, summarizing the main points, and presenting them to the user.

[0319] "Means for analyzing the user's facial expressions and voice" refers to technology for analyzing the user's facial expressions and voice and recognizing their emotional state.

[0320] The "means for adjusting the content of proposals based on the results of emotion analysis" is a function that takes into account the emotional state of the user obtained from the emotion analysis and adjusts the content of product proposals accordingly.

[0321] This invention is a system that assists users in the process of efficiently replacing their home appliances. Users upload images of their home appliances to an application using a smartphone or other device. The images are then sent to a server, where image analysis begins.

[0322] On the server, image analysis technology is used to automatically extract information such as model number, manufacturer, and manufacturing date from the uploaded image. This process uses image analysis software such as TensorFlow and OpenCV. The extracted information is then used by the server to collect price information from related sources. To collect price information, scraping technologies such as BeautifulSoup and Selenium are used.

[0323] The server also analyzes the user's facial expressions and voice in real time to recognize their emotional state. This emotion analysis uses Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API. Based on the analysis results, the server adjusts the proposal content and makes the optimal product recommendations for the user. The proposal content is displayed on the user's device as presentation materials generated using generative AI models such as OpenAI's GPT-4.

[0324] For example, consider the case where a user is considering replacing their refrigerator. The user takes a picture of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the image and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, based on this information, the server collects information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[0325] At the same time, the server uses an emotion engine to analyze the user's voice commands and facial expressions to detect if the user is concerned about price. Based on this result, the emotion engine strengthens price-focused proposals. The generative AI summarizes the features of the listed products, creates a presentation document, and displays it to the user via their device. The user then reviews the document, selects the product they like, and purchases it from the online store.

[0326] Examples of prompts for the generation AI include:

[0327] "This user is considering replacing their refrigerator. The information for their current refrigerator is as follows: model number XYZ123, manufacturer A, manufactured in January 2015. The associated price information is new purchase price of 100,000 yen, used purchase price of 20,000 yen, and lowest sale price of 80,000 yen. The user is concerned about the price. Please generate the optimal replacement proposal based on this information."

[0328] Such a system would allow users to receive optimal product recommendations based on their preferences and emotional state, making the replacement purchase process more efficient and satisfying.

[0329] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0330] Step 1:

[0331] A user takes a picture of a home appliance with their smartphone and uploads it through a dedicated application. The user's input is the image of the appliance, and the output is that the image is sent to the server.

[0332] Step 2:

[0333] The server sends the received images to an image analysis module, which extracts information such as the model number, manufacturer, and manufacturing date. The data input is an image of the home appliance, and the output is the extracted product information (model number, manufacturer, and manufacturing date). TensorFlow and OpenCV are used for processing.

[0334] Step 3:

[0335] The server uses the extracted product information to collect price information from related sources (price comparison sites and online stores). The input information is product information, and the output is collected price information (new purchase price, used purchase price, lowest sale price). BeautifulSoup and Selenium are used to collect the data.

[0336] Step 4:

[0337] The server captures the user's facial expressions and voice to analyze the desired criteria and emotional state entered by the user. The input is the user's facial expression images and voice data, and the output is the analyzed user's emotional state. For emotion analysis, Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API are used.

[0338] Step 5:

[0339] The server uses an evaluation algorithm to list the most suitable new products based on the collected price information and the user's emotional state. The input is price information and the results of the emotional analysis, and the output is a list of the most suitable new products. The evaluation algorithm takes into account the user's desired criteria and emotional state, and ranks the products by assigning them scores.

[0340] Step 6:

[0341] The server uses a generative AI model (e.g., OpenAI's GPT-4) to summarize the features of the listed products and create presentation materials. The input is the list of optimal new products and the user's emotional state, and the output is the presentation materials. Prompts are provided to the generative AI model to generate appropriate materials.

[0342] Step 7:

[0343] The server sends the created presentation materials to the user's terminal and displays them through the application. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can review the materials and select the suggested products.

[0344] Step 8:

[0345] The user selects a product to purchase from the suggested products and clicks on a link to the online store. The input is the purchase link included in the presentation materials, and the output is the purchase page of the online store. The user can then complete the purchase process.

[0346] 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.

[0347] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0348] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0349] [Second embodiment]

[0350] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0351] 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.

[0352] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0353] 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.

[0354] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0355] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0356] 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.

[0357] 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.

[0358] 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 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.

[0359] 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.

[0360] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0361] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0362] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. Specifically, users upload photos of the home appliances, and the system analyzes the images on a server, collects necessary information, and proposes optimal new products. This system is configured as follows:

[0363] User operations

[0364] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, television, etc.), and the photo is uploaded to the system via a dedicated application on a smartphone or PC.

[0365] Image analysis on the server

[0366] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[0367] Collection of information

[0368] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[0369] Information collection and evaluation

[0370] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0371] Generate a presentation

[0372] The generative AI summarizes the features of the listed products and creates a presentation for the user, which includes proposals based on the user's desired criteria.

[0373] Provision to users

[0374] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[0375] Specific examples

[0376] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." The server then uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products (e.g., "Company A's model that prioritizes price" and "Company B's model that prioritizes performance"). The AI ​​then summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user reviews the materials, selects the product they like, and purchases it from the online store.

[0377] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[0378] The processing flow will be explained below.

[0379] Step 1:

[0380] Users take a photo of the home appliances they are using at home using a dedicated application on their smartphone or PC, and then press the upload button on the application.

[0381] Step 2:

[0382] The device sends the photo taken by the user from the application to the server, which includes the photo data as well as basic information about the user (such as the user ID and location information).

[0383] Step 3:

[0384] The server receives the photo data sent from the terminal and transfers it to the image analysis module.

[0385] Step 4:

[0386] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[0387] Step 5:

[0388] Based on the analyzed information, the server collects price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[0389] Step 6:

[0390] The server collects the following information:

[0391] New purchase price

[0392] Current second-hand purchase price

[0393] Lowest price on sale

[0394] Step 7:

[0395] The server stores the collected information in a database.

[0396] Step 8:

[0397] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0398] Step 9:

[0399] The server's generation AI summarizes the features of each product in the optimal new product list and creates presentation materials for users.

[0400] Step 10:

[0401] The server transmits the created presentation materials to the terminal.

[0402] Step 11:

[0403] The terminal receives the presentation materials sent from the server and displays them to the user.

[0404] Step 12:

[0405] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[0406] The above is a specific flow of program processing in the system of the present invention. The operations performed in each step allow the user to easily find and purchase the most suitable home appliance.

[0407] Example 1

[0408] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0409] Today's consumers are surrounded by a wide variety of home appliances, and are required to efficiently collect and evaluate vast amounts of information when replacing or purchasing new products. However, manually researching detailed information such as product model numbers and manufacturing dates is extremely time-consuming, and when price information is also collected and compared, it requires a considerable amount of time and effort. This makes it difficult for consumers to select the optimal home appliance.

[0410] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0411] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date information from the images of the home appliances on the server, means for collecting price data from related websites based on the analyzed data, means for summarizing the listed product characteristics using a generative AI model and making recommendations, and means for storing the collected data in a database. This enables users to efficiently replace their home appliances and easily select and purchase the optimal new products.

[0412] "Users" refer to consumers who use the system to upload images of home appliances and receive new product suggestions.

[0413] "Server" refers to the central processing unit that receives and analyzes data sent by users, collects and evaluates information, and generates and sends optimal new product proposals.

[0414] "Home appliances" refers to electronic devices used in the home, such as refrigerators, washing machines, and televisions.

[0415] "Images" refers to photographs or pictures of home appliances.

[0416] "Model number" refers to the product identification number assigned by the manufacturer of a home appliance.

[0417] "Manufacturing company" refers to a corporation that manufactures and sells home appliances.

[0418] "Manufacturing date information" refers to data indicating the date and year when a home appliance was manufactured.

[0419] "Related websites" refers to internet pages that provide prices and information about home appliances, such as price comparison sites, online stores, and review sites.

[0420] "Price data" refers to price information about home appliances, such as the price when purchased new, the price at which a used item is purchased, and the lowest sale price.

[0421] A "generative AI model" refers to an artificial intelligence computational model that summarizes product characteristics based on large amounts of data and makes suggestions to users.

[0422] "Evaluation algorithm" refers to a calculation method for evaluating and selecting optimal new products based on collected data.

[0423] "Database" refers to a digital storage system for systematically storing and managing collected information.

[0424] "Means" refers to a method or apparatus for achieving a particular function.

[0425] "Suggestion" refers to the act of selecting the most suitable home appliance for the user and presenting the options.

[0426] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes optimal new products.

[0427] First, a user takes a picture of the home appliances they use at home using a smartphone or digital camera. This image is then uploaded to the system using a dedicated application on their smartphone or PC. The dedicated application has an upload function that allows the user to send the image to the server.

[0428] The server then receives the image sent by the user and passes it to an image analysis module, which uses tools such as Google Cloud Vision or Azure Computer Vision. This module uses a generative AI model (such as OpenAI's CLIP model or YOLOv5) to accurately extract information such as the appliance's model number, manufacturer, and manufacturing date from the image.

[0429] Based on the analyzed information, the server collects price data from related websites. Web scraping technologies (such as BeautifulSoup or Scrapy) are used to obtain information such as "new purchase price," "current used buy price," and "lowest sale price" from price comparison sites and online stores (e.g., price comparison sites and major online stores). The collected data is then stored in a database (e.g., MySQL or PostgreSQL) on the server.

[0430] The stored data is then fed into an evaluation algorithm (e.g., Linear Regression or Decision Trees) to generate a list of optimal new products based on criteria such as "price-first," "performance-first," and "balanced." A generative AI model (e.g., OpenAI's GPT-4) is used to summarize the features of each product from this list and create a presentation for users.

[0431] Finally, the server sends the generated presentation materials to the user's device, where the user can review the materials through a dedicated application, select the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase process at the online store.

[0432] As a concrete example, consider a user considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads the photo through a dedicated application. The server receives the photo and uses an image analysis module to extract information such as "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products based on the user's desired criteria (e.g., "price-first" or "performance-first"). The generation AI summarizes the features of the products on the list and creates presentation materials. Finally, the materials are sent to the user's device, where they can be viewed using a dedicated application. The user selects from the suggested products and clicks the purchase link to purchase them from the online store.

[0433] An example prompt might be, "I'm considering replacing my refrigerator, so I uploaded a photo of my current one. Can you suggest a suitable new product?"

[0434] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[0435] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0436] Step 1:

[0437] Users take pictures of home appliances and upload them to the system through a dedicated application.

[0438] Input: Image of a home appliance

[0439] Specific operation: A user takes pictures of the home appliances they use at home using a smartphone or digital camera. Then, they use a dedicated application to upload the pictures to the system. The application has an "Upload image" button, which the user clicks.

[0440] Step 2:

[0441] The server receives the uploaded images and passes them to the analysis module.

[0442] Input: User-submitted images of home appliances

[0443] Output: Passing image data to the analysis module

[0444] Specific operation: The server receives images uploaded by users via HTTP requests and passes the image data to an analysis module, which uses cloud services (e.g., Google Cloud Vision or Azure Computer Vision).

[0445] Step 3:

[0446] The server uses an image analysis module to extract model number, manufacturer, and manufacturing date information.

[0447] Input: Image data

[0448] Output: Extracted model number, manufacturer, and manufacturing date information

[0449] How it works: The server calls the image analysis module and uses an AI model (e.g., YOLOv5) to analyze the text and features in the image. The extracted data (model number, manufacturer, and manufacturing date information) is used for further processing.

[0450] Step 4:

[0451] The server collects price data from relevant websites based on the parsed information.

[0452] Input: Extracted model number, manufacturer, manufacturing date information

[0453] Output: Collected price data

[0454] Specific operation: The server uses web scraping technology (e.g., BeautifulSoup or Scrapy) to obtain price data such as "new purchase price," "used purchase price," and "lowest sale price" from price comparison sites and online stores.

[0455] Step 5:

[0456] The server stores the collected price data in a database.

[0457] Input: Collected price data

[0458] Output: Price data stored in a database

[0459] What it does: The server uses a database connectivity library (e.g., SQLAlchemy) to store the collected price data in a database (e.g., MySQL, PostgreSQL), making it available for subsequent use in the evaluation algorithm.

[0460] Step 6:

[0461] The server uses a rating algorithm to generate a list of optimal new products.

[0462] Input: Price data stored in a database

[0463] Output: Best New Product List

[0464] Specific operation: The server executes an evaluation algorithm (e.g., Linear Regression, Decision Trees) and analyzes the results based on the user's desired criteria (e.g., "price-oriented," "performance-oriented," "balanced," etc.). It generates a list of optimal new products and proceeds to the next step.

[0465] Step 7:

[0466] The server uses a generative AI model to summarize the features of the listed products and create presentation materials.

[0467] Input: Best New Product List

[0468] Output: Presentation materials

[0469] Specific operation: Using a generative AI model (e.g., GPT-4), the features of each product in the new product list are summarized. The summarized content is compiled into a presentation document for users and created as a proposal document.

[0470] Step 8:

[0471] The server transmits the generated presentation materials to the terminal.

[0472] Input: Presentation materials

[0473] Output: Proposal materials displayed on the user's device

[0474] Specific operation: The server sends the generated presentation materials to the user's device, where the user can view them through a dedicated application.

[0475] Step 9:

[0476] The user selects from the suggested products and clicks the purchase link to make a purchase at the online store.

[0477] Input: Product information selected by the user

[0478] Output: Purchase processing on the online store

[0479] Specific operation: The user checks the suggested products in the dedicated application and clicks the purchase link for the desired product, which redirects them to the purchase page of the online store and allows them to proceed with the purchase.

[0480] This allows users to efficiently and hassle-freely select and purchase the most suitable home appliances.

[0481] (Application example 1)

[0482] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0483] When modern consumers consider replacing their home appliances, the time and effort required is a major problem. The process of manually collecting information such as product model numbers, manufacturers, and manufacturing dates, and then selecting the optimal new product based on that information, is particularly tedious. Furthermore, it is necessary to individually collect price information from numerous online sites, compare and evaluate options, which requires advanced knowledge and time. This presents a challenge for consumers, making it difficult to select the optimal new product without hassle and purchase it efficiently.

[0484] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0485] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date from the images of the home appliances on the server, means for collecting price information from related online sites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products using a generative AI model and presenting them to the user, and means for providing purchase links for the products selected by the user. This allows consumers to easily select and purchase the optimal home appliances.

[0486] "Users" are consumers who use the system to consider replacing their home appliances.

[0487] "Images of Home Appliances" are photographs of home appliances taken by users using smartphones or other image capture devices and uploaded to the system.

[0488] "Server" means a computer system on a network that performs functions such as image analysis, information gathering, evaluation algorithms, and presentation generation using generative AI models.

[0489] A "model number" is an identification number that indicates a specific model of a home appliance.

[0490] "Manufacturer" refers to the company that produced the home appliance.

[0491] "Date of manufacture" is the date or month when the home appliance was manufactured.

[0492] "Online site" refers to a website that provides product price information, including price comparison sites and mail order sites.

[0493] "Price information" refers to information about the price of home appliances, such as the purchase price, second-hand purchase price, and sale price.

[0494] An "evaluation algorithm" is a calculation method for selecting the best new product for a user based on collected pricing information and other data.

[0495] A "generative AI model" is an artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[0496] A "presentation" is a document generated by a generative AI model and presented to the user about the features of a new product and the reasons for its proposal.

[0497] A "purchase link" is a link that allows a user to purchase the selected product directly from the online store.

[0498] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes new products.

[0499] System Configuration

[0500] Hardware

[0501] User terminal: A device such as a smartphone or PC that allows users to take pictures of home appliances and upload them to the system.

[0502] Server: A computer system on a network that performs image analysis, information collection, and presentation generation using generative AI models.

[0503] software

[0504] Image analysis module: Software for analyzing the model number, manufacturer, and manufacturing date from images of home appliances.

[0505] Information collection module: Software to collect price information from related online sites based on the analyzed information. It uses scraping technology.

[0506] Evaluation algorithm: A calculation method for selecting the most suitable new product based on collected information.

[0507] Generative AI model: An artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[0508] Processing steps

[0509] 1. Image upload: Users take pictures of home appliances using their smartphones or PCs and upload them to the system via a dedicated application.

[0510] 2. Image analysis: The server processes the received images using an image analysis module to extract information such as the appliance model number, manufacturer, and manufacturing date.

[0511] 3. Information collection: Based on the extracted information, the server collects product price information (new purchase price, used purchase price, lowest sale price, etc.) from related online sites.

[0512] 4. Selection of optimal products: The server processes the collected price information with an evaluation algorithm to generate a list of optimal products based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[0513] 5. Presentation generation: The generative AI model summarizes the features of the listed products and creates a presentation to provide to the user.

[0514] 6. User presentation and purchase link provision: Finally, the presentation materials are sent from the server to the user's terminal, and the user can review the materials and proceed with purchasing the selected product at the online store via the purchase link.

[0515] Specific examples

[0516] When a user is considering replacing their refrigerator, they take a picture of their home refrigerator using their smartphone and upload it through a dedicated application. The server uses an image analysis module to extract the model number, manufacturer, and manufacturing date, and uses an information collection module to collect information from price comparison sites and online shopping sites, such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen." An evaluation algorithm then generates a list of optimal new products, such as "Company A's model that prioritizes price" and "Company B's model that prioritizes performance." The generative AI model summarizes the product features based on the list, creates presentation materials, and provides them to the user.

[0517] Prompt Sentence Examples

[0518] "Based on the model number, manufacturer, and production date extracted from the image, collect the best price information and generate the best new product proposals using an evaluation algorithm."

[0519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0520] Step 1:

[0521] Users take pictures of home appliances using their smartphones or PCs and upload them to the system through a dedicated application. The input is the image of the home appliance, and the output is the uploaded image data. The device then sends this image data to the server.

[0522] Step 2:

[0523] The server receives the uploaded images and uses an image analysis module to analyze the model number, manufacturer, and manufacturing date from the images. The input is the uploaded image data, and the output is the extracted product information (model number, manufacturer, manufacturing date). The server temporarily stores the analysis results in a database.

[0524] Step 3:

[0525] The server uses the parsed product information to use scraping technology to collect price information from related online sites. The input is product information (model number, manufacturer, manufacturing date), and the output is collected price information (new purchase price, used buyback price, lowest sale price). The server stores the collected price information in a database.

[0526] Step 4:

[0527] The server uses an evaluation algorithm to generate an optimal list of new products based on the collected price information. The input is the collected price information, and the output is a list of optimal new products. This list is also based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[0528] Step 5:

[0529] The server uses a generative AI model to summarize the product features of the optimal new product list and create a presentation. The input is the optimal new product list, and the output is the presentation. The generative AI model summarizes the product's benefits and features in a format that is easy for users to understand.

[0530] Step 6:

[0531] The server sends the generated presentation materials to the user's terminal and displays them to the user. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can check the proposed new products, select the most suitable product, and click the purchase link.

[0532] This allows users to easily select and purchase the most suitable home appliances.

[0533] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0534] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is configured as follows.

[0535] User operations

[0536] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, TV, etc.). This photo is then uploaded to the system via a dedicated application on a smartphone or PC. The application can also capture the user's voice commands and facial expressions at the same time.

[0537] Image analysis on the server

[0538] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[0539] Collection of information

[0540] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[0541] Information collection and evaluation

[0542] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0543] Emotion Engine Operation

[0544] The server then uses an emotion engine to analyze the user's voice, facial expressions, and past behavioral data. This allows the server to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time. The emotion engine then adjusts the content of the recommendations based on the analysis results. For example, if the user shows a relieved expression, the server will strengthen price-focused recommendations.

[0545] Generate a presentation

[0546] The generative AI summarizes the features of the listed products and creates a presentation that takes into account the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state.

[0547] Provision to users

[0548] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[0549] Specific examples

[0550] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[0551] At the same time, the emotion engine analyzes the user's voice commands and facial expressions to detect if the user is concerned about price. The emotion engine reflects this result and strengthens price-focused recommendations. The generative AI summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user then reviews the materials, selects the product they like, and makes a purchase from the online store.

[0552] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[0553] The processing flow will be explained below.

[0554] The present invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is specifically implemented as follows.

[0555] Step 1:

[0556] Users take photos of their home appliances using a dedicated application on their smartphone or PC, press the upload button on the application, and simultaneously provide their emotional state to the system by using voice commands or capturing facial expressions with a webcam.

[0557] Step 2:

[0558] The terminal transmits the photos taken by the user, voice data, and facial expression capture data from the application to the server.

[0559] Step 3:

[0560] The server receives the photo data sent from the device and transfers it to the image analysis module. It also transfers the voice data and facial expression capture data to the emotion engine at the same time.

[0561] Step 4:

[0562] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[0563] Step 5:

[0564] The server's emotion engine analyzes the user's voice data and facial expression capture data to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time.

[0565] Step 6:

[0566] The server uses the analyzed information (model number, manufacturer, manufacturing date) to collect price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[0567] Step 7:

[0568] The server collects the following information:

[0569] New purchase price

[0570] Current second-hand purchase price

[0571] Lowest price on sale

[0572] Step 8:

[0573] The server stores the collected information in a database.

[0574] Step 9:

[0575] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0576] Step 10:

[0577] The server's AI then summarizes the features of each product in the list of optimal new products and creates a presentation for the user. However, it also adjusts the proposals based on the user's emotional state. For example, if the user expresses anxiety, it emphasizes price-focused proposals that will reassure the user.

[0578] Step 11:

[0579] The server transmits the created presentation materials to the terminal.

[0580] Step 12:

[0581] The terminal receives the presentation materials sent from the server and displays them to the user.

[0582] Step 13:

[0583] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[0584] The above is the specific flow of program processing in the system of the present invention. The operations performed at each step allow users to easily find the best home appliances, and by utilizing the emotion engine, they can receive personalized suggestions and have a highly satisfying purchasing experience.

[0585] Example 2

[0586] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0587] Today's consumers must refer to a large number of options and information when choosing the best home appliance from a wide variety of products, which requires a lot of time and effort. Furthermore, systems that make blanket recommendations without recognizing emotions have the problem of making optimal product recommendations that meet the user's true needs. Furthermore, a mechanism for appropriately evaluating and organizing the collected information is needed.

[0588] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for taking and uploading a photo of the home appliance used by the user, means for capturing the user's voice commands and facial expressions simultaneously with the photo, means for extracting the model number, manufacturer, and manufacturing date from the photo of the home appliance on the server, means for collecting price information from related websites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and generating presentation materials based on the user's emotional state, and means for transmitting the generated presentation materials to the user's terminal and displaying them. This allows the user to easily select the optimal home appliance, and by using an emotion engine, personalized suggestions based on the user's emotional state are possible.

[0589] "Means for taking and uploading photos" refers to a system in which users take photos of home appliances using their smartphones or PCs and send them to a server via a dedicated application.

[0590] "Means for capturing voice commands and facial expressions" refers to a mechanism that simultaneously captures voice instructions and facial expression data and sends them to a server when a user uploads a photo of a home appliance.

[0591] "Means for extracting model numbers, manufacturers, and manufacturing dates from photographs of home appliances on a server" refers to a system that analyzes photographs uploaded to a server using a generative AI model, etc., to identify the model number, manufacturer, and manufacturing date of the home appliance.

[0592] "Means for collecting price information" refers to a system in which the server collects price data from related websites (price comparison sites, online stores, review sites, etc.) based on the analyzed information.

[0593] "Means for evaluating using an evaluation algorithm and listing optimal new products" refers to a system in which the server selects and lists optimal new products according to specific evaluation criteria (e.g., price-oriented, performance-oriented, balanced, etc.) based on collected price information and product data.

[0594] The "means for generating presentation materials" is a mechanism by which the server summarizes the features of the listed products and generates presentation materials that take into account the user's emotional state.

[0595] The "means for transmitting and displaying presentation materials" refers to a mechanism for transmitting the generated presentation materials to a user's terminal, allowing the user to view the materials on the terminal.

[0596] The "emotion engine" is a system that analyzes the user's voice, facial expressions, and past behavioral data to recognize the user's emotional state (e.g., anxiety, satisfaction, excitement, etc.) in real time and adjusts the content of suggestions.

[0597] The present invention is a system for hassle-freely proposing optimal new products to consumers who are considering replacing their home appliances. In particular, by combining an emotion engine that recognizes the user's emotions, more personalized proposals can be made. This system utilizes various hardware and software to flexibly respond to user needs. This section describes specific embodiments of the system.

[0598] First, the user uses a smartphone or PC to take a photo of the home appliance they are currently using (e.g., refrigerator, washing machine, television, etc.). The user then launches a dedicated application and uploads the photo to the system. This application has the function of simultaneously capturing the user's voice commands and facial expressions. This data is then sent in bulk to the server.

[0599] The server receives the uploaded photos and voice and facial expression data. The image analysis module for photo analysis uses a generative AI model, which enables highly accurate extraction of information such as the model number, manufacturer, and manufacturing date. For example, the analysis results may yield information such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015." The information extracted in this way is used in the next processing step.

[0600] Next, the server uses this analysis information to collect price information from related websites. This information is collected using scraping technology, and data such as "price when purchased new," "current used purchase price," and "lowest sale price" are collected. For example, "price when purchased new: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" may be collected.

[0601] This collected information is stored in a database on the server. The server uses an evaluation algorithm to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced." For example, if price is the most important criterion, information such as "XYZ200 model, price: 70,000 yen" will be listed.

[0602] Furthermore, the server is equipped with an emotion engine that can analyze the user's voice, facial expressions, and past behavioral data. This allows the system to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time and adjust the content of its recommendations accordingly. For example, if the user is concerned about price, the system will strengthen price-focused recommendations.

[0603] The generative AI summarizes the features of the listed products and creates a presentation that reflects the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state. For example, the presentation might be, "Affordable refrigerator XYZ200, features: freezer included, price: 70,000 yen."

[0604] Finally, the server sends the generated presentation materials to the user's device, where the user can review them on their smartphone or PC. The user can then select the most suitable product from the presented products, and once they have decided to purchase, they can click the instant purchase link to proceed with the purchase at the online store.

[0605] As a concrete example of this system, consider a user considering replacing their refrigerator. The user takes a photo of their refrigerator at home and uploads it to the server using a dedicated application. The server then uses an image analysis module to extract information such as the refrigerator's model number, manufacturer, and manufacturing date, and collects price information from related websites. At the same time, the emotion engine analyzes the user's voice commands and facial expressions to understand their emotional state. Finally, the generative AI creates a presentation summarizing the plan and presents it to the user.

[0606] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[0607] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] The user takes a photo of the home appliances in their home and uploads it using a dedicated application. The user takes a photo of the appliance using a smartphone or PC and imports the photo file into the application. At the same time, voice commands and facial expressions are also captured. This information is sent from the application to the server. The input is the photo of the appliance taken by the user, voice data, and facial expression data, and the output is data sent to the server.

[0610] Step 2:

[0611] The server inputs the photos received from the user into the image analysis module. This module uses a generative AI model to perform highly accurate image analysis. Specifically, it extracts information such as the home appliance model number, manufacturer, and manufacturing date from the photo. After analysis, data such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015" is obtained. The input is the uploaded photo, and the output is the analyzed product information.

[0612] Step 3:

[0613] The server collects price information from websites based on the analyzed product information. Using scraping technology, data such as "new purchase price," "current used purchase price," and "lowest sale price" are obtained from related sites (price comparison sites, online stores, reviews, etc.). For example, information such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" is collected. The input is product information, and the output is the collected price information.

[0614] Step 4:

[0615] The server stores the collected price information in a database, which provides a basis for subsequent processing and revaluation. The input is the collected price information, and the output is the data stored in the database.

[0616] Step 5:

[0617] The server uses an evaluation algorithm based on the stored information to generate an optimal list of new products. Evaluation criteria include "price-oriented," "performance-oriented," and "balanced." For example, a list such as "price-oriented refrigerator XYZ200, price: 70,000 yen" is generated. The input is existing data in the database, and the output is the listed product information.

[0618] Step 6:

[0619] The server activates an emotion engine and analyzes the user's voice commands and facial expression data. By analyzing past behavioral data as well, the user's emotional state is recognized. The content of suggestions is adjusted based on these results. For example, if the user is concerned about price, suggestions that emphasize price are strengthened. The input is voice data, facial expression data, and past behavioral data, and the output is the emotion analysis results.

[0620] Step 7:

[0621] The server uses generative AI to summarize the features of the listed products and create presentation materials that reflect the user's emotional state. The user's desired criteria are also taken into consideration. For example, a presentation might be generated that describes the "price-conscious refrigerator XYZ200, features: freezer included, price: 70,000 yen." The input is the listed product information and the results of the emotional analysis, and the output is the presentation materials.

[0622] Step 8:

[0623] The server sends the generated presentation materials to the user's device, where the user can view them on their smartphone or PC. The input is the presentation materials, and the output is the materials sent to the user's device.

[0624] Step 9:

[0625] The user reviews the provided presentation materials and selects the most suitable product. If they decide to purchase, they click the instant purchase link to complete the purchase process at the online store. The input is the provided presentation materials, and the output is the purchase process for the selected product.

[0626] As a result, users can easily select the most suitable home appliances and receive personalized suggestions from the emotion engine.

[0627] (Application example 2)

[0628] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0629] When considering replacing home appliances, modern consumers find it difficult to gather a large amount of information and select the most suitable product. Furthermore, conventional systems make uniform recommendations without considering the user's emotions or individual preferences, making it difficult to improve user satisfaction. Given this background, there is a growing need for a system that takes user emotions into account and makes more personalized product recommendations.

[0630] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload an image of a home appliance, means for analyzing the model number, manufacturer, and manufacturing date from the image of the home appliance on the server, means for collecting price information from related information sources based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and presenting them to the user, means for analyzing the user's facial expressions and voice, and means for adjusting the content of proposals based on the results of the emotion analysis. This makes it possible to select and propose optimal products based on the user's emotions and desired criteria, thereby improving user satisfaction.

[0631] "Means for users to upload images of home appliances" refers to an interface or function that allows users to send images of home appliances to the system using a smartphone or other device.

[0632] "Means for analyzing the model number, manufacturer, and manufacturing date from images of home appliances on a server" refers to a function that uses image analysis technology on a server to automatically extract information such as the model number, manufacturer, and manufacturing date from images of home appliances.

[0633] "Means for collecting price information from related sources based on the analyzed information" refers to a technology for using the extracted product information to collect related price information from external sources such as price comparison sites and online stores.

[0634] "Means for evaluating collected information using an evaluation algorithm and listing optimal new products" refers to a function that uses an evaluation algorithm to select and list optimal new products based on collected price information and product information.

[0635] The "means for summarizing the features of the listed products and presenting them to the user" is a function for aggregating the features of the listed products, summarizing the main points, and presenting them to the user.

[0636] "Means for analyzing the user's facial expressions and voice" refers to technology for analyzing the user's facial expressions and voice and recognizing their emotional state.

[0637] The "means for adjusting the content of proposals based on the results of emotion analysis" is a function that takes into account the emotional state of the user obtained from the emotion analysis and adjusts the content of product proposals accordingly.

[0638] This invention is a system that assists users in the process of efficiently replacing their home appliances. Users upload images of their home appliances to an application using a smartphone or other device. The images are then sent to a server, where image analysis begins.

[0639] On the server, image analysis technology is used to automatically extract information such as model number, manufacturer, and manufacturing date from the uploaded image. This process uses image analysis software such as TensorFlow and OpenCV. The extracted information is then used by the server to collect price information from related sources. To collect price information, scraping technologies such as BeautifulSoup and Selenium are used.

[0640] The server also analyzes the user's facial expressions and voice in real time to recognize their emotional state. This emotion analysis uses Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API. Based on the analysis results, the server adjusts the proposal content and makes the optimal product recommendations for the user. The proposal content is displayed on the user's device as presentation materials generated using generative AI models such as OpenAI's GPT-4.

[0641] For example, consider the case where a user is considering replacing their refrigerator. The user takes a picture of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the image and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, based on this information, the server collects information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[0642] At the same time, the server uses an emotion engine to analyze the user's voice commands and facial expressions to detect if the user is concerned about price. Based on this result, the emotion engine strengthens price-focused proposals. The generative AI summarizes the features of the listed products, creates a presentation document, and displays it to the user via their device. The user then reviews the document, selects the product they like, and purchases it from the online store.

[0643] Examples of prompts for the generation AI include:

[0644] "This user is considering replacing their refrigerator. The information for their current refrigerator is as follows: model number XYZ123, manufacturer A, manufactured in January 2015. The associated price information is new purchase price of 100,000 yen, used purchase price of 20,000 yen, and lowest sale price of 80,000 yen. The user is concerned about the price. Please generate the optimal replacement proposal based on this information."

[0645] Such a system would allow users to receive optimal product recommendations based on their preferences and emotional state, making the replacement purchase process more efficient and satisfying.

[0646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0647] Step 1:

[0648] A user takes a picture of a home appliance with their smartphone and uploads it through a dedicated application. The user's input is the image of the appliance, and the output is that the image is sent to the server.

[0649] Step 2:

[0650] The server sends the received images to an image analysis module, which extracts information such as the model number, manufacturer, and manufacturing date. The data input is an image of the home appliance, and the output is the extracted product information (model number, manufacturer, and manufacturing date). TensorFlow and OpenCV are used for processing.

[0651] Step 3:

[0652] The server uses the extracted product information to collect price information from related sources (price comparison sites and online stores). The input information is product information, and the output is collected price information (new purchase price, used purchase price, lowest sale price). BeautifulSoup and Selenium are used to collect the data.

[0653] Step 4:

[0654] The server captures the user's facial expressions and voice to analyze the desired criteria and emotional state entered by the user. The input is the user's facial expression images and voice data, and the output is the analyzed user's emotional state. For emotion analysis, Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API are used.

[0655] Step 5:

[0656] The server uses an evaluation algorithm to list the most suitable new products based on the collected price information and the user's emotional state. The input is price information and the results of the emotional analysis, and the output is a list of the most suitable new products. The evaluation algorithm takes into account the user's desired criteria and emotional state, and ranks the products by assigning them scores.

[0657] Step 6:

[0658] The server uses a generative AI model (e.g., OpenAI's GPT-4) to summarize the features of the listed products and create presentation materials. The input is the list of optimal new products and the user's emotional state, and the output is the presentation materials. Prompts are provided to the generative AI model to generate appropriate materials.

[0659] Step 7:

[0660] The server sends the created presentation materials to the user's terminal and displays them through the application. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can review the materials and select the suggested products.

[0661] Step 8:

[0662] The user selects a product to purchase from the suggested products and clicks on a link to the online store. The input is the purchase link included in the presentation materials, and the output is the purchase page of the online store. The user can then complete the purchase process.

[0663] 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.

[0664] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0665] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0666] [Third embodiment]

[0667] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0668] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0669] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0670] 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.

[0671] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0672] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0673] 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.

[0674] 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.

[0675] 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 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.

[0676] 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.

[0677] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0678] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0679] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. Specifically, users upload photos of the home appliances, and the system analyzes the images on a server, collects necessary information, and proposes optimal new products. This system is configured as follows:

[0680] User operations

[0681] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, television, etc.), and the photo is uploaded to the system via a dedicated application on a smartphone or PC.

[0682] Image analysis on the server

[0683] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[0684] Collection of information

[0685] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[0686] Information collection and evaluation

[0687] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0688] Generate a presentation

[0689] The generative AI summarizes the features of the listed products and creates a presentation for the user, which includes proposals based on the user's desired criteria.

[0690] Provision to users

[0691] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[0692] Specific examples

[0693] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." The server then uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products (e.g., "Company A's model that prioritizes price" and "Company B's model that prioritizes performance"). The AI ​​then summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user reviews the materials, selects the product they like, and purchases it from the online store.

[0694] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[0695] The processing flow will be explained below.

[0696] Step 1:

[0697] Users take a photo of the home appliances they are using at home using a dedicated application on their smartphone or PC, and then press the upload button on the application.

[0698] Step 2:

[0699] The device sends the photo taken by the user from the application to the server, which includes the photo data as well as basic information about the user (such as the user ID and location information).

[0700] Step 3:

[0701] The server receives the photo data sent from the terminal and transfers it to the image analysis module.

[0702] Step 4:

[0703] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[0704] Step 5:

[0705] Based on the analyzed information, the server collects price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[0706] Step 6:

[0707] The server collects the following information:

[0708] New purchase price

[0709] Current second-hand purchase price

[0710] Lowest price on sale

[0711] Step 7:

[0712] The server stores the collected information in a database.

[0713] Step 8:

[0714] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0715] Step 9:

[0716] The server's generation AI summarizes the features of each product in the optimal new product list and creates presentation materials for users.

[0717] Step 10:

[0718] The server transmits the created presentation materials to the terminal.

[0719] Step 11:

[0720] The terminal receives the presentation materials sent from the server and displays them to the user.

[0721] Step 12:

[0722] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[0723] The above is a specific flow of program processing in the system of the present invention. The operations performed in each step allow the user to easily find and purchase the most suitable home appliance.

[0724] Example 1

[0725] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0726] Today's consumers are surrounded by a wide variety of home appliances, and are required to efficiently collect and evaluate vast amounts of information when replacing or purchasing new products. However, manually researching detailed information such as product model numbers and manufacturing dates is extremely time-consuming, and when price information is also collected and compared, it requires a considerable amount of time and effort. This makes it difficult for consumers to select the optimal home appliance.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0728] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date information from the images of the home appliances on the server, means for collecting price data from related websites based on the analyzed data, means for summarizing the listed product characteristics using a generative AI model and making recommendations, and means for storing the collected data in a database. This enables users to efficiently replace their home appliances and easily select and purchase the optimal new products.

[0729] "Users" refer to consumers who use the system to upload images of home appliances and receive new product suggestions.

[0730] "Server" refers to the central processing unit that receives and analyzes data sent by users, collects and evaluates information, and generates and sends optimal new product proposals.

[0731] "Home appliances" refers to electronic devices used in the home, such as refrigerators, washing machines, and televisions.

[0732] "Images" refers to photographs or pictures of home appliances.

[0733] "Model number" refers to the product identification number assigned by the manufacturer of a home appliance.

[0734] "Manufacturing company" refers to a corporation that manufactures and sells home appliances.

[0735] "Manufacturing date information" refers to data indicating the date and year when a home appliance was manufactured.

[0736] "Related websites" refers to internet pages that provide prices and information about home appliances, such as price comparison sites, online stores, and review sites.

[0737] "Price data" refers to price information about home appliances, such as the price when purchased new, the price at which a used item is purchased, and the lowest sale price.

[0738] A "generative AI model" refers to an artificial intelligence computational model that summarizes product characteristics based on large amounts of data and makes suggestions to users.

[0739] "Evaluation algorithm" refers to a calculation method for evaluating and selecting optimal new products based on collected data.

[0740] "Database" refers to a digital storage system for systematically storing and managing collected information.

[0741] "Means" refers to a method or apparatus for achieving a particular function.

[0742] "Suggestion" refers to the act of selecting the most suitable home appliance for the user and presenting the options.

[0743] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes optimal new products.

[0744] First, a user takes a picture of the home appliances they use at home using a smartphone or digital camera. This image is then uploaded to the system using a dedicated application on their smartphone or PC. The dedicated application has an upload function that allows the user to send the image to the server.

[0745] The server then receives the image sent by the user and passes it to an image analysis module, which uses tools such as Google Cloud Vision or Azure Computer Vision. This module uses a generative AI model (such as OpenAI's CLIP model or YOLOv5) to accurately extract information such as the appliance's model number, manufacturer, and manufacturing date from the image.

[0746] Based on the analyzed information, the server collects price data from related websites. Web scraping technologies (such as BeautifulSoup or Scrapy) are used to obtain information such as "new purchase price," "current used buy price," and "lowest sale price" from price comparison sites and online stores (e.g., price comparison sites and major online stores). The collected data is then stored in a database (e.g., MySQL or PostgreSQL) on the server.

[0747] The stored data is then fed into an evaluation algorithm (e.g., Linear Regression or Decision Trees) to generate a list of optimal new products based on criteria such as "price-first," "performance-first," and "balanced." A generative AI model (e.g., OpenAI's GPT-4) is used to summarize the features of each product from this list and create a presentation for users.

[0748] Finally, the server sends the generated presentation materials to the user's device, where the user can review the materials through a dedicated application, select the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase process at the online store.

[0749] As a concrete example, consider a user considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads the photo through a dedicated application. The server receives the photo and uses an image analysis module to extract information such as "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products based on the user's desired criteria (e.g., "price-first" or "performance-first"). The generation AI summarizes the features of the products on the list and creates presentation materials. Finally, the materials are sent to the user's device, where they can be viewed using a dedicated application. The user selects from the suggested products and clicks the purchase link to purchase them from the online store.

[0750] An example prompt might be, "I'm considering replacing my refrigerator, so I uploaded a photo of my current one. Can you suggest a suitable new product?"

[0751] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[0752] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] Users take pictures of home appliances and upload them to the system through a dedicated application.

[0755] Input: Image of a home appliance

[0756] Specific operation: A user takes pictures of the home appliances they use at home using a smartphone or digital camera. Then, they use a dedicated application to upload the pictures to the system. The application has an "Upload image" button, which the user clicks.

[0757] Step 2:

[0758] The server receives the uploaded images and passes them to the analysis module.

[0759] Input: User-submitted images of home appliances

[0760] Output: Passing image data to the analysis module

[0761] Specific operation: The server receives images uploaded by users via HTTP requests and passes the image data to an analysis module, which uses cloud services (e.g., Google Cloud Vision or Azure Computer Vision).

[0762] Step 3:

[0763] The server uses an image analysis module to extract model number, manufacturer, and manufacturing date information.

[0764] Input: Image data

[0765] Output: Extracted model number, manufacturer, and manufacturing date information

[0766] How it works: The server calls the image analysis module and uses an AI model (e.g., YOLOv5) to analyze the text and features in the image. The extracted data (model number, manufacturer, and manufacturing date information) is used for further processing.

[0767] Step 4:

[0768] The server collects price data from relevant websites based on the parsed information.

[0769] Input: Extracted model number, manufacturer, manufacturing date information

[0770] Output: Collected price data

[0771] Specific operation: The server uses web scraping technology (e.g., BeautifulSoup or Scrapy) to obtain price data such as "new purchase price," "used purchase price," and "lowest sale price" from price comparison sites and online stores.

[0772] Step 5:

[0773] The server stores the collected price data in a database.

[0774] Input: Collected price data

[0775] Output: Price data stored in a database

[0776] What it does: The server uses a database connectivity library (e.g., SQLAlchemy) to store the collected price data in a database (e.g., MySQL, PostgreSQL), making it available for subsequent use in the evaluation algorithm.

[0777] Step 6:

[0778] The server uses a rating algorithm to generate a list of optimal new products.

[0779] Input: Price data stored in a database

[0780] Output: Best New Product List

[0781] Specific operation: The server executes an evaluation algorithm (e.g., Linear Regression, Decision Trees) and analyzes the results based on the user's desired criteria (e.g., "price-oriented," "performance-oriented," "balanced," etc.). It generates a list of optimal new products and proceeds to the next step.

[0782] Step 7:

[0783] The server uses a generative AI model to summarize the features of the listed products and create presentation materials.

[0784] Input: Best New Product List

[0785] Output: Presentation materials

[0786] Specific operation: Using a generative AI model (e.g., GPT-4), the features of each product in the new product list are summarized. The summarized content is compiled into a presentation document for users and created as a proposal document.

[0787] Step 8:

[0788] The server transmits the generated presentation materials to the terminal.

[0789] Input: Presentation materials

[0790] Output: Proposal materials displayed on the user's device

[0791] Specific operation: The server sends the generated presentation materials to the user's device, where the user can view them through a dedicated application.

[0792] Step 9:

[0793] The user selects from the suggested products and clicks the purchase link to make a purchase at the online store.

[0794] Input: Product information selected by the user

[0795] Output: Purchase processing on the online store

[0796] Specific operation: The user checks the suggested products in the dedicated application and clicks the purchase link for the desired product, which redirects them to the purchase page of the online store and allows them to proceed with the purchase.

[0797] This allows users to efficiently and hassle-freely select and purchase the most suitable home appliances.

[0798] (Application example 1)

[0799] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0800] When modern consumers consider replacing their home appliances, the time and effort required is a major problem. The process of manually collecting information such as product model numbers, manufacturers, and manufacturing dates, and then selecting the optimal new product based on that information, is particularly tedious. Furthermore, it is necessary to individually collect price information from numerous online sites, compare and evaluate options, which requires advanced knowledge and time. This presents a challenge for consumers, making it difficult to select the optimal new product without hassle and purchase it efficiently.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0802] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date from the images of the home appliances on the server, means for collecting price information from related online sites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products using a generative AI model and presenting them to the user, and means for providing purchase links for the products selected by the user. This allows consumers to easily select and purchase the optimal home appliances.

[0803] "Users" are consumers who use the system to consider replacing their home appliances.

[0804] "Images of Home Appliances" are photographs of home appliances taken by users using smartphones or other image capture devices and uploaded to the system.

[0805] "Server" means a computer system on a network that performs functions such as image analysis, information gathering, evaluation algorithms, and presentation generation using generative AI models.

[0806] A "model number" is an identification number that indicates a specific model of a home appliance.

[0807] "Manufacturer" refers to the company that produced the home appliance.

[0808] "Date of manufacture" is the date or month when the home appliance was manufactured.

[0809] "Online site" refers to a website that provides product price information, including price comparison sites and mail order sites.

[0810] "Price information" refers to information about the price of home appliances, such as the purchase price, second-hand purchase price, and sale price.

[0811] An "evaluation algorithm" is a calculation method for selecting the best new product for a user based on collected pricing information and other data.

[0812] A "generative AI model" is an artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[0813] A "presentation" is a document generated by a generative AI model and presented to the user about the features of a new product and the reasons for its proposal.

[0814] A "purchase link" is a link that allows a user to purchase the selected product directly from the online store.

[0815] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes new products.

[0816] System Configuration

[0817] Hardware

[0818] User terminal: A device such as a smartphone or PC that allows users to take pictures of home appliances and upload them to the system.

[0819] Server: A computer system on a network that performs image analysis, information collection, and presentation generation using generative AI models.

[0820] software

[0821] Image analysis module: Software for analyzing the model number, manufacturer, and manufacturing date from images of home appliances.

[0822] Information collection module: Software to collect price information from related online sites based on the analyzed information. It uses scraping technology.

[0823] Evaluation algorithm: A calculation method for selecting the most suitable new product based on collected information.

[0824] Generative AI model: An artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[0825] Processing steps

[0826] 1. Image upload: Users take pictures of home appliances using their smartphones or PCs and upload them to the system via a dedicated application.

[0827] 2. Image analysis: The server processes the received images using an image analysis module to extract information such as the appliance model number, manufacturer, and manufacturing date.

[0828] 3. Information collection: Based on the extracted information, the server collects product price information (new purchase price, used purchase price, lowest sale price, etc.) from related online sites.

[0829] 4. Selection of optimal products: The server processes the collected price information with an evaluation algorithm to generate a list of optimal products based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[0830] 5. Presentation generation: The generative AI model summarizes the features of the listed products and creates a presentation to provide to the user.

[0831] 6. User presentation and purchase link provision: Finally, the presentation materials are sent from the server to the user's terminal, and the user can review the materials and proceed with purchasing the selected product at the online store via the purchase link.

[0832] Specific examples

[0833] When a user is considering replacing their refrigerator, they take a picture of their home refrigerator using their smartphone and upload it through a dedicated application. The server uses an image analysis module to extract the model number, manufacturer, and manufacturing date, and uses an information collection module to collect information from price comparison sites and online shopping sites, such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen." An evaluation algorithm then generates a list of optimal new products, such as "Company A's model that prioritizes price" and "Company B's model that prioritizes performance." The generative AI model summarizes the product features based on the list, creates presentation materials, and provides them to the user.

[0834] Prompt Sentence Examples

[0835] "Based on the model number, manufacturer, and production date extracted from the image, collect the best price information and generate the best new product proposals using an evaluation algorithm."

[0836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0837] Step 1:

[0838] Users take pictures of home appliances using their smartphones or PCs and upload them to the system through a dedicated application. The input is the image of the home appliance, and the output is the uploaded image data. The device then sends this image data to the server.

[0839] Step 2:

[0840] The server receives the uploaded images and uses an image analysis module to analyze the model number, manufacturer, and manufacturing date from the images. The input is the uploaded image data, and the output is the extracted product information (model number, manufacturer, manufacturing date). The server temporarily stores the analysis results in a database.

[0841] Step 3:

[0842] The server uses the parsed product information to use scraping technology to collect price information from related online sites. The input is product information (model number, manufacturer, manufacturing date), and the output is collected price information (new purchase price, used buyback price, lowest sale price). The server stores the collected price information in a database.

[0843] Step 4:

[0844] The server uses an evaluation algorithm to generate an optimal list of new products based on the collected price information. The input is the collected price information, and the output is a list of optimal new products. This list is also based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[0845] Step 5:

[0846] The server uses a generative AI model to summarize the product features of the optimal new product list and create a presentation. The input is the optimal new product list, and the output is the presentation. The generative AI model summarizes the product's benefits and features in a format that is easy for users to understand.

[0847] Step 6:

[0848] The server sends the generated presentation materials to the user's terminal and displays them to the user. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can check the proposed new products, select the most suitable product, and click the purchase link.

[0849] This allows users to easily select and purchase the most suitable home appliances.

[0850] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0851] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is configured as follows.

[0852] User operations

[0853] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, TV, etc.). This photo is then uploaded to the system via a dedicated application on a smartphone or PC. The application can also capture the user's voice commands and facial expressions at the same time.

[0854] Image analysis on the server

[0855] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[0856] Collection of information

[0857] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[0858] Information collection and evaluation

[0859] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0860] Emotion Engine Operation

[0861] The server then uses an emotion engine to analyze the user's voice, facial expressions, and past behavioral data. This allows the server to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time. The emotion engine then adjusts the content of the recommendations based on the analysis results. For example, if the user shows a relieved expression, the server will strengthen price-focused recommendations.

[0862] Generate a presentation

[0863] The generative AI summarizes the features of the listed products and creates a presentation that takes into account the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state.

[0864] Provision to users

[0865] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[0866] Specific examples

[0867] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[0868] At the same time, the emotion engine analyzes the user's voice commands and facial expressions to detect if the user is concerned about price. The emotion engine reflects this result and strengthens price-focused recommendations. The generative AI summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user then reviews the materials, selects the product they like, and makes a purchase from the online store.

[0869] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[0870] The processing flow will be explained below.

[0871] The present invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is specifically implemented as follows.

[0872] Step 1:

[0873] Users take photos of their home appliances using a dedicated application on their smartphone or PC, press the upload button on the application, and simultaneously provide their emotional state to the system by using voice commands or capturing facial expressions with a webcam.

[0874] Step 2:

[0875] The terminal transmits the photos taken by the user, voice data, and facial expression capture data from the application to the server.

[0876] Step 3:

[0877] The server receives the photo data sent from the device and transfers it to the image analysis module. It also transfers the voice data and facial expression capture data to the emotion engine at the same time.

[0878] Step 4:

[0879] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[0880] Step 5:

[0881] The server's emotion engine analyzes the user's voice data and facial expression capture data to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time.

[0882] Step 6:

[0883] The server uses the analyzed information (model number, manufacturer, manufacturing date) to collect price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[0884] Step 7:

[0885] The server collects the following information:

[0886] New purchase price

[0887] Current second-hand purchase price

[0888] Lowest price on sale

[0889] Step 8:

[0890] The server stores the collected information in a database.

[0891] Step 9:

[0892] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[0893] Step 10:

[0894] The server's AI then summarizes the features of each product in the list of optimal new products and creates a presentation for the user. However, it also adjusts the proposals based on the user's emotional state. For example, if the user expresses anxiety, it emphasizes price-focused proposals that will reassure the user.

[0895] Step 11:

[0896] The server transmits the created presentation materials to the terminal.

[0897] Step 12:

[0898] The terminal receives the presentation materials sent from the server and displays them to the user.

[0899] Step 13:

[0900] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[0901] The above is the specific flow of program processing in the system of the present invention. The operations performed at each step allow users to easily find the best home appliances, and by utilizing the emotion engine, they can receive personalized suggestions and have a highly satisfying purchasing experience.

[0902] Example 2

[0903] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0904] Today's consumers must refer to a large number of options and information when choosing the best home appliance from a wide variety of products, which requires a lot of time and effort. Furthermore, systems that make blanket recommendations without recognizing emotions have the problem of making optimal product recommendations that meet the user's true needs. Furthermore, a mechanism for appropriately evaluating and organizing the collected information is needed.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for taking and uploading a photo of the home appliance used by the user, means for capturing the user's voice commands and facial expressions simultaneously with the photo, means for extracting the model number, manufacturer, and manufacturing date from the photo of the home appliance on the server, means for collecting price information from related websites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and generating presentation materials based on the user's emotional state, and means for transmitting the generated presentation materials to the user's terminal and displaying them. This allows the user to easily select the optimal home appliance, and by using an emotion engine, personalized suggestions based on the user's emotional state are possible.

[0906] "Means for taking and uploading photos" refers to a system in which users take photos of home appliances using their smartphones or PCs and send them to a server via a dedicated application.

[0907] "Means for capturing voice commands and facial expressions" refers to a mechanism that simultaneously captures voice instructions and facial expression data and sends them to a server when a user uploads a photo of a home appliance.

[0908] "Means for extracting model numbers, manufacturers, and manufacturing dates from photographs of home appliances on a server" refers to a system that analyzes photographs uploaded to a server using a generative AI model, etc., to identify the model number, manufacturer, and manufacturing date of the home appliance.

[0909] "Means for collecting price information" refers to a system in which the server collects price data from related websites (price comparison sites, online stores, review sites, etc.) based on the analyzed information.

[0910] "Means for evaluating using an evaluation algorithm and listing optimal new products" refers to a system in which the server selects and lists optimal new products according to specific evaluation criteria (e.g., price-oriented, performance-oriented, balanced, etc.) based on collected price information and product data.

[0911] The "means for generating presentation materials" is a mechanism by which the server summarizes the features of the listed products and generates presentation materials that take into account the user's emotional state.

[0912] The "means for transmitting and displaying presentation materials" refers to a mechanism for transmitting the generated presentation materials to a user's terminal, allowing the user to view the materials on the terminal.

[0913] The "emotion engine" is a system that analyzes the user's voice, facial expressions, and past behavioral data to recognize the user's emotional state (e.g., anxiety, satisfaction, excitement, etc.) in real time and adjusts the content of suggestions.

[0914] The present invention is a system for hassle-freely proposing optimal new products to consumers who are considering replacing their home appliances. In particular, by combining an emotion engine that recognizes the user's emotions, more personalized proposals can be made. This system utilizes various hardware and software to flexibly respond to user needs. This section describes specific embodiments of the system.

[0915] First, the user uses a smartphone or PC to take a photo of the home appliance they are currently using (e.g., refrigerator, washing machine, television, etc.). The user then launches a dedicated application and uploads the photo to the system. This application has the function of simultaneously capturing the user's voice commands and facial expressions. This data is then sent in bulk to the server.

[0916] The server receives the uploaded photos and voice and facial expression data. The image analysis module for photo analysis uses a generative AI model, which enables highly accurate extraction of information such as the model number, manufacturer, and manufacturing date. For example, the analysis results may yield information such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015." The information extracted in this way is used in the next processing step.

[0917] Next, the server uses this analysis information to collect price information from related websites. This information is collected using scraping technology, and data such as "price when purchased new," "current used purchase price," and "lowest sale price" are collected. For example, "price when purchased new: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" may be collected.

[0918] This collected information is stored in a database on the server. The server uses an evaluation algorithm to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced." For example, if price is the most important criterion, information such as "XYZ200 model, price: 70,000 yen" will be listed.

[0919] Furthermore, the server is equipped with an emotion engine that can analyze the user's voice, facial expressions, and past behavioral data. This allows the system to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time and adjust the content of its recommendations accordingly. For example, if the user is concerned about price, the system will strengthen price-focused recommendations.

[0920] The generative AI summarizes the features of the listed products and creates a presentation that reflects the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state. For example, the presentation might be, "Affordable refrigerator XYZ200, features: freezer included, price: 70,000 yen."

[0921] Finally, the server sends the generated presentation materials to the user's device, where the user can review them on their smartphone or PC. The user can then select the most suitable product from the presented products, and once they have decided to purchase, they can click the instant purchase link to proceed with the purchase at the online store.

[0922] As a concrete example of this system, consider a user considering replacing their refrigerator. The user takes a photo of their refrigerator at home and uploads it to the server using a dedicated application. The server then uses an image analysis module to extract information such as the refrigerator's model number, manufacturer, and manufacturing date, and collects price information from related websites. At the same time, the emotion engine analyzes the user's voice commands and facial expressions to understand their emotional state. Finally, the generative AI creates a presentation summarizing the plan and presents it to the user.

[0923] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[0924] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0925] Step 1:

[0926] The user takes a photo of the home appliances in their home and uploads it using a dedicated application. The user takes a photo of the appliance using a smartphone or PC and imports the photo file into the application. At the same time, voice commands and facial expressions are also captured. This information is sent from the application to the server. The input is the photo of the appliance taken by the user, voice data, and facial expression data, and the output is data sent to the server.

[0927] Step 2:

[0928] The server inputs the photos received from the user into the image analysis module. This module uses a generative AI model to perform highly accurate image analysis. Specifically, it extracts information such as the home appliance model number, manufacturer, and manufacturing date from the photo. After analysis, data such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015" is obtained. The input is the uploaded photo, and the output is the analyzed product information.

[0929] Step 3:

[0930] The server collects price information from websites based on the analyzed product information. Using scraping technology, data such as "new purchase price," "current used purchase price," and "lowest sale price" are obtained from related sites (price comparison sites, online stores, reviews, etc.). For example, information such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" is collected. The input is product information, and the output is the collected price information.

[0931] Step 4:

[0932] The server stores the collected price information in a database, which provides a basis for subsequent processing and revaluation. The input is the collected price information, and the output is the data stored in the database.

[0933] Step 5:

[0934] The server uses an evaluation algorithm based on the stored information to generate an optimal list of new products. Evaluation criteria include "price-oriented," "performance-oriented," and "balanced." For example, a list such as "price-oriented refrigerator XYZ200, price: 70,000 yen" is generated. The input is existing data in the database, and the output is the listed product information.

[0935] Step 6:

[0936] The server activates an emotion engine and analyzes the user's voice commands and facial expression data. By analyzing past behavioral data as well, the user's emotional state is recognized. The content of suggestions is adjusted based on these results. For example, if the user is concerned about price, suggestions that emphasize price are strengthened. The input is voice data, facial expression data, and past behavioral data, and the output is the emotion analysis results.

[0937] Step 7:

[0938] The server uses generative AI to summarize the features of the listed products and create presentation materials that reflect the user's emotional state. The user's desired criteria are also taken into consideration. For example, a presentation might be generated that describes the "price-conscious refrigerator XYZ200, features: freezer included, price: 70,000 yen." The input is the listed product information and the results of the emotional analysis, and the output is the presentation materials.

[0939] Step 8:

[0940] The server sends the generated presentation materials to the user's device, where the user can view them on their smartphone or PC. The input is the presentation materials, and the output is the materials sent to the user's device.

[0941] Step 9:

[0942] The user reviews the provided presentation materials and selects the most suitable product. If they decide to purchase, they click the instant purchase link to complete the purchase process at the online store. The input is the provided presentation materials, and the output is the purchase process for the selected product.

[0943] As a result, users can easily select the most suitable home appliances and receive personalized suggestions from the emotion engine.

[0944] (Application example 2)

[0945] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0946] When considering replacing home appliances, modern consumers find it difficult to gather a large amount of information and select the most suitable product. Furthermore, conventional systems make uniform recommendations without considering the user's emotions or individual preferences, making it difficult to improve user satisfaction. Given this background, there is a growing need for a system that takes user emotions into account and makes more personalized product recommendations.

[0947] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload an image of a home appliance, means for analyzing the model number, manufacturer, and manufacturing date from the image of the home appliance on the server, means for collecting price information from related information sources based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and presenting them to the user, means for analyzing the user's facial expressions and voice, and means for adjusting the content of proposals based on the results of the emotion analysis. This makes it possible to select and propose optimal products based on the user's emotions and desired criteria, thereby improving user satisfaction.

[0948] "Means for users to upload images of home appliances" refers to an interface or function that allows users to send images of home appliances to the system using a smartphone or other device.

[0949] "Means for analyzing the model number, manufacturer, and manufacturing date from images of home appliances on a server" refers to a function that uses image analysis technology on a server to automatically extract information such as the model number, manufacturer, and manufacturing date from images of home appliances.

[0950] "Means for collecting price information from related sources based on the analyzed information" refers to a technology for using the extracted product information to collect related price information from external sources such as price comparison sites and online stores.

[0951] "Means for evaluating collected information using an evaluation algorithm and listing optimal new products" refers to a function that uses an evaluation algorithm to select and list optimal new products based on collected price information and product information.

[0952] The "means for summarizing the features of the listed products and presenting them to the user" is a function for aggregating the features of the listed products, summarizing the main points, and presenting them to the user.

[0953] "Means for analyzing the user's facial expressions and voice" refers to technology for analyzing the user's facial expressions and voice and recognizing their emotional state.

[0954] The "means for adjusting the content of proposals based on the results of emotion analysis" is a function that takes into account the emotional state of the user obtained from the emotion analysis and adjusts the content of product proposals accordingly.

[0955] This invention is a system that assists users in the process of efficiently replacing their home appliances. Users upload images of their home appliances to an application using a smartphone or other device. The images are then sent to a server, where image analysis begins.

[0956] On the server, image analysis technology is used to automatically extract information such as model number, manufacturer, and manufacturing date from the uploaded image. This process uses image analysis software such as TensorFlow and OpenCV. The extracted information is then used by the server to collect price information from related sources. To collect price information, scraping technologies such as BeautifulSoup and Selenium are used.

[0957] The server also analyzes the user's facial expressions and voice in real time to recognize their emotional state. This emotion analysis uses Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API. Based on the analysis results, the server adjusts the proposal content and makes the optimal product recommendations for the user. The proposal content is displayed on the user's device as presentation materials generated using generative AI models such as OpenAI's GPT-4.

[0958] For example, consider the case where a user is considering replacing their refrigerator. The user takes a picture of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the image and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, based on this information, the server collects information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[0959] At the same time, the server uses an emotion engine to analyze the user's voice commands and facial expressions to detect if the user is concerned about price. Based on this result, the emotion engine strengthens price-focused proposals. The generative AI summarizes the features of the listed products, creates a presentation document, and displays it to the user via their device. The user then reviews the document, selects the product they like, and purchases it from the online store.

[0960] Examples of prompts for the generation AI include:

[0961] "This user is considering replacing their refrigerator. The information for their current refrigerator is as follows: model number XYZ123, manufacturer A, manufactured in January 2015. The associated price information is new purchase price of 100,000 yen, used purchase price of 20,000 yen, and lowest sale price of 80,000 yen. The user is concerned about the price. Please generate the optimal replacement proposal based on this information."

[0962] Such a system would allow users to receive optimal product recommendations based on their preferences and emotional state, making the replacement purchase process more efficient and satisfying.

[0963] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0964] Step 1:

[0965] A user takes a picture of a home appliance with their smartphone and uploads it through a dedicated application. The user's input is the image of the appliance, and the output is that the image is sent to the server.

[0966] Step 2:

[0967] The server sends the received images to an image analysis module, which extracts information such as the model number, manufacturer, and manufacturing date. The data input is an image of the home appliance, and the output is the extracted product information (model number, manufacturer, and manufacturing date). TensorFlow and OpenCV are used for processing.

[0968] Step 3:

[0969] The server uses the extracted product information to collect price information from related sources (price comparison sites and online stores). The input information is product information, and the output is collected price information (new purchase price, used purchase price, lowest sale price). BeautifulSoup and Selenium are used to collect the data.

[0970] Step 4:

[0971] The server captures the user's facial expressions and voice to analyze the desired criteria and emotional state entered by the user. The input is the user's facial expression images and voice data, and the output is the analyzed user's emotional state. For emotion analysis, Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API are used.

[0972] Step 5:

[0973] The server uses an evaluation algorithm to list the most suitable new products based on the collected price information and the user's emotional state. The input is price information and the results of the emotional analysis, and the output is a list of the most suitable new products. The evaluation algorithm takes into account the user's desired criteria and emotional state, and ranks the products by assigning them scores.

[0974] Step 6:

[0975] The server uses a generative AI model (e.g., OpenAI's GPT-4) to summarize the features of the listed products and create presentation materials. The input is the list of optimal new products and the user's emotional state, and the output is the presentation materials. Prompts are provided to the generative AI model to generate appropriate materials.

[0976] Step 7:

[0977] The server sends the created presentation materials to the user's terminal and displays them through the application. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can review the materials and select the suggested products.

[0978] Step 8:

[0979] The user selects a product to purchase from the suggested products and clicks on a link to the online store. The input is the purchase link included in the presentation materials, and the output is the purchase page of the online store. The user can then complete the purchase process.

[0980] 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.

[0981] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0982] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0983] [Fourth embodiment]

[0984] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0985] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0986] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0987] 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.

[0988] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0989] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0990] 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.

[0991] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0992] 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.

[0993] 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 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.

[0994] 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.

[0995] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0996] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0997] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. Specifically, users upload photos of the home appliances, and the system analyzes the images on a server, collects necessary information, and proposes optimal new products. This system is configured as follows:

[0998] User operations

[0999] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, television, etc.), and the photo is uploaded to the system via a dedicated application on a smartphone or PC.

[1000] Image analysis on the server

[1001] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[1002] Collection of information

[1003] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[1004] Information collection and evaluation

[1005] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[1006] Generate a presentation

[1007] The generative AI summarizes the features of the listed products and creates a presentation for the user, which includes proposals based on the user's desired criteria.

[1008] Provision to users

[1009] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[1010] Specific examples

[1011] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." The server then uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products (e.g., "Company A's model that prioritizes price" and "Company B's model that prioritizes performance"). The AI ​​then summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user reviews the materials, selects the product they like, and purchases it from the online store.

[1012] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[1013] The processing flow will be explained below.

[1014] Step 1:

[1015] Users take a photo of the home appliances they are using at home using a dedicated application on their smartphone or PC, and then press the upload button on the application.

[1016] Step 2:

[1017] The device sends the photo taken by the user from the application to the server, which includes the photo data as well as basic information about the user (such as the user ID and location information).

[1018] Step 3:

[1019] The server receives the photo data sent from the terminal and transfers it to the image analysis module.

[1020] Step 4:

[1021] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[1022] Step 5:

[1023] Based on the analyzed information, the server collects price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[1024] Step 6:

[1025] The server collects the following information:

[1026] New purchase price

[1027] Current second-hand purchase price

[1028] Lowest price on sale

[1029] Step 7:

[1030] The server stores the collected information in a database.

[1031] Step 8:

[1032] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[1033] Step 9:

[1034] The server's generation AI summarizes the features of each product in the optimal new product list and creates presentation materials for users.

[1035] Step 10:

[1036] The server transmits the created presentation materials to the terminal.

[1037] Step 11:

[1038] The terminal receives the presentation materials sent from the server and displays them to the user.

[1039] Step 12:

[1040] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[1041] The above is a specific flow of program processing in the system of the present invention. The operations performed in each step allow the user to easily find and purchase the most suitable home appliance.

[1042] Example 1

[1043] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1044] Today's consumers are surrounded by a wide variety of home appliances, and are required to efficiently collect and evaluate vast amounts of information when replacing or purchasing new products. However, manually researching detailed information such as product model numbers and manufacturing dates is extremely time-consuming, and when price information is also collected and compared, it requires a considerable amount of time and effort. This makes it difficult for consumers to select the optimal home appliance.

[1045] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1046] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date information from the images of the home appliances on the server, means for collecting price data from related websites based on the analyzed data, means for summarizing the listed product characteristics using a generative AI model and making recommendations, and means for storing the collected data in a database. This enables users to efficiently replace their home appliances and easily select and purchase the optimal new products.

[1047] "Users" refer to consumers who use the system to upload images of home appliances and receive new product suggestions.

[1048] "Server" refers to the central processing unit that receives and analyzes data sent by users, collects and evaluates information, and generates and sends optimal new product proposals.

[1049] "Home appliances" refers to electronic devices used in the home, such as refrigerators, washing machines, and televisions.

[1050] "Images" refers to photographs or pictures of home appliances.

[1051] "Model number" refers to the product identification number assigned by the manufacturer of a home appliance.

[1052] "Manufacturing company" refers to a corporation that manufactures and sells home appliances.

[1053] "Manufacturing date information" refers to data indicating the date and year when a home appliance was manufactured.

[1054] "Related websites" refers to internet pages that provide prices and information about home appliances, such as price comparison sites, online stores, and review sites.

[1055] "Price data" refers to price information about home appliances, such as the price when purchased new, the price at which a used item is purchased, and the lowest sale price.

[1056] A "generative AI model" refers to an artificial intelligence computational model that summarizes product characteristics based on large amounts of data and makes suggestions to users.

[1057] "Evaluation algorithm" refers to a calculation method for evaluating and selecting optimal new products based on collected data.

[1058] "Database" refers to a digital storage system for systematically storing and managing collected information.

[1059] "Means" refers to a method or apparatus for achieving a particular function.

[1060] "Suggestion" refers to the act of selecting the most suitable home appliance for the user and presenting the options.

[1061] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes optimal new products.

[1062] First, a user takes a picture of the home appliances they use at home using a smartphone or digital camera. This image is then uploaded to the system using a dedicated application on their smartphone or PC. The dedicated application has an upload function that allows the user to send the image to the server.

[1063] The server then receives the image sent by the user and passes it to an image analysis module, which uses tools such as Google Cloud Vision or Azure Computer Vision. This module uses a generative AI model (such as OpenAI's CLIP model or YOLOv5) to accurately extract information such as the appliance's model number, manufacturer, and manufacturing date from the image.

[1064] Based on the analyzed information, the server collects price data from related websites. Web scraping technologies (such as BeautifulSoup or Scrapy) are used to obtain information such as "new purchase price," "current used buy price," and "lowest sale price" from price comparison sites and online stores (e.g., price comparison sites and major online stores). The collected data is then stored in a database (e.g., MySQL or PostgreSQL) on the server.

[1065] The stored data is then fed into an evaluation algorithm (e.g., Linear Regression or Decision Trees) to generate a list of optimal new products based on criteria such as "price-first," "performance-first," and "balanced." A generative AI model (e.g., OpenAI's GPT-4) is used to summarize the features of each product from this list and create a presentation for users.

[1066] Finally, the server sends the generated presentation materials to the user's device, where the user can review the materials through a dedicated application, select the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase process at the online store.

[1067] As a concrete example, consider a user considering replacing their refrigerator. The user takes a photo of their home refrigerator with their smartphone and uploads the photo through a dedicated application. The server receives the photo and uses an image analysis module to extract information such as "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen." The collected information is stored in a database, and an evaluation algorithm generates a list of optimal new products based on the user's desired criteria (e.g., "price-first" or "performance-first"). The generation AI summarizes the features of the products on the list and creates presentation materials. Finally, the materials are sent to the user's device, where they can be viewed using a dedicated application. The user selects from the suggested products and clicks the purchase link to purchase them from the online store.

[1068] An example prompt might be, "I'm considering replacing my refrigerator, so I uploaded a photo of my current one. Can you suggest a suitable new product?"

[1069] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, which is expected to improve satisfaction in the market.

[1070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1071] Step 1:

[1072] Users take pictures of home appliances and upload them to the system through a dedicated application.

[1073] Input: Image of a home appliance

[1074] Specific operation: A user takes pictures of the home appliances they use at home using a smartphone or digital camera. Then, they use a dedicated application to upload the pictures to the system. The application has an "Upload image" button, which the user clicks.

[1075] Step 2:

[1076] The server receives the uploaded images and passes them to the analysis module.

[1077] Input: User-submitted images of home appliances

[1078] Output: Passing image data to the analysis module

[1079] Specific operation: The server receives images uploaded by users via HTTP requests and passes the image data to an analysis module, which uses cloud services (e.g., Google Cloud Vision or Azure Computer Vision).

[1080] Step 3:

[1081] The server uses an image analysis module to extract model number, manufacturer, and manufacturing date information.

[1082] Input: Image data

[1083] Output: Extracted model number, manufacturer, and manufacturing date information

[1084] How it works: The server calls the image analysis module and uses an AI model (e.g., YOLOv5) to analyze the text and features in the image. The extracted data (model number, manufacturer, and manufacturing date information) is used for further processing.

[1085] Step 4:

[1086] The server collects price data from relevant websites based on the parsed information.

[1087] Input: Extracted model number, manufacturer, manufacturing date information

[1088] Output: Collected price data

[1089] Specific operation: The server uses web scraping technology (e.g., BeautifulSoup or Scrapy) to obtain price data such as "new purchase price," "used purchase price," and "lowest sale price" from price comparison sites and online stores.

[1090] Step 5:

[1091] The server stores the collected price data in a database.

[1092] Input: Collected price data

[1093] Output: Price data stored in a database

[1094] What it does: The server uses a database connectivity library (e.g., SQLAlchemy) to store the collected price data in a database (e.g., MySQL, PostgreSQL), making it available for subsequent use in the evaluation algorithm.

[1095] Step 6:

[1096] The server uses a rating algorithm to generate a list of optimal new products.

[1097] Input: Price data stored in a database

[1098] Output: Best New Product List

[1099] Specific operation: The server executes an evaluation algorithm (e.g., Linear Regression, Decision Trees) and analyzes the results based on the user's desired criteria (e.g., "price-oriented," "performance-oriented," "balanced," etc.). It generates a list of optimal new products and proceeds to the next step.

[1100] Step 7:

[1101] The server uses a generative AI model to summarize the features of the listed products and create presentation materials.

[1102] Input: Best New Product List

[1103] Output: Presentation materials

[1104] Specific operation: Using a generative AI model (e.g., GPT-4), the features of each product in the new product list are summarized. The summarized content is compiled into a presentation document for users and created as a proposal document.

[1105] Step 8:

[1106] The server transmits the generated presentation materials to the terminal.

[1107] Input: Presentation materials

[1108] Output: Proposal materials displayed on the user's device

[1109] Specific operation: The server sends the generated presentation materials to the user's device, where the user can view them through a dedicated application.

[1110] Step 9:

[1111] The user selects from the suggested products and clicks the purchase link to make a purchase at the online store.

[1112] Input: Product information selected by the user

[1113] Output: Purchase processing on the online store

[1114] Specific operation: The user checks the suggested products in the dedicated application and clicks the purchase link for the desired product, which redirects them to the purchase page of the online store and allows them to proceed with the purchase.

[1115] This allows users to efficiently and hassle-freely select and purchase the most suitable home appliances.

[1116] (Application example 1)

[1117] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1118] When modern consumers consider replacing their home appliances, the time and effort required is a major problem. The process of manually collecting information such as product model numbers, manufacturers, and manufacturing dates, and then selecting the optimal new product based on that information, is particularly tedious. Furthermore, it is necessary to individually collect price information from numerous online sites, compare and evaluate options, which requires advanced knowledge and time. This presents a challenge for consumers, making it difficult to select the optimal new product without hassle and purchase it efficiently.

[1119] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1120] In this invention, the server includes means for users to upload images of home appliances, means for analyzing the model number, manufacturer, and manufacturing date from the images of the home appliances on the server, means for collecting price information from related online sites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products using a generative AI model and presenting them to the user, and means for providing purchase links for the products selected by the user. This allows consumers to easily select and purchase the optimal home appliances.

[1121] "Users" are consumers who use the system to consider replacing their home appliances.

[1122] "Images of Home Appliances" are photographs of home appliances taken by users using smartphones or other image capture devices and uploaded to the system.

[1123] "Server" means a computer system on a network that performs functions such as image analysis, information gathering, evaluation algorithms, and presentation generation using generative AI models.

[1124] A "model number" is an identification number that indicates a specific model of a home appliance.

[1125] "Manufacturer" refers to the company that produced the home appliance.

[1126] "Date of manufacture" is the date or month when the home appliance was manufactured.

[1127] "Online site" refers to a website that provides product price information, including price comparison sites and mail order sites.

[1128] "Price information" refers to information about the price of home appliances, such as the purchase price, second-hand purchase price, and sale price.

[1129] An "evaluation algorithm" is a calculation method for selecting the best new product for a user based on collected pricing information and other data.

[1130] A "generative AI model" is an artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[1131] A "presentation" is a document generated by a generative AI model and presented to the user about the features of a new product and the reasons for its proposal.

[1132] A "purchase link" is a link that allows a user to purchase the selected product directly from the online store.

[1133] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any effort. This system allows users to upload images of the home appliances, analyzes the images on a server, collects necessary information, and proposes new products.

[1134] System Configuration

[1135] Hardware

[1136] User terminal: A device such as a smartphone or PC that allows users to take pictures of home appliances and upload them to the system.

[1137] Server: A computer system on a network that performs image analysis, information collection, and presentation generation using generative AI models.

[1138] software

[1139] Image analysis module: Software for analyzing the model number, manufacturer, and manufacturing date from images of home appliances.

[1140] Information collection module: Software to collect price information from related online sites based on the analyzed information. It uses scraping technology.

[1141] Evaluation algorithm: A calculation method for selecting the most suitable new product based on collected information.

[1142] Generative AI model: An artificial intelligence model that summarizes the features of listed products and generates presentation materials.

[1143] Processing steps

[1144] 1. Image upload: Users take pictures of home appliances using their smartphones or PCs and upload them to the system via a dedicated application.

[1145] 2. Image analysis: The server processes the received images using an image analysis module to extract information such as the appliance model number, manufacturer, and manufacturing date.

[1146] 3. Information collection: Based on the extracted information, the server collects product price information (new purchase price, used purchase price, lowest sale price, etc.) from related online sites.

[1147] 4. Selection of optimal products: The server processes the collected price information with an evaluation algorithm to generate a list of optimal products based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[1148] 5. Presentation generation: The generative AI model summarizes the features of the listed products and creates a presentation to provide to the user.

[1149] 6. User presentation and purchase link provision: Finally, the presentation materials are sent from the server to the user's terminal, and the user can review the materials and proceed with purchasing the selected product at the online store via the purchase link.

[1150] Specific examples

[1151] When a user is considering replacing their refrigerator, they take a picture of their home refrigerator using their smartphone and upload it through a dedicated application. The server uses an image analysis module to extract the model number, manufacturer, and manufacturing date, and uses an information collection module to collect information from price comparison sites and online shopping sites, such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen." An evaluation algorithm then generates a list of optimal new products, such as "Company A's model that prioritizes price" and "Company B's model that prioritizes performance." The generative AI model summarizes the product features based on the list, creates presentation materials, and provides them to the user.

[1152] Prompt Sentence Examples

[1153] "Based on the model number, manufacturer, and production date extracted from the image, collect the best price information and generate the best new product proposals using an evaluation algorithm."

[1154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1155] Step 1:

[1156] Users take pictures of home appliances using their smartphones or PCs and upload them to the system through a dedicated application. The input is the image of the home appliance, and the output is the uploaded image data. The device then sends this image data to the server.

[1157] Step 2:

[1158] The server receives the uploaded images and uses an image analysis module to analyze the model number, manufacturer, and manufacturing date from the images. The input is the uploaded image data, and the output is the extracted product information (model number, manufacturer, manufacturing date). The server temporarily stores the analysis results in a database.

[1159] Step 3:

[1160] The server uses the parsed product information to use scraping technology to collect price information from related online sites. The input is product information (model number, manufacturer, manufacturing date), and the output is collected price information (new purchase price, used buyback price, lowest sale price). The server stores the collected price information in a database.

[1161] Step 4:

[1162] The server uses an evaluation algorithm to generate an optimal list of new products based on the collected price information. The input is the collected price information, and the output is a list of optimal new products. This list is also based on the user's desired criteria (price-oriented, performance-oriented, etc.).

[1163] Step 5:

[1164] The server uses a generative AI model to summarize the product features of the optimal new product list and create a presentation. The input is the optimal new product list, and the output is the presentation. The generative AI model summarizes the product's benefits and features in a format that is easy for users to understand.

[1165] Step 6:

[1166] The server sends the generated presentation materials to the user's terminal and displays them to the user. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can check the proposed new products, select the most suitable product, and click the purchase link.

[1167] This allows users to easily select and purchase the most suitable home appliances.

[1168] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1169] This invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is configured as follows.

[1170] User operations

[1171] First, the user takes a photo of the home appliances they use at home (e.g., refrigerator, washing machine, TV, etc.). This photo is then uploaded to the system via a dedicated application on a smartphone or PC. The application can also capture the user's voice commands and facial expressions at the same time.

[1172] Image analysis on the server

[1173] Next, the server receives the photo sent by the user and uses an image analysis module to extract information such as the model number, manufacturer, and manufacturing date from the photo. This process uses generative AI to perform highly accurate image analysis.

[1174] Collection of information

[1175] Based on the analyzed information, the server collects price information from related websites (such as price comparison sites, online stores, review sites, etc.) This collection process uses scraping technology to obtain information such as "new purchase price," "current used purchase price," and "lowest sale price."

[1176] Information collection and evaluation

[1177] The collected information is stored in a database on a server, and then an evaluation algorithm is used to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[1178] Emotion Engine Operation

[1179] The server then uses an emotion engine to analyze the user's voice, facial expressions, and past behavioral data. This allows the server to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time. The emotion engine then adjusts the content of the recommendations based on the analysis results. For example, if the user shows a relieved expression, the server will strengthen price-focused recommendations.

[1180] Generate a presentation

[1181] The generative AI summarizes the features of the listed products and creates a presentation that takes into account the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state.

[1182] Provision to users

[1183] Finally, the server sends the presentation materials to the terminal and displays them to the user, who can review them, choose the most suitable product from the suggested products, and click the instant purchase link to proceed with the purchase from the online store.

[1184] Specific examples

[1185] For example, suppose a user is considering replacing their refrigerator. The user takes a photo of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the photo and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, the server uses this information to collect information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[1186] At the same time, the emotion engine analyzes the user's voice commands and facial expressions to detect if the user is concerned about price. The emotion engine reflects this result and strengthens price-focused recommendations. The generative AI summarizes the features of the products on the list, creates presentation materials, and displays them to the user via their device. The user then reviews the materials, selects the product they like, and makes a purchase from the online store.

[1187] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[1188] The processing flow will be explained below.

[1189] The present invention is a system that proposes optimal new products to consumers who are considering replacing their home appliances without any hassle. In particular, by combining it with an emotion engine that recognizes the user's emotions, it is possible to make more personalized proposals. This system is specifically implemented as follows.

[1190] Step 1:

[1191] Users take photos of their home appliances using a dedicated application on their smartphone or PC, press the upload button on the application, and simultaneously provide their emotional state to the system by using voice commands or capturing facial expressions with a webcam.

[1192] Step 2:

[1193] The terminal transmits the photos taken by the user, voice data, and facial expression capture data from the application to the server.

[1194] Step 3:

[1195] The server receives the photo data sent from the device and transfers it to the image analysis module. It also transfers the voice data and facial expression capture data to the emotion engine at the same time.

[1196] Step 4:

[1197] The server's image analysis module uses generative AI to analyze images in order to extract the home appliance's model number, manufacturer, and manufacturing date from the photo.

[1198] Step 5:

[1199] The server's emotion engine analyzes the user's voice data and facial expression capture data to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time.

[1200] Step 6:

[1201] The server uses the analyzed information (model number, manufacturer, manufacturing date) to collect price information from related websites (price comparison sites, online stores, review sites, etc.) using scraping technology.

[1202] Step 7:

[1203] The server collects the following information:

[1204] New purchase price

[1205] Current second-hand purchase price

[1206] Lowest price on sale

[1207] Step 8:

[1208] The server stores the collected information in a database.

[1209] Step 9:

[1210] The server uses an evaluation algorithm to evaluate the collected information and generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced."

[1211] Step 10:

[1212] The server's AI then summarizes the features of each product in the list of optimal new products and creates a presentation for the user. However, it also adjusts the proposals based on the user's emotional state. For example, if the user expresses anxiety, it emphasizes price-focused proposals that will reassure the user.

[1213] Step 11:

[1214] The server transmits the created presentation materials to the terminal.

[1215] Step 12:

[1216] The terminal receives the presentation materials sent from the server and displays them to the user.

[1217] Step 13:

[1218] The user reviews the displayed presentation materials, selects the most suitable product from the suggested products, and clicks the instant purchase link to proceed with the purchase at the online store.

[1219] The above is the specific flow of program processing in the system of the present invention. The operations performed at each step allow users to easily find the best home appliances, and by utilizing the emotion engine, they can receive personalized suggestions and have a highly satisfying purchasing experience.

[1220] Example 2

[1221] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] Today's consumers must refer to a large number of options and information when choosing the best home appliance from a wide variety of products, which requires a lot of time and effort. Furthermore, systems that make blanket recommendations without recognizing emotions have the problem of making optimal product recommendations that meet the user's true needs. Furthermore, a mechanism for appropriately evaluating and organizing the collected information is needed.

[1223] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for taking and uploading a photo of the home appliance used by the user, means for capturing the user's voice commands and facial expressions simultaneously with the photo, means for extracting the model number, manufacturer, and manufacturing date from the photo of the home appliance on the server, means for collecting price information from related websites based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and generating presentation materials based on the user's emotional state, and means for transmitting the generated presentation materials to the user's terminal and displaying them. This allows the user to easily select the optimal home appliance, and by using an emotion engine, personalized suggestions based on the user's emotional state are possible.

[1224] "Means for taking and uploading photos" refers to a system in which users take photos of home appliances using their smartphones or PCs and send them to a server via a dedicated application.

[1225] "Means for capturing voice commands and facial expressions" refers to a mechanism that simultaneously captures voice instructions and facial expression data and sends them to a server when a user uploads a photo of a home appliance.

[1226] "Means for extracting model numbers, manufacturers, and manufacturing dates from photographs of home appliances on a server" refers to a system that analyzes photographs uploaded to a server using a generative AI model, etc., to identify the model number, manufacturer, and manufacturing date of the home appliance.

[1227] "Means for collecting price information" refers to a system in which the server collects price data from related websites (price comparison sites, online stores, review sites, etc.) based on the analyzed information.

[1228] "Means for evaluating using an evaluation algorithm and listing optimal new products" refers to a system in which the server selects and lists optimal new products according to specific evaluation criteria (e.g., price-oriented, performance-oriented, balanced, etc.) based on collected price information and product data.

[1229] The "means for generating presentation materials" is a mechanism by which the server summarizes the features of the listed products and generates presentation materials that take into account the user's emotional state.

[1230] The "means for transmitting and displaying presentation materials" refers to a mechanism for transmitting the generated presentation materials to a user's terminal, allowing the user to view the materials on the terminal.

[1231] The "emotion engine" is a system that analyzes the user's voice, facial expressions, and past behavioral data to recognize the user's emotional state (e.g., anxiety, satisfaction, excitement, etc.) in real time and adjusts the content of suggestions.

[1232] The present invention is a system for hassle-freely proposing optimal new products to consumers who are considering replacing their home appliances. In particular, by combining an emotion engine that recognizes the user's emotions, more personalized proposals can be made. This system utilizes various hardware and software to flexibly respond to user needs. This section describes specific embodiments of the system.

[1233] First, the user uses a smartphone or PC to take a photo of the home appliance they are currently using (e.g., refrigerator, washing machine, television, etc.). The user then launches a dedicated application and uploads the photo to the system. This application has the function of simultaneously capturing the user's voice commands and facial expressions. This data is then sent in bulk to the server.

[1234] The server receives the uploaded photos and voice and facial expression data. The image analysis module for photo analysis uses a generative AI model, which enables highly accurate extraction of information such as the model number, manufacturer, and manufacturing date. For example, the analysis results may yield information such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015." The information extracted in this way is used in the next processing step.

[1235] Next, the server uses this analysis information to collect price information from related websites. This information is collected using scraping technology, and data such as "price when purchased new," "current used purchase price," and "lowest sale price" are collected. For example, "price when purchased new: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" may be collected.

[1236] This collected information is stored in a database on the server. The server uses an evaluation algorithm to generate a list of optimal new products based on criteria such as "price-oriented," "performance-oriented," and "balanced." For example, if price is the most important criterion, information such as "XYZ200 model, price: 70,000 yen" will be listed.

[1237] Furthermore, the server is equipped with an emotion engine that can analyze the user's voice, facial expressions, and past behavioral data. This allows the system to recognize the user's emotional state (e.g., excitement, satisfaction, anxiety, etc.) in real time and adjust the content of its recommendations accordingly. For example, if the user is concerned about price, the system will strengthen price-focused recommendations.

[1238] The generative AI summarizes the features of the listed products and creates a presentation that reflects the user's emotional state. This presentation includes suggestions based on the user's desired criteria and emotional state. For example, the presentation might be, "Affordable refrigerator XYZ200, features: freezer included, price: 70,000 yen."

[1239] Finally, the server sends the generated presentation materials to the user's device, where the user can review them on their smartphone or PC. The user can then select the most suitable product from the presented products, and once they have decided to purchase, they can click the instant purchase link to proceed with the purchase at the online store.

[1240] As a concrete example of this system, consider a user considering replacing their refrigerator. The user takes a photo of their refrigerator at home and uploads it to the server using a dedicated application. The server then uses an image analysis module to extract information such as the refrigerator's model number, manufacturer, and manufacturing date, and collects price information from related websites. At the same time, the emotion engine analyzes the user's voice commands and facial expressions to understand their emotional state. Finally, the generative AI creates a presentation summarizing the plan and presents it to the user.

[1241] In this way, by implementing the present invention, consumers can easily select and purchase the most suitable home appliances, and by using the emotion engine, a more personalized experience can be provided.

[1242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1243] Step 1:

[1244] The user takes a photo of the home appliances in their home and uploads it using a dedicated application. The user takes a photo of the appliance using a smartphone or PC and imports the photo file into the application. At the same time, voice commands and facial expressions are also captured. This information is sent from the application to the server. The input is the photo of the appliance taken by the user, voice data, and facial expression data, and the output is data sent to the server.

[1245] Step 2:

[1246] The server inputs the photos received from the user into the image analysis module. This module uses a generative AI model to perform highly accurate image analysis. Specifically, it extracts information such as the home appliance model number, manufacturer, and manufacturing date from the photo. After analysis, data such as "Model number: XYZ123," "Manufacturer: Company A," and "Manufacturing date: January 2015" is obtained. The input is the uploaded photo, and the output is the analyzed product information.

[1247] Step 3:

[1248] The server collects price information from websites based on the analyzed product information. Using scraping technology, data such as "new purchase price," "current used purchase price," and "lowest sale price" are obtained from related sites (price comparison sites, online stores, reviews, etc.). For example, information such as "new purchase price: 100,000 yen," "used purchase price: 20,000 yen," and "lowest sale price: 80,000 yen" is collected. The input is product information, and the output is the collected price information.

[1249] Step 4:

[1250] The server stores the collected price information in a database, which provides a basis for subsequent processing and revaluation. The input is the collected price information, and the output is the data stored in the database.

[1251] Step 5:

[1252] The server uses an evaluation algorithm based on the stored information to generate an optimal list of new products. Evaluation criteria include "price-oriented," "performance-oriented," and "balanced." For example, a list such as "price-oriented refrigerator XYZ200, price: 70,000 yen" is generated. The input is existing data in the database, and the output is the listed product information.

[1253] Step 6:

[1254] The server activates an emotion engine and analyzes the user's voice commands and facial expression data. By analyzing past behavioral data as well, the user's emotional state is recognized. The content of suggestions is adjusted based on these results. For example, if the user is concerned about price, suggestions that emphasize price are strengthened. The input is voice data, facial expression data, and past behavioral data, and the output is the emotion analysis results.

[1255] Step 7:

[1256] The server uses generative AI to summarize the features of the listed products and create presentation materials that reflect the user's emotional state. The user's desired criteria are also taken into consideration. For example, a presentation might be generated that describes the "price-conscious refrigerator XYZ200, features: freezer included, price: 70,000 yen." The input is the listed product information and the results of the emotional analysis, and the output is the presentation materials.

[1257] Step 8:

[1258] The server sends the generated presentation materials to the user's device, where the user can view them on their smartphone or PC. The input is the presentation materials, and the output is the materials sent to the user's device.

[1259] Step 9:

[1260] The user reviews the provided presentation materials and selects the most suitable product. If they decide to purchase, they click the instant purchase link to complete the purchase process at the online store. The input is the provided presentation materials, and the output is the purchase process for the selected product.

[1261] As a result, users can easily select the most suitable home appliances and receive personalized suggestions from the emotion engine.

[1262] (Application example 2)

[1263] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] When considering replacing home appliances, modern consumers find it difficult to gather a large amount of information and select the most suitable product. Furthermore, conventional systems make uniform recommendations without considering the user's emotions or individual preferences, making it difficult to improve user satisfaction. Given this background, there is a growing need for a system that takes user emotions into account and makes more personalized product recommendations.

[1265] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload an image of a home appliance, means for analyzing the model number, manufacturer, and manufacturing date from the image of the home appliance on the server, means for collecting price information from related information sources based on the analyzed information, means for evaluating the collected information using an evaluation algorithm and listing optimal new products, means for summarizing the features of the listed products and presenting them to the user, means for analyzing the user's facial expressions and voice, and means for adjusting the content of proposals based on the results of the emotion analysis. This makes it possible to select and propose optimal products based on the user's emotions and desired criteria, thereby improving user satisfaction.

[1266] "Means for users to upload images of home appliances" refers to an interface or function that allows users to send images of home appliances to the system using a smartphone or other device.

[1267] "Means for analyzing the model number, manufacturer, and manufacturing date from images of home appliances on a server" refers to a function that uses image analysis technology on a server to automatically extract information such as the model number, manufacturer, and manufacturing date from images of home appliances.

[1268] "Means for collecting price information from related sources based on the analyzed information" refers to a technology for using the extracted product information to collect related price information from external sources such as price comparison sites and online stores.

[1269] "Means for evaluating collected information using an evaluation algorithm and listing optimal new products" refers to a function that uses an evaluation algorithm to select and list optimal new products based on collected price information and product information.

[1270] The "means for summarizing the features of the listed products and presenting them to the user" is a function for aggregating the features of the listed products, summarizing the main points, and presenting them to the user.

[1271] "Means for analyzing the user's facial expressions and voice" refers to technology for analyzing the user's facial expressions and voice and recognizing their emotional state.

[1272] The "means for adjusting the content of proposals based on the results of emotion analysis" is a function that takes into account the emotional state of the user obtained from the emotion analysis and adjusts the content of product proposals accordingly.

[1273] This invention is a system that assists users in the process of efficiently replacing their home appliances. Users upload images of their home appliances to an application using a smartphone or other device. The images are then sent to a server, where image analysis begins.

[1274] On the server, image analysis technology is used to automatically extract information such as model number, manufacturer, and manufacturing date from the uploaded image. This process uses image analysis software such as TensorFlow and OpenCV. The extracted information is then used by the server to collect price information from related sources. To collect price information, scraping technologies such as BeautifulSoup and Selenium are used.

[1275] The server also analyzes the user's facial expressions and voice in real time to recognize their emotional state. This emotion analysis uses Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API. Based on the analysis results, the server adjusts the proposal content and makes the optimal product recommendations for the user. The proposal content is displayed on the user's device as presentation materials generated using generative AI models such as OpenAI's GPT-4.

[1276] For example, consider the case where a user is considering replacing their refrigerator. The user takes a picture of their refrigerator at home with their smartphone and uploads it through a dedicated application. The server receives the image and uses an image analysis module to extract the following information: "Model number: XYZ123," "Manufacturer: Company A," and "Date of manufacture: January 2015." Next, based on this information, the server collects information from price comparison sites and online stores, such as "New purchase price: 100,000 yen," "Used purchase price: 20,000 yen," and "Lowest sale price: 80,000 yen."

[1277] At the same time, the server uses an emotion engine to analyze the user's voice commands and facial expressions to detect if the user is concerned about price. Based on this result, the emotion engine strengthens price-focused proposals. The generative AI summarizes the features of the listed products, creates a presentation document, and displays it to the user via their device. The user then reviews the document, selects the product they like, and purchases it from the online store.

[1278] Examples of prompts for the generation AI include:

[1279] "This user is considering replacing their refrigerator. The information for their current refrigerator is as follows: model number XYZ123, manufacturer A, manufactured in January 2015. The associated price information is new purchase price of 100,000 yen, used purchase price of 20,000 yen, and lowest sale price of 80,000 yen. The user is concerned about the price. Please generate the optimal replacement proposal based on this information."

[1280] Such a system would allow users to receive optimal product recommendations based on their preferences and emotional state, making the replacement purchase process more efficient and satisfying.

[1281] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1282] Step 1:

[1283] A user takes a picture of a home appliance with their smartphone and uploads it through a dedicated application. The user's input is the image of the appliance, and the output is that the image is sent to the server.

[1284] Step 2:

[1285] The server sends the received images to an image analysis module, which extracts information such as the model number, manufacturer, and manufacturing date. The data input is an image of the home appliance, and the output is the extracted product information (model number, manufacturer, and manufacturing date). TensorFlow and OpenCV are used for processing.

[1286] Step 3:

[1287] The server uses the extracted product information to collect price information from related sources (price comparison sites and online stores). The input information is product information, and the output is collected price information (new purchase price, used purchase price, lowest sale price). BeautifulSoup and Selenium are used to collect the data.

[1288] Step 4:

[1289] The server captures the user's facial expressions and voice to analyze the desired criteria and emotional state entered by the user. The input is the user's facial expression images and voice data, and the output is the analyzed user's emotional state. For emotion analysis, Google Cloud's Emotion Analysis API and Microsoft Azure's Emotion API are used.

[1290] Step 5:

[1291] The server uses an evaluation algorithm to list the most suitable new products based on the collected price information and the user's emotional state. The input is price information and the results of the emotional analysis, and the output is a list of the most suitable new products. The evaluation algorithm takes into account the user's desired criteria and emotional state, and ranks the products by assigning them scores.

[1292] Step 6:

[1293] The server uses a generative AI model (e.g., OpenAI's GPT-4) to summarize the features of the listed products and create presentation materials. The input is the list of optimal new products and the user's emotional state, and the output is the presentation materials. Prompts are provided to the generative AI model to generate appropriate materials.

[1294] Step 7:

[1295] The server sends the created presentation materials to the user's terminal and displays them through the application. The input is the presentation materials, and the output is the presentation materials displayed on the user's terminal. The user can review the materials and select the suggested products.

[1296] Step 8:

[1297] The user selects a product to purchase from the suggested products and clicks on a link to the online store. The input is the purchase link included in the presentation materials, and the output is the purchase page of the online store. The user can then complete the purchase process.

[1298] 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.

[1299] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1300] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1301] 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.

[1302] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1303] 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.

[1304] 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).

[1305] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1306] 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."

[1307] 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.

[1308] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1309] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1310] 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.

[1311] 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.

[1312] 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.

[1313] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1314] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] The following is further disclosed regarding the above embodiment.

[1320] (Claim 1)

[1321] means for a user to upload a photograph of the home appliance;

[1322] A method to analyze the model number, manufacturer, and manufacturing date from the photo of the home appliance on the server,

[1323] A means for collecting price information from related websites based on the analyzed information;

[1324] A means for evaluating the collected information using an evaluation algorithm and listing the most suitable new products;

[1325] A means of summarizing and presenting the listed product features to the user;

[1326] A system including:

[1327] (Claim 2)

[1328] 10. The system of claim 1, further comprising means for providing suggestions according to user preference criteria.

[1329] (Claim 3)

[1330] 10. The system of claim 1, further comprising means for storing the collected information in a database.

[1331] "Example 1"

[1332] (Claim 1)

[1333] means for a user to upload an image of a home appliance;

[1334] A means to analyze the model number, manufacturer, and manufacturing date information from the image of the home appliance on the server,

[1335] means for collecting price data from relevant websites based on the analyzed data;

[1336] A means for evaluating the collected information using an evaluation algorithm and listing the most suitable new products;

[1337] A means for summarizing and suggesting the listed product characteristics to the user;

[1338] A system including:

[1339] (Claim 2)

[1340] 10. The system of claim 1, further comprising means for summarizing the listed product characteristics using a generative AI model to provide recommendations.

[1341] (Claim 3)

[1342] 10. The system of claim 1, further comprising means for storing the collected data in a database.

[1343] "Application Example 1"

[1344] (Claim 1)

[1345] means for a user to upload an image of a home appliance;

[1346] A method for analyzing the model number, manufacturer, and manufacturing date from images of home appliances on a server.

[1347] a means for collecting price information from related online sites based on the analyzed information;

[1348] A means for evaluating the collected information using an evaluation algorithm and listing the most suitable new products;

[1349] A means of summarizing the features of the listed products using a generative AI model and presenting them to the user; and

[1350] means for providing a purchase link for the product selected by the user;

[1351] A system including:

[1352] (Claim 2)

[1353] 10. The system of claim 1, further comprising means for providing suggestions according to user preference criteria.

[1354] (Claim 3)

[1355] 10. The system of claim 1, further comprising means for storing the collected information in a database.

[1356] "Example 2: Combining Emotion Engines"

[1357] (Claim 1)

[1358] A means for users to take and upload photos of home appliances they use;

[1359] A means of capturing the user's voice commands and facial expressions simultaneously with the photograph;

[1360] A means for extracting model numbers, manufacturers, and manufacturing dates from photographs of home appliances on a server;

[1361] a means for collecting price information from related websites based on the analyzed information;

[1362] A means for evaluating the collected information using an evaluation algorithm and listing the most suitable new products;

[1363] a means for summarizing the features of the listed products and generating a presentation based on the user's emotional state;

[1364] means for transmitting the generated presentation materials to a user's terminal and displaying the presentation materials;

[1365] A system including:

[1366] (Claim 2)

[1367] 2. The system according to claim 1, further comprising: means for making suggestions according to a user's desired criteria; and an emotion engine for analyzing the user's emotions and adjusting the suggestions.

[1368] (Claim 3)

[1369] 10. The system of claim 1, further comprising means for storing the collected information in a database.

[1370] "Application example 2 when combining emotion engines"

[1371] (Claim 1)

[1372] means for a user to upload an image of a home appliance;

[1373] A method for analyzing the model number, manufacturer, and manufacturing date from images of home appliances on a server.

[1374] a means for collecting price information from relevant sources based on the analyzed information;

[1375] A means for evaluating the collected information using an evaluation algorithm and listing the most suitable new products;

[1376] A means of summarizing and presenting the listed product features to the user;

[1377] A means for analyzing the user's facial expressions and voice to express emotions;

[1378] A means for adjusting the content of the proposal based on the results of the sentiment analysis;

[1379] A system including:

[1380] (Claim 2)

[1381] 10. The system of claim 1, further comprising means for providing suggestions according to a user's desired criteria and emotional state.

[1382] (Claim 3)

[1383] 10. The system of claim 1, further comprising means for storing the collected information and sentiment analysis results in a database. [Explanation of symbols]

[1384] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for a user to upload a photograph of the home appliance; A method to analyze the model number, manufacturer, and manufacturing date from the photo of the home appliance on the server, A means for collecting price information from related websites based on the analyzed information; A means for evaluating the collected information using an evaluation algorithm and listing the most suitable new products; A means of summarizing and presenting the listed product features to the user; A system including:

2. 10. The system of claim 1, further comprising means for providing suggestions according to user preference criteria.

3. 10. The system of claim 1, further comprising means for storing the collected information in a database.

Images