system

Abstract

We provide the system. [Solution] A means of collecting product data from multiple online shops, A means of analyzing and verbalizing collected data using generating artificial intelligence, A means for generating virtual reality simulation images based on collected data using artificial intelligence, A means for providing the above analysis results and virtual reality simulation images to the user's terminal, A means for receiving product search requests from the user's terminal and displaying the above analysis results and virtual reality simulation images, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In online shopping, when a user compares and considers multiple online stores, there are problems that it takes a great deal of time and effort to collect product information, compare prices, and check reviews. Also, due to the lack of in-store customer service, there is often anxiety when selecting products. Especially when purchasing high-priced products, these problems become more prominent. In addition, there is also a problem that it is difficult to grasp the image when the product is actually installed.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means. Specifically, it provides a system that includes means for collecting product data from multiple online shops, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for providing the analysis results and virtual reality simulation images to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results and virtual reality simulation images. This system enables users to effectively compare information from multiple online shops and enjoy an online shopping experience equivalent to or better than that of a physical store.

[0006] An "online shop" is a website where consumers can search for and purchase products online.

[0007] "Data collection" refers to the process of automatically obtaining product information (such as price, stock availability, reviews, and warranty information) from online shops.

[0008] "Generative artificial intelligence" refers to algorithms and programs that analyze collected data to generate explanations in natural language or create virtual reality simulation images.

[0009] "Analysis" refers to the process of analyzing collected data and extracting meaningful information (for example, price comparisons or review summaries).

[0010] "Verbalization" refers to the process of expressing analysis results in natural language as text in a way that is easy for users to understand.

[0011] A "virtual reality simulation image" is an image created by artificial intelligence based on data, which represents what a product would look like when actually installed.

[0012] "User's device" refers to a computer device operated by the user (for example, a smartphone or personal computer).

[0013] A "search request" is a request that a user sends from their device to obtain information about a specific product.

[0014] "Analysis results" refer to information derived from collected data by the generated artificial intelligence (for example, price comparisons or the advantages / disadvantages of a product).

[0015] "Provision" refers to the process of sending analysis results and virtual reality simulation images to the user's device for display. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0030] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] ---

[0038] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, translates it into language, generates virtual reality simulation images, and provides them to the user.

[0039] System Configuration

[0040] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[0041] System Operation Overview

[0042] 1. The user requests product information.

[0043] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0044] 2. The server collects and analyzes the information.

[0045] The server collects requested product information through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data includes price, stock status, reviews, warranty information, etc. The server filters the collected information, removing duplicate and inaccurate data, and stores it in a database.

[0046] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[0047] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[0048] 3. Provide results to the user.

[0049] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[0050] For example, if a user searches for "the latest 4K TV," the device screen will display price comparisons such as "$999 at online shop A, $950 at online shop B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like installed in their home.

[0051] Furthermore, for products that users are interested in, a link is provided that takes them directly to the corresponding online shop's purchase page. This makes it easy for users to select the most suitable product and complete the purchase process.

[0052] Specific example

[0053] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0054] Online shop A: $1000, in stock, average rating 4.5

[0055] Online shop B: $950, out of stock, average rating 4.3

[0056] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0057] AR simulation image as it would appear when installed in the user's living room.

[0058] This makes it easier for users to compare options and make the best choice quickly.

[0059] As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and sense of security in product selection during online shopping.

[0060] ---

[0061] The above is a description of the "mode for carrying out the invention."

[0062] The following describes the processing flow.

[0063] ---

[0064] Step 1:

[0065] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[0066] Step 2:

[0067] The terminal sends the user's search request to the server. The request includes the product name and category.

[0068] Step 3:

[0069] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[0070] Step 4:

[0071] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[0072] Step 5:

[0073] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[0074] Step 6:

[0075] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[0076] Step 7:

[0077] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[0078] Step 8:

[0079] The server sends the generated analysis results and simulation images to the user's terminal.

[0080] Step 9:

[0081] The terminal displays analysis results and simulation images received from the server to the user. For example, it may display price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[0082] Step 10:

[0083] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[0084] ---

[0085] The above outlines the specific steps involved in the program processing of "EC Personal Assist."

[0086] (Example 1)

[0087] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In online shopping, it is not easy for users to quickly and efficiently select the best products. Currently, it is necessary to individually check multiple e-commerce platforms and manually compare information such as price, availability, and reviews. Another challenge is the difficulty in visualizing how the products will actually be installed. It is necessary to solve these problems and improve the user's purchasing experience.

[0089] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0090] In this invention, the server includes means for collecting product data from multiple e-commerce platforms, means for analyzing the collected data and generating explanations in natural language using artificial intelligence, and means for generating virtual reality simulation images based on the collected data using artificial intelligence. This enables users to quickly and efficiently compare products based on unified information collected from multiple platforms. Furthermore, because the virtual reality simulation images allow users to visually confirm how the products will look when installed, they can make purchasing decisions with greater confidence.

[0091] "Multiple e-commerce platforms" refers to websites and applications used to buy and sell goods and services on multiple internet platforms.

[0092] "Product data" refers to all information related to a specific product, such as price, availability, user reviews, warranty information, and product features.

[0093] "Generative artificial intelligence" refers to an artificial intelligence model that analyzes collected data to generate explanations in natural language or virtual reality simulation images.

[0094] A "virtual reality simulation image" is a virtual visual image of a product as it would appear when actually used or installed, generated based on collected product data.

[0095] A "user's device" refers to a device, such as a smartphone or personal computer, that a user uses to connect to the internet and view and manipulate information.

[0096] "Explanation in natural language" refers to a text-based explanation generated by the artificial intelligence that analyzes the collected data and provides it to the user in an easily understandable format.

[0097] A "prompt message" is an instruction given to an artificial intelligence to perform a specific analysis or generation task.

[0098] "Filtering" is the process of removing duplicate or inaccurate data from collected data.

[0099] "Analysis results" refer to information obtained by analyzing data collected by the generating artificial intelligence, and include price comparisons, review summaries, and product advantages / disadvantages.

[0100] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product-related data from multiple e-commerce platforms, analyzes it using artificial intelligence (AI), translates it into language, generates virtual reality simulation images, and provides them to the user.

[0101] System Configuration

[0102] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[0103] Hardware and software to be used

[0104] User devices: Smartphones and personal computers (iPhone®, Android®, Windows PCs, etc.)

[0105] Servers: Servers for data collection and analysis (AWS®, Google® Cloud, Microsoft® Azure®)

[0106] Artificial intelligence generated: AI modules for natural language processing and virtual reality simulation image generation (GPT-4®, Stable Diffusion, etc.)

[0107] System Operation Overview

[0108] 1. The user requests product information.

[0109] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0110] 2. The server collects and analyzes the information.

[0111] The server collects requested product information through APIs from multiple e-commerce platforms. The collected data includes price, availability, reviews, and warranty information. The server filters the collected information, removing duplicate and inaccurate data before storing it in a database.

[0112] Next, the AI ​​analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. The AI ​​also generates virtual reality simulation images based on the collected data. The following prompts are used for this:

[0113] "Please provide a price comparison and review summary for 4K TVs."

[0114] "Please generate a simulated image of what a 4K TV would look like in a living room."

[0115] 3. Provide results to the user.

[0116] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images. For example, if a user searches for "the latest 4K TV," the terminal screen will display price comparisons such as "$999 on e-commerce platform A, $950 on e-commerce platform B," and descriptions such as "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to get an idea of ​​what the TV would look like in their home.

[0117] Furthermore, for products that users are interested in, a link is provided that takes them directly to the purchase page on the corresponding e-commerce platform. This allows users to easily select the most suitable product and complete the purchase process.

[0118] Specific example

[0119] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0120] E-commerce platform A: $1000, in stock, average rating 4.5

[0121] E-commerce platform B: $950, out of stock, average rating 4.3

[0122] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0123] AR simulation image as it would appear when installed in the user's living room.

[0124] This makes it easier for users to compare and consider options, allowing them to quickly make the best choice. As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and peace of mind of product selection in online shopping.

[0125] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0126] Step 1:

[0127] The user enters the product name or category into the device's search bar and presses the search button. The data entered is the search keyword. The device sends this search request to the server. Here, the input is the search keyword, and the output is sent to the server as an HTTP request.

[0128] Step 2:

[0129] The server sends requests to APIs of multiple e-commerce platforms to collect relevant product data. The input is the data obtained from the APIs, and the output is the collected product data. Specifically, the server retrieves price, inventory status, reviews, warranty information, etc., from each platform and stores it in a database.

[0130] Step 3:

[0131] The server filters the collected data, removing inaccurate and duplicate data. The input is the collected raw data, and the output is the filtered, high-accuracy data. Specifically, it checks for duplicate entries and missing data from the information stored in the database and constructs a normalized dataset.

[0132] Step 4:

[0133] The generating artificial intelligence analyzes the filtered data. The input is product information stored in a database, and the output is price comparisons, review ratings, and product advantages / disadvantages as analysis results. Specifically, the server inputs the following prompts to the generating AI model (e.g., GPT-4):

[0134] "Please provide a price comparison and review summary for 4K TVs."

[0135] Step 5:

[0136] The AI ​​generating the system produces virtual reality simulation images. The input is product information and associated image data retrieved from a database, and the output is a simulated image of what the product would look like if it were installed in the user's environment. Specifically, the server inputs the following prompts to the generating AI model:

[0137] "Please generate a simulated image of what a 4K TV would look like in a living room."

[0138] Step 6:

[0139] The server sends the generated analysis results and simulation images to the user's terminal. The input is the generated analysis results and simulation images, and the output is the data sent to the user's terminal as an HTTP response.

[0140] Step 7:

[0141] The system displays information received by the user's device. Input consists of analysis results and simulation images sent from the server, while output is the information displayed on the user interface. The device displays price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images in a user-friendly format.

[0142] Step 8:

[0143] The user clicks a link to the purchase page for a product they are interested in. The input is the displayed link information, and the output is being redirected to the purchase page of the corresponding e-commerce platform. Specifically, the user clicks the link, and the browser opens the purchase page of the relevant platform.

[0144] The above outlines the specific processing steps of the system.

[0145] (Application Example 1)

[0146] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0147] In modern online shopping, it is extremely time-consuming for users to cross multiple e-commerce sites to select the best product. Furthermore, it is difficult to evaluate individual product information, compare prices, and visualize how the product would look in one's home. Solving these problems is essential.

[0148] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0149] In this invention, the server includes means for collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using artificial intelligence, and means for generating and displaying virtual reality simulation images based on the collected product data. This makes it possible for users to easily compare information from multiple e-commerce sites, check product reviews and prices, and visually understand how the products will look when installed through virtual reality simulation.

[0150] "Multiple e-commerce sites" refers to multiple online platforms or websites, each selling different products, allowing users to purchase those products via the internet.

[0151] "Generative artificial intelligence" refers to algorithms and programs that analyze collected data and generate explanatory text in natural language or virtual reality simulation images.

[0152] "To verbalize" refers to analyzing collected data and describing the results in natural language that is easily understood by humans.

[0153] A "virtual reality simulation image" is an image that visually simulates how a product selected by the user will look in the environment in which it will actually be used.

[0154] "Providing" refers to sending data and simulation images generated by the server to the user's terminal, making them accessible to the user.

[0155] "Displaying" refers to visually presenting data or simulation images on the user's device.

[0156] "Accepting product search requests" refers to receiving requests from users' devices to search for information about specific products.

[0157] "Price comparison information" refers to information that compares the prices of the same product when it is sold on multiple e-commerce sites.

[0158] "Review information" refers to information containing evaluations and opinions from users about products they have purchased, including feedback on product quality and usability.

[0159] This invention relates to a system that generates natural language descriptions and virtual reality simulation images using a generative AI model based on product data collected by the user from multiple e-commerce sites, and provides them to the user's terminal. The detailed configuration and operation of the system are described below.

[0160] System Configuration

[0161] This system primarily consists of user terminals, a server, and generating artificial intelligence (AI) modules. User terminals are computer devices such as smartphones and personal computers, while the server serves as the infrastructure for data collection, analysis, and result distribution. The generating AI analyzes the collected data and is responsible for generating natural language explanations and virtual reality simulation images.

[0162] System Operation Overview

[0163] 1. The user requests product information.

[0164] The user uses the application on their device to enter the name of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0165] 2. The server collects and analyzes the information.

[0166] The server collects requested product information through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the collected information to remove duplicate and inaccurate data and stores it in a database.

[0167] Next, the generative AI model analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the generative AI model generates a user-friendly natural language description. For example, if the generative AI model is given input data such as "Product Name: Sony 4K TV, Price: $999, Rating: 4.5", it will generate a description.

[0168] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[0169] 3. Provide results to the user.

[0170] The server sends back information generated by the AI ​​to the user's terminal. This includes price comparison information for products, review summaries, product advantages / disadvantages, and virtual reality simulation images. The user's terminal displays this information, making it easy for the user to compare and consider options.

[0171] Specifically, when a user searches for "the latest 4K TV," the device screen displays price comparisons such as "$999 on e-commerce site A, $950 on e-commerce site B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like in their home.

[0172] Hardware and software to be used

[0173] This system uses the following hardware and software:

[0174] User's device: Smartphone, PC, etc.

[0175] Server: Foundation for data collection and analysis

[0176] Artificial intelligence generated: OpenAI (registered trademark) API, etc.

[0177] Specific example

[0178] For example, if a user searches for a product called "Sony 4K TV," the server passes the input data to the generating AI model as follows:

[0179] "Product Name: Sony 4K TV, Price: $999, Rating: 4.5"

[0180] The generative AI model uses this to generate the following explanatory text:

[0181] "This television features rich colors and high resolution, making it particularly suitable for watching movies."

[0182] By providing users with this description and virtual reality simulation images, they can quickly select the most suitable product.

[0183] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0184] Step 1:

[0185] The user opens the application on their device and enters the name of the desired product into the search bar. For example, they might enter "latest 4K TV". This search request is sent from the user to the server.

[0186] Input: The name of the product entered by the user.

[0187] Output: Search request from user's terminal to server

[0188] Step 2:

[0189] The server collects data about products requested by users through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the data to remove duplicate and inaccurate data, ensuring clean data.

[0190] Input: Search request sent to the server

[0191] Output: Product data collected from multiple e-commerce sites (price, availability, review information)

[0192] Step 3:

[0193] Next, the server inputs the collected product data into a generative AI model for analysis. The generative AI model compares product prices, aggregates user review ratings, and extracts product advantages and disadvantages. Furthermore, the generative AI model generates natural language descriptions based on these analysis results.

[0194] Input: Filtered product data

[0195] Output: Analyzed product information, generated natural language description

[0196] Step 4:

[0197] The server then generates virtual reality simulation images based on the collected data. These images provide a visual representation of how the product would look when installed in the user's home or usage environment.

[0198] Input: Filtered product data

[0199] Output: Virtual reality simulation image

[0200] Step 5:

[0201] The server sends the description, price comparison information, and virtual reality simulation images generated by the AI ​​model to the user's device. The user's device receives this information and displays it to the user.

[0202] Input: Description text generated by the AI ​​model, price comparison information, and virtual reality simulation images.

[0203] Output: Analysis results and virtual reality simulation images sent to the user's terminal.

[0204] Step 6:

[0205] Users can review and compare product details based on the information displayed on their device screen. Ultimately, they can select the most suitable product and proceed with the purchase.

[0206] Input: Analysis results and virtual reality simulation images displayed on the user's device.

[0207] Output: The user selects the most suitable product and proceeds with the purchase.

[0208] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0209] ---

[0210] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support that responds to the user's emotions. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[0211] System Configuration

[0212] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[0213] System Operation Overview

[0214] 1. The user requests product information.

[0215] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV". The device then sends this search request to the server.

[0216] 2. The server collects and analyzes the information.

[0217] The server collects requested product information (price, stock status, reviews, warranty information, etc.) through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data is stored in a database after removing duplicates and inaccurate information.

[0218] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[0219] Furthermore, the artificial intelligence generates virtual reality simulation images based on the collected data. These images provide an example of what it would look like if, for instance, a 4K television were installed in the user's living room.

[0220] 3. Recognize the user's emotions.

[0221] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional information. This emotional information is passed to the AI ​​that generates product recommendations based on the user's current emotional state.

[0222] 4. Provide results to the user.

[0223] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[0224] For example, if a user searches for "the latest 4K TV" and displays an excited expression, the emotion engine will recognize this excited state, and the generated artificial intelligence will provide explanations and recommendations tailored to that emotion, such as "recommended features for enjoying high-quality video." Similarly, if the user is feeling down, the generated AI will recommend "content recommended for stress relief," enabling the provision of information that takes the user's emotional state into consideration.

[0225] Specific example

[0226] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0227] Online shop A: $1000, in stock, average rating 4.5

[0228] Online shop B: $950, out of stock, average rating 4.3

[0229] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0230] AR simulation image as it would appear when installed in the user's living room.

[0231] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[0232] This makes it easier for users to compare options, make the best choice quickly, and receive personalized information tailored to their emotions, thus further enhancing the purchasing experience.

[0233] As described above, the "EC Personal Assist" system according to the present invention not only significantly improves the efficiency and sense of security in product selection during online shopping, but also provides pinpoint support tailored to the user's emotional state.

[0234] ---

[0235] The above is a description of the "mode for carrying out the invention."

[0236] The following describes the processing flow.

[0237] ---

[0238] Step 1:

[0239] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[0240] Step 2:

[0241] The terminal sends the user's search request to the server. The request includes the product name and category.

[0242] Step 3:

[0243] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[0244] Step 4:

[0245] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[0246] Step 5:

[0247] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[0248] Step 6:

[0249] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[0250] Step 7:

[0251] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[0252] Step 8:

[0253] The server allows the emotion engine to acquire real-time emotional data from the user. The emotion engine analyzes the user's facial expressions and voice to obtain emotional information, such as whether the user is excited or depressed.

[0254] Step 9:

[0255] The artificial intelligence, generated based on emotional data acquired from the emotion engine, provides product recommendations tailored to the user's emotions. If the user expresses positive emotions, it offers product descriptions and options that emphasize enjoyable experiences; if the user expresses negative emotions, it offers products and content that promote relaxation.

[0256] Step 10:

[0257] The server sends the generated analysis results, simulation images, and emotion-based recommendation information to the user's terminal.

[0258] Step 11:

[0259] The terminal displays analysis results received from the server, simulation images, and sentiment-based recommendations to the user. These recommendations may include price comparison tables, review summaries, product pros and cons, virtual reality simulation images, and sentiment-based product recommendations.

[0260] Step 12:

[0261] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[0262] ---

[0263] The above outlines the specific processing steps of the "EC Personal Assist" system, which incorporates an emotion engine.

[0264] (Example 2)

[0265] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0266] Traditional online shopping systems failed to consider the emotions users feel when selecting products, merely providing simple price and review information. As a result, the user's purchasing experience was cold and impersonal, leading to low satisfaction. Furthermore, it was difficult for users to intuitively understand what it would be like to actually use the product. To address this, it is necessary to consider user emotions, provide more personalized support, and improve the purchasing experience.

[0267] The specific processing performed by the specific 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 collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring user emotional information using an emotion engine and passing it to generating artificial intelligence, means for providing the analysis results and virtual reality simulation images to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results and virtual reality simulation images. This makes it possible to provide personalized support that takes into account the user's emotional state and realize an intuitive purchasing experience.

[0268] "Multiple e-commerce sites" refers to a collection of websites or platforms that sell goods and services via the internet.

[0269] "Generative artificial intelligence" refers to a machine learning model that analyzes collected data and generates explanations in natural language or images.

[0270] An "emotion engine" is a combination of software and hardware used to analyze a user's facial expressions and voice to acquire emotional information.

[0271] A "virtual reality simulation image" is an image that generates a visual representation of what a product would look like if it were virtually placed in a real-world environment.

[0272] "User terminal" refers to a computer device used by a user, including smartphones, tablets, and personal computers.

[0273] "Price comparison information" refers to data collected and compared from multiple e-commerce sites regarding the prices and sales conditions of identical or similar products.

[0274] "Review information" refers to ratings and comments posted by users who have previously purchased a product, and is a summary of that information.

[0275] A "search request" is a request in which a user enters the name or category of a product they wish to purchase, and information is collected based on that information.

[0276] "Providing it to the generating artificial intelligence" means providing the emotional information acquired by the emotion engine to the generating artificial intelligence so that it can be used for analysis and information generation.

[0277] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support tailored to the user's emotions. This system collects product data from multiple e-commerce sites, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[0278] System Configuration

[0279] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[0280] Hardware and software to be used

[0281] This invention utilizes the following hardware and software:

[0282] Emotion engine: OpenCV, Dlib

[0283] Data Collection and Analysis: BeautifulSoup, Requests

[0284] Natural Language Processing: NLTK, GPT-3 (registered trademark) (OpenAI)

[0285] Virtual Reality Simulation: Three.js

[0286] Overview of Program Processing

[0287] The operation of this system is as follows.

[0288] The user uses a terminal to enter the name or category of a desired product such as "Sony 4K TV" and sends this as a request to the server. The server collects the requested product information (price, inventory status, reviews, warranty information, etc.) through the APIs of multiple e-commerce sites (for example, E-commerce Site A, E-commerce Site B, E-commerce Site C). The collected data is used with the BeautifulSoup and Requests libraries to remove duplicate and inaccurate information and save it in a database.

[0289] Next, the artificial intelligence to be generated (GPT-3) analyzes the product information stored in the database. This analysis includes price comparison of products, aggregation of evaluations of user reviews, and extraction of merits / demerits of products. Based on the analysis results, the artificial intelligence to be generated generates an easy-to-understand natural language explanation for the user. Also, the artificial intelligence to be generated generates a virtual reality simulation image based on the data collected using Three.js. This image provides an image of "when a Sony 4K TV is installed in the user's living room".

[0290] Furthermore, the emotion engine uses OpenCV and Dlib to analyze the user's expression and voice in real time and obtain emotion information. This emotion information is passed to the artificial intelligence to be generated to make product recommendations based on the user's current emotional state.

[0291] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[0292] Specific example

[0293] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0294] E-commerce site A: $1000, in stock, average rating 4.5

[0295] E-commerce site B: $950, out of stock, average rating 4.3

[0296] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0297] Virtual reality simulation image as it would appear when installed in the user's living room.

[0298] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[0299] Example of a prompt

[0300] The following is an example of a prompt to input into a generative AI model:

[0301] "Please obtain prices and reviews for Sony 4K TVs from various e-commerce sites (e-commerce site A, e-commerce site B, e-commerce site C) and generate comparative information in a user-friendly format. Furthermore, based on sentiment information from the sentiment engine, please provide product recommendations tailored to the user's current emotions."

[0302] The above is a specific implementation example of the "EC Personal Assist" system.

[0303] The flow of the specific process in Example 2 will be described using FIG. 13.

[0304] Step 1:

[0305] The user inputs and transmits product information using the terminal. Specifically, the user enters the product name "Sony 4K TV" that they wish to purchase in the search bar of the dedicated app and presses the send button. This input content is transmitted as a request from the terminal to the server.

[0306] Input: The product name "Sony 4K TV" entered by the user

[0307] Output: The search request transmitted to the server

[0308] Step 2:

[0309] The server collects and organizes product information from multiple e-commerce sites. The server uses the Amazon API, Rakuten API, and Yahoo Shopping API to collect information such as the price, inventory status, and reviews regarding "Sony 4K TV". After that, the collected data is used with the BeautifulSoup and Requests libraries to remove duplicate and inaccurate information, and the organized data is saved in the database.

[0310] Input: The search request based on the product name "Sony 4K TV"

[0311] Output: Organized product information (price, inventory status, reviews)

[0312] Step 3:

[0313] The server uses a generative AI model (GPT-3) to analyze product information. The generative AI model on the server analyzes product information stored in the database, performing price comparisons, summarizing review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, it generates a description in natural language. The server also uses Three.js to generate a virtual reality simulation image of "what if a Sony 4K TV were installed in the user's living room."

[0314] Input: Prepared product information

[0315] Output: Price comparison table, review summary, pros / conveniences, virtual reality simulation images

[0316] Step 4:

[0317] The emotion engine recognizes the user's emotions in real time. Using the OpenCV and Dlib libraries, the emotion engine analyzes the user's facial expressions and voice to obtain emotional information. This emotional information is stored in a database and passed to the generating artificial intelligence.

[0318] Input: User's facial expressions and voice

[0319] Output: Emotional information (e.g., joy, excitement, sadness)

[0320] Step 5:

[0321] The server generates information by combining analysis results and emotional data. The generating AI model takes emotional data into consideration and customizes product recommendations based on the user's current emotional state. For example, if a user searches for "Sony 4K TV" and is excited, it will recommend features that allow them to enjoy high-quality video.

[0322] Input: Analysis results, sentiment information

[0323] Output: Customized recommendations, e.g., "Recommended features for enjoying high-quality video."

[0324] Step 6:

[0325] The server sends generated information and sentiment information to the user's terminal. The server packages the customized information and sentiment information in JSON format and sends it to the user's terminal.

[0326] Input: Customized recommendations, sentiment information

[0327] Output: Information packaged in JSON format

[0328] Step 7:

[0329] The user's device displays the information it receives. The user's device parses the received JSON and converts it into a format that can be displayed in a web browser or application. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations tailored to the user's emotions.

[0330] Input: Information in JSON format

[0331] Output: Information converted into a displayable format (e.g., price comparison table, review summary, virtual reality simulation image, additional recommendation information)

[0332] The above describes the specific processing steps and operation of this system.

[0333] (Application Example 2)

[0334] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0335] Traditional online shopping systems have struggled to automatically analyze user emotions when they access product information and provide customized product recommendations based on that analysis. Furthermore, because they rely solely on simple data analysis without considering user emotions, the user purchasing experience is uniform and lacks individuality. This can lead to decreased user satisfaction and reduced purchasing intent.

[0336] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0337] In this invention, the server includes means for collecting product data from multiple commercial sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring emotional information from the user's facial expressions and voice using an emotion recognition engine, means for generating and providing personalized product recommendation information based on the user's emotional information, means for providing the analysis results, virtual reality simulation images, and product recommendation information based on emotional information to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results, virtual reality simulation images, and product recommendation information based on emotional information. This enables personalized product recommendations that take into account the user's emotional information. As a result, the user's purchasing experience is improved, not only increasing satisfaction but also stimulating purchasing intent.

[0338] "Multiple commercial sites" refers to a general term for websites that sell products on multiple online platforms.

[0339] "Product data" refers to information related to a product, such as price, stock availability, reviews, and warranty information.

[0340] "Generative artificial intelligence" refers to artificial intelligence technology that has the function of analyzing collected data, translating it into language, and generating virtual reality simulation images.

[0341] A "virtual reality simulation image" is an image or video that virtually displays what a product would look like installed in the real world.

[0342] An "emotion recognition engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice.

[0343] "User emotion information" refers to data that indicates the user's current emotional state, obtained by the emotion recognition engine.

[0344] "Personalized product recommendations" refer to product recommendations that are best suited to a specific user, generated by taking into account the user's emotional information.

[0345] "User's device" refers to the computer device used by the user, such as a smartphone or tablet.

[0346] A "search request" refers to an information request based on the product name or category entered by the user in the search bar.

[0347] This invention relates to an "emotion-recognition shopping assist" system that improves the purchasing experience in online shopping. This system combines artificial intelligence (AI) that collects and generates product data from multiple commercial sites with an emotion recognition engine, making it possible to provide personalized product recommendation information based on the user's emotions.

[0348] System Configuration

[0349] This system consists of the following main elements:

[0350] 1. Server

[0351] 2. Artificial Intelligence (AI) Modules to be Generated

[0352] 3. Emotion Recognition Engine

[0353] 4. User terminal

[0354] System Operation Overview

[0355] USER product search

[0356] Users enter product search queries using devices such as smartphones or personal computers. For example, if a user searches for "high-resolution television," their device sends this search request to the server.

[0357] Data collection and analysis

[0358] The server collects product data from multiple commercial websites using APIs. This data includes price, inventory, reviews, and warranty information. The collected data is stored in a database and analyzed by an AI module. The AI ​​module performs price comparisons, review summaries, and extracts pros and cons.

[0359] Generation of virtual reality simulation images

[0360] The AI ​​module further generates virtual reality simulation images. For example, it can generate a simulation image of "what if a high-resolution television were installed in the user's living room."

[0361] emotion recognition

[0362] The emotion recognition engine analyzes the user's facial expressions and voice in real time to obtain the user's emotional state. The acquired emotional information is then passed to the AI ​​module.

[0363] Personalized product recommendations

[0364] The AI ​​module generates personalized product recommendations based on collected product data and emotional information. For example, if a user shows an excited expression, it will recommend a television suitable for enjoying high-definition movies that match that emotion.

[0365] Displaying Results

[0366] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's device. The user's device displays the received information, and the user can receive personalized product recommendations tailored to their emotions.

[0367] Hardware and software used

[0368] Hardware: Servers, user terminals (smartphones, personal computers, etc.)

[0369] Software: AI module, emotion recognition engine, data collection API, database management system

[0370] Specific example

[0371] For example, if a user searches for "high-definition television" and expresses excitement, the following information might be displayed:

[0372] Commercial site A: ¥150,000, in stock, average rating 4.5

[0373] Commercial site B: ¥140,000, out of stock, average rating 4.3

[0374] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0375] Virtual reality simulation image as it would appear when installed in the user's living room.

[0376] Emotion-based recommendation feature

[0377] Examples of prompts for generative AI models

[0378] A user has searched for "high-resolution TV" and is expressing excitement. Generate emotion-based product recommendations for this user.

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] USER product search

[0382] The user uses a terminal to enter a product search query (for example, "high-resolution television"). The terminal sends this search request to the server. The input is the user's product search query, and the output is the search request sent to the server.

[0383] Step 2:

[0384] Data collection

[0385] The server collects product data (price, stock, reviews, warranty information, etc.) through APIs of multiple commercial websites. The collected data is stored in a database. The input is product data obtained from the APIs of multiple commercial websites, and the output is organized product data stored in the database.

[0386] Step 3:

[0387] Data Analysis

[0388] The server analyzes the collected product data using an artificial intelligence (AI) module. This analysis includes price comparison, review summaries, and extraction of product advantages and disadvantages. The input is product data stored in a database, and the output is the analysis results (price comparison information, review summaries, and lists of advantages and disadvantages).

[0389] Step 4:

[0390] Generation of virtual reality simulation images

[0391] The AI ​​generates virtual reality simulation images based on the analyzed data. For example, it generates a simulation image of "a high-resolution television installed in the user's living room." The input is the analysis results and the user's environment information, and the output is the virtual reality simulation image.

[0392] Step 5:

[0393] emotion recognition

[0394] The emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional information (e.g., state of excitement). This emotional information is then passed to the AI ​​module that generates the emotional information. The input is the user's facial expressions and voice data, and the output is emotional information.

[0395] Step 6:

[0396] Generating personalized product recommendations

[0397] The generated AI module produces personalized product recommendations based on collected product data and emotional information. For example, if a user is excited, it selects products and features that match their excited state (e.g., recommending high-definition movies). The input is product data and emotional information, and the output is personalized product recommendations.

[0398] Step 7:

[0399] Displaying Results

[0400] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's terminal. The terminal displays the received information and provides it to the user. The inputs are the analysis results, virtual reality simulation images, and personalized product recommendation information, while the output is the information displayed on the user's terminal.

[0401] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0402] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0404] [Second Embodiment]

[0405] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0406] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0417] ---

[0418] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, translates it into language, generates virtual reality simulation images, and provides them to the user.

[0419] System Configuration

[0420] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[0421] System Operation Overview

[0422] 1. The user requests product information.

[0423] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0424] 2. The server collects and analyzes the information.

[0425] The server collects requested product information through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data includes price, stock status, reviews, warranty information, etc. The server filters the collected information, removing duplicate and inaccurate data, and stores it in a database.

[0426] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[0427] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[0428] 3. Provide results to the user.

[0429] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[0430] For example, if a user searches for "the latest 4K TV," the device screen will display price comparisons such as "$999 at online shop A, $950 at online shop B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like installed in their home.

[0431] Furthermore, for products that users are interested in, a link is provided that takes them directly to the corresponding online shop's purchase page. This makes it easy for users to select the most suitable product and complete the purchase process.

[0432] Specific example

[0433] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0434] Online shop A: $1000, in stock, average rating 4.5

[0435] Online shop B: $950, out of stock, average rating 4.3

[0436] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0437] AR simulation image as it would appear when installed in the user's living room.

[0438] This makes it easier for users to compare options and make the best choice quickly.

[0439] As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and sense of security in product selection during online shopping.

[0440] ---

[0441] The above is a description of the "mode for carrying out the invention."

[0442] The following describes the processing flow.

[0443] ---

[0444] Step 1:

[0445] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[0446] Step 2:

[0447] The terminal sends the user's search request to the server. The request includes the product name and category.

[0448] Step 3:

[0449] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[0450] Step 4:

[0451] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[0452] Step 5:

[0453] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[0454] Step 6:

[0455] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[0456] Step 7:

[0457] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[0458] Step 8:

[0459] The server sends the generated analysis results and simulation images to the user's terminal.

[0460] Step 9:

[0461] The terminal displays analysis results and simulation images received from the server to the user. For example, it may display price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[0462] Step 10:

[0463] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[0464] ---

[0465] The above outlines the specific steps involved in the program processing of "EC Personal Assist."

[0466] (Example 1)

[0467] Next, we will describe Example 1. 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".

[0468] In online shopping, it is not easy for users to quickly and efficiently select the best products. Currently, it is necessary to individually check multiple e-commerce platforms and manually compare information such as price, availability, and reviews. Another challenge is the difficulty in visualizing how the products will actually be installed. It is necessary to solve these problems and improve the user's purchasing experience.

[0469] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0470] In this invention, the server includes means for collecting product data from multiple e-commerce platforms, means for analyzing the collected data and generating explanations in natural language using artificial intelligence, and means for generating virtual reality simulation images based on the collected data using artificial intelligence. This enables users to quickly and efficiently compare products based on unified information collected from multiple platforms. Furthermore, because the virtual reality simulation images allow users to visually confirm how the products will look when installed, they can make purchasing decisions with greater confidence.

[0471] "Multiple e-commerce platforms" refers to websites and applications used to buy and sell goods and services on multiple internet platforms.

[0472] "Product data" refers to all information related to a specific product, such as price, availability, user reviews, warranty information, and product features.

[0473] "Generative artificial intelligence" refers to an artificial intelligence model that analyzes collected data to generate explanations in natural language or virtual reality simulation images.

[0474] A "virtual reality simulation image" is a virtual visual image of a product as it would appear when actually used or installed, generated based on collected product data.

[0475] A "user's device" refers to a device, such as a smartphone or personal computer, that a user uses to connect to the internet and view and manipulate information.

[0476] "Explanation in natural language" refers to a text-based explanation generated by the artificial intelligence that analyzes the collected data and provides it to the user in an easily understandable format.

[0477] A "prompt message" is an instruction given to an artificial intelligence to perform a specific analysis or generation task.

[0478] "Filtering" is the process of removing duplicate or inaccurate data from collected data.

[0479] "Analysis results" refer to information obtained by analyzing data collected by the generating artificial intelligence, and include price comparisons, review summaries, and product advantages / disadvantages.

[0480] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product-related data from multiple e-commerce platforms, analyzes it using artificial intelligence (AI), translates it into language, generates virtual reality simulation images, and provides them to the user.

[0481] System Configuration

[0482] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[0483] Hardware and software to be used

[0484] User devices: Smartphones and personal computers (iPhone, Android, Windows PC, etc.)

[0485] Servers: Servers for data collection and analysis (AWS, Google Cloud, Microsoft Azure)

[0486] Artificial intelligence to be generated: AI modules for natural language processing and virtual reality simulation image generation (GPT-4, Stable Diffusion, etc.)

[0487] System Operation Overview

[0488] 1. The user requests product information.

[0489] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0490] 2. The server collects and analyzes the information.

[0491] The server collects requested product information through APIs from multiple e-commerce platforms. The collected data includes price, availability, reviews, and warranty information. The server filters the collected information, removing duplicate and inaccurate data before storing it in a database.

[0492] Next, the AI ​​analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. The AI ​​also generates virtual reality simulation images based on the collected data. The following prompts are used for this:

[0493] "Please provide a price comparison and review summary for 4K TVs."

[0494] "Please generate a simulated image of what a 4K TV would look like in a living room."

[0495] 3. Provide results to the user.

[0496] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images. For example, if a user searches for "the latest 4K TV," the terminal screen will display price comparisons such as "$999 on e-commerce platform A, $950 on e-commerce platform B," and descriptions such as "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to get an idea of ​​what the TV would look like in their home.

[0497] Furthermore, for products that users are interested in, a link is provided that takes them directly to the purchase page on the corresponding e-commerce platform. This allows users to easily select the most suitable product and complete the purchase process.

[0498] Specific example

[0499] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0500] E-commerce platform A: $1000, in stock, average rating 4.5

[0501] E-commerce platform B: $950, out of stock, average rating 4.3

[0502] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0503] AR simulation image as it would appear when installed in the user's living room.

[0504] This makes it easier for users to compare and consider options, allowing them to quickly make the best choice. As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and peace of mind of product selection in online shopping.

[0505] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0506] Step 1:

[0507] The user enters the product name or category into the device's search bar and presses the search button. The data entered is the search keyword. The device sends this search request to the server. Here, the input is the search keyword, and the output is sent to the server as an HTTP request.

[0508] Step 2:

[0509] The server sends requests to APIs of multiple e-commerce platforms to collect relevant product data. The input is the data obtained from the APIs, and the output is the collected product data. Specifically, the server retrieves price, inventory status, reviews, warranty information, etc., from each platform and stores it in a database.

[0510] Step 3:

[0511] The server filters the collected data, removing inaccurate and duplicate data. The input is the collected raw data, and the output is the filtered, high-accuracy data. Specifically, it checks for duplicate entries and missing data from the information stored in the database and constructs a normalized dataset.

[0512] Step 4:

[0513] The generating artificial intelligence analyzes the filtered data. The input is product information stored in a database, and the output is price comparisons, review ratings, and product advantages / disadvantages as analysis results. Specifically, the server inputs the following prompts to the generating AI model (e.g., GPT-4):

[0514] "Please provide a price comparison and review summary for 4K TVs."

[0515] Step 5:

[0516] The AI ​​generating the system produces virtual reality simulation images. The input is product information and associated image data retrieved from a database, and the output is a simulated image of what the product would look like if it were installed in the user's environment. Specifically, the server inputs the following prompts to the generating AI model:

[0517] "Please generate a simulated image of what a 4K TV would look like in a living room."

[0518] Step 6:

[0519] The server sends the generated analysis results and simulation images to the user's terminal. The input is the generated analysis results and simulation images, and the output is the data sent to the user's terminal as an HTTP response.

[0520] Step 7:

[0521] The system displays information received by the user's device. Input consists of analysis results and simulation images sent from the server, while output is the information displayed on the user interface. The device displays price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images in a user-friendly format.

[0522] Step 8:

[0523] The user clicks a link to the purchase page for a product they are interested in. The input is the displayed link information, and the output is being redirected to the purchase page of the corresponding e-commerce platform. Specifically, the user clicks the link, and the browser opens the purchase page of the relevant platform.

[0524] The above outlines the specific processing steps of the system.

[0525] (Application Example 1)

[0526] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0527] In modern online shopping, it is extremely time-consuming for users to cross multiple e-commerce sites to select the best product. Furthermore, it is difficult to evaluate individual product information, compare prices, and visualize how the product would look in one's home. Solving these problems is essential.

[0528] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0529] In this invention, the server includes means for collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using artificial intelligence, and means for generating and displaying virtual reality simulation images based on the collected product data. This makes it possible for users to easily compare information from multiple e-commerce sites, check product reviews and prices, and visually understand how the products will look when installed through virtual reality simulation.

[0530] "Multiple e-commerce sites" refers to multiple online platforms or websites, each selling different products, allowing users to purchase those products via the internet.

[0531] "Generative artificial intelligence" refers to algorithms and programs that analyze collected data and generate explanatory text in natural language or virtual reality simulation images.

[0532] "To verbalize" refers to analyzing collected data and describing the results in natural language that is easily understood by humans.

[0533] A "virtual reality simulation image" is an image that visually simulates how a product selected by the user will look in the environment in which it will actually be used.

[0534] "Providing" refers to sending data and simulation images generated by the server to the user's terminal, making them accessible to the user.

[0535] "Displaying" refers to visually presenting data or simulation images on the user's device.

[0536] "Accepting product search requests" refers to receiving requests from users' devices to search for information about specific products.

[0537] "Price comparison information" refers to information that compares the prices of the same product when it is sold on multiple e-commerce sites.

[0538] "Review information" refers to information containing evaluations and opinions from users about products they have purchased, including feedback on product quality and usability.

[0539] This invention relates to a system that generates natural language descriptions and virtual reality simulation images using a generative AI model based on product data collected by the user from multiple e-commerce sites, and provides them to the user's terminal. The detailed configuration and operation of the system are described below.

[0540] System Configuration

[0541] This system primarily consists of user terminals, a server, and generating artificial intelligence (AI) modules. User terminals are computer devices such as smartphones and personal computers, while the server serves as the infrastructure for data collection, analysis, and result distribution. The generating AI analyzes the collected data and is responsible for generating natural language explanations and virtual reality simulation images.

[0542] System Operation Overview

[0543] 1. The user requests product information.

[0544] The user uses the application on their device to enter the name of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0545] 2. The server collects and analyzes the information.

[0546] The server collects requested product information through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the collected information to remove duplicate and inaccurate data and stores it in a database.

[0547] Next, the generative AI model analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the generative AI model generates a user-friendly natural language description. For example, if the generative AI model is given input data such as "Product Name: Sony 4K TV, Price: $999, Rating: 4.5", it will generate a description.

[0548] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[0549] 3. Provide results to the user.

[0550] The server sends back information generated by the AI ​​to the user's terminal. This includes price comparison information for products, review summaries, product advantages / disadvantages, and virtual reality simulation images. The user's terminal displays this information, making it easy for the user to compare and consider options.

[0551] Specifically, when a user searches for "the latest 4K TV," the device screen displays price comparisons such as "$999 on e-commerce site A, $950 on e-commerce site B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like in their home.

[0552] Hardware and software to be used

[0553] This system uses the following hardware and software:

[0554] User's device: Smartphone, PC, etc.

[0555] Server: Foundation for data collection and analysis

[0556] Artificial intelligence to be generated: OpenAI API, etc.

[0557] Specific example

[0558] For example, if a user searches for a product called "Sony 4K TV," the server passes the input data to the generating AI model as follows:

[0559] "Product Name: Sony 4K TV, Price: $999, Rating: 4.5"

[0560] The generative AI model uses this to generate the following explanatory text:

[0561] "This television features rich colors and high resolution, making it particularly suitable for watching movies."

[0562] By providing users with this description and virtual reality simulation images, they can quickly select the most suitable product.

[0563] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0564] Step 1:

[0565] The user opens the application on their device and enters the name of the desired product into the search bar. For example, they might enter "latest 4K TV". This search request is sent from the user to the server.

[0566] Input: The name of the product entered by the user.

[0567] Output: Search request from user's terminal to server

[0568] Step 2:

[0569] The server collects data about products requested by users through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the data to remove duplicate and inaccurate data, ensuring clean data.

[0570] Input: Search request sent to the server

[0571] Output: Product data collected from multiple e-commerce sites (price, availability, review information)

[0572] Step 3:

[0573] Next, the server inputs the collected product data into a generative AI model for analysis. The generative AI model compares product prices, aggregates user review ratings, and extracts product advantages and disadvantages. Furthermore, the generative AI model generates natural language descriptions based on these analysis results.

[0574] Input: Filtered product data

[0575] Output: Analyzed product information, generated natural language description

[0576] Step 4:

[0577] The server then generates virtual reality simulation images based on the collected data. These images provide a visual representation of how the product would look when installed in the user's home or usage environment.

[0578] Input: Filtered product data

[0579] Output: Virtual reality simulation image

[0580] Step 5:

[0581] The server sends the description, price comparison information, and virtual reality simulation images generated by the AI ​​model to the user's device. The user's device receives this information and displays it to the user.

[0582] Input: Description text generated by the AI ​​model, price comparison information, and virtual reality simulation images.

[0583] Output: Analysis results and virtual reality simulation images sent to the user's terminal.

[0584] Step 6:

[0585] Users can review and compare product details based on the information displayed on their device screen. Ultimately, they can select the most suitable product and proceed with the purchase.

[0586] Input: Analysis results and virtual reality simulation images displayed on the user's device.

[0587] Output: The user selects the most suitable product and proceeds with the purchase.

[0588] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0589] ---

[0590] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support that responds to the user's emotions. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[0591] System Configuration

[0592] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[0593] System Operation Overview

[0594] 1. The user requests product information.

[0595] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV". The device then sends this search request to the server.

[0596] 2. The server collects and analyzes the information.

[0597] The server collects requested product information (price, stock status, reviews, warranty information, etc.) through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data is stored in a database after removing duplicates and inaccurate information.

[0598] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[0599] Furthermore, the artificial intelligence generates virtual reality simulation images based on the collected data. These images provide an example of what it would look like if, for instance, a 4K television were installed in the user's living room.

[0600] 3. Recognize the user's emotions.

[0601] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional information. This emotional information is passed to the AI ​​that generates product recommendations based on the user's current emotional state.

[0602] 4. Provide results to the user.

[0603] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[0604] For example, if a user searches for "the latest 4K TV" and displays an excited expression, the emotion engine will recognize this excited state, and the generated artificial intelligence will provide explanations and recommendations tailored to that emotion, such as "recommended features for enjoying high-quality video." Similarly, if the user is feeling down, the generated AI will recommend "content recommended for stress relief," enabling the provision of information that takes the user's emotional state into consideration.

[0605] Specific example

[0606] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0607] Online shop A: $1000, in stock, average rating 4.5

[0608] Online shop B: $950, out of stock, average rating 4.3

[0609] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0610] AR simulation image as it would appear when installed in the user's living room.

[0611] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[0612] This makes it easier for users to compare options, make the best choice quickly, and receive personalized information tailored to their emotions, thus further enhancing the purchasing experience.

[0613] As described above, the "EC Personal Assist" system according to the present invention not only significantly improves the efficiency and sense of security in product selection during online shopping, but also provides pinpoint support tailored to the user's emotional state.

[0614] ---

[0615] The above is a description of the "mode for carrying out the invention."

[0616] The following describes the processing flow.

[0617] ---

[0618] Step 1:

[0619] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[0620] Step 2:

[0621] The terminal sends the user's search request to the server. The request includes the product name and category.

[0622] Step 3:

[0623] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[0624] Step 4:

[0625] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[0626] Step 5:

[0627] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[0628] Step 6:

[0629] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[0630] Step 7:

[0631] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[0632] Step 8:

[0633] The server allows the emotion engine to acquire real-time emotional data from the user. The emotion engine analyzes the user's facial expressions and voice to obtain emotional information, such as whether the user is excited or depressed.

[0634] Step 9:

[0635] The artificial intelligence, generated based on emotional data acquired from the emotion engine, provides product recommendations tailored to the user's emotions. If the user expresses positive emotions, it offers product descriptions and options that emphasize enjoyable experiences; if the user expresses negative emotions, it offers products and content that promote relaxation.

[0636] Step 10:

[0637] The server sends the generated analysis results, simulation images, and emotion-based recommendation information to the user's terminal.

[0638] Step 11:

[0639] The terminal displays analysis results received from the server, simulation images, and sentiment-based recommendations to the user. These recommendations may include price comparison tables, review summaries, product pros and cons, virtual reality simulation images, and sentiment-based product recommendations.

[0640] Step 12:

[0641] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[0642] ---

[0643] The above outlines the specific processing steps of the "EC Personal Assist" system, which incorporates an emotion engine.

[0644] (Example 2)

[0645] Next, we will describe Example 2. 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".

[0646] Traditional online shopping systems failed to consider the emotions users feel when selecting products, merely providing simple price and review information. As a result, the user's purchasing experience was cold and impersonal, leading to low satisfaction. Furthermore, it was difficult for users to intuitively understand what it would be like to actually use the product. To address this, it is necessary to consider user emotions, provide more personalized support, and improve the purchasing experience.

[0647] The specific processing performed by the specific 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 collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring user emotional information using an emotion engine and passing it to generating artificial intelligence, means for providing the analysis results and virtual reality simulation images to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results and virtual reality simulation images. This makes it possible to provide personalized support that takes into account the user's emotional state and realize an intuitive purchasing experience.

[0648] "Multiple e-commerce sites" refers to a collection of websites or platforms that sell goods and services via the internet.

[0649] "Generative artificial intelligence" refers to a machine learning model that analyzes collected data and generates explanations in natural language or images.

[0650] An "emotion engine" is a combination of software and hardware used to analyze a user's facial expressions and voice to acquire emotional information.

[0651] A "virtual reality simulation image" is an image that generates a visual representation of what a product would look like if it were virtually placed in a real-world environment.

[0652] "User terminal" refers to a computer device used by a user, including smartphones, tablets, and personal computers.

[0653] "Price comparison information" refers to data collected and compared from multiple e-commerce sites regarding the prices and sales conditions of identical or similar products.

[0654] "Review information" refers to ratings and comments posted by users who have previously purchased a product, and is a summary of that information.

[0655] A "search request" is a request in which a user enters the name or category of a product they wish to purchase, and information is collected based on that information.

[0656] "Providing it to the generating artificial intelligence" means providing the emotional information acquired by the emotion engine to the generating artificial intelligence so that it can be used for analysis and information generation.

[0657] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support tailored to the user's emotions. This system collects product data from multiple e-commerce sites, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[0658] System Configuration

[0659] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[0660] Hardware and software to be used

[0661] This invention utilizes the following hardware and software:

[0662] Emotion engine: OpenCV, Dlib

[0663] Data collection and analysis: BeautifulSoup, Requests

[0664] Natural Language Processing: NLTK, GPT-3 (OpenAI)

[0665] Virtual reality simulation: Three.js

[0666] Overview of program processing

[0667] The operation of this system is as follows:

[0668] The user uses their device to enter the name and category of the product they wish to purchase, such as "Sony 4K TV," and sends this as a request to the server. The server collects the requested product information (price, availability, reviews, warranty information, etc.) through APIs of multiple e-commerce sites (e.g., e-commerce site A, e-commerce site B, e-commerce site C). The collected data is then filtered for duplication and inaccuracies using the BeautifulSoup and Requests libraries and stored in a database.

[0669] Next, the AI ​​(GPT-3) analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. Furthermore, the AI ​​generates a virtual reality simulation image based on the collected data using Three.js. This image provides an image of what a Sony 4K TV would look like if it were installed in the user's living room.

[0670] Furthermore, the emotion engine uses OpenCV and Dlib to analyze the user's facial expressions and voice in real time and acquire emotional information. This emotional information is passed to the generating artificial intelligence, which then makes product recommendations based on the user's current emotional state.

[0671] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[0672] Specific example

[0673] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0674] E-commerce site A: $1000, in stock, average rating 4.5

[0675] E-commerce site B: $950, out of stock, average rating 4.3

[0676] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0677] Virtual reality simulation image as it would appear when installed in the user's living room.

[0678] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[0679] Example of a prompt

[0680] The following is an example of a prompt to input into a generative AI model:

[0681] "Please obtain prices and reviews for Sony 4K TVs from various e-commerce sites (e-commerce site A, e-commerce site B, e-commerce site C) and generate comparative information in a user-friendly format. Furthermore, based on sentiment information from the sentiment engine, please provide product recommendations tailored to the user's current emotions."

[0682] The above is a specific implementation example of the "EC Personal Assist" system.

[0683] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0684] Step 1:

[0685] The user enters and submits product information using their device. Specifically, the user enters the name of the product they wish to purchase, "Sony 4K TV," into the search bar of the dedicated app and presses the submit button. This input is then sent from the device to the server as a request.

[0686] Input: User-entered product name "Sony 4K TV"

[0687] Output: Search request sent to the server

[0688] Step 2:

[0689] The server collects and organizes product information from multiple e-commerce sites. The server uses the Amazon API, Rakuten API, and Yahoo Shopping API to collect information such as price, availability, and reviews for "Sony 4K TV." The collected data is then filtered for duplication and inaccuracies using the BeautifulSoup and Requests libraries, and the organized data is stored in a database.

[0690] Input: Search request based on product name "Sony 4K TV"

[0691] Output: Organized product information (price, availability, reviews)

[0692] Step 3:

[0693] The server uses a generative AI model (GPT-3) to analyze product information. The generative AI model on the server analyzes product information stored in the database, performing price comparisons, summarizing review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, it generates a description in natural language. The server also uses Three.js to generate a virtual reality simulation image of "what if a Sony 4K TV were installed in the user's living room."

[0694] Input: Prepared product information

[0695] Output: Price comparison table, review summary, pros / conveniences, virtual reality simulation images

[0696] Step 4:

[0697] The emotion engine recognizes the user's emotions in real time. Using the OpenCV and Dlib libraries, the emotion engine analyzes the user's facial expressions and voice to obtain emotional information. This emotional information is stored in a database and passed to the generating artificial intelligence.

[0698] Input: User's facial expressions and voice

[0699] Output: Emotional information (e.g., joy, excitement, sadness)

[0700] Step 5:

[0701] The server generates information by combining analysis results and emotional data. The generating AI model takes emotional data into consideration and customizes product recommendations based on the user's current emotional state. For example, if a user searches for "Sony 4K TV" and is excited, it will recommend features that allow them to enjoy high-quality video.

[0702] Input: Analysis results, sentiment information

[0703] Output: Customized recommendations, e.g., "Recommended features for enjoying high-quality video."

[0704] Step 6:

[0705] The server sends generated information and sentiment information to the user's terminal. The server packages the customized information and sentiment information in JSON format and sends it to the user's terminal.

[0706] Input: Customized recommendations, sentiment information

[0707] Output: Information packaged in JSON format

[0708] Step 7:

[0709] The user's device displays the information it receives. The user's device parses the received JSON and converts it into a format that can be displayed in a web browser or application. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations tailored to the user's emotions.

[0710] Input: Information in JSON format

[0711] Output: Information converted into a displayable format (e.g., price comparison table, review summary, virtual reality simulation image, additional recommendation information)

[0712] The above describes the specific processing steps and operation of this system.

[0713] (Application Example 2)

[0714] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0715] Traditional online shopping systems have struggled to automatically analyze user emotions when they access product information and provide customized product recommendations based on that analysis. Furthermore, because they rely solely on simple data analysis without considering user emotions, the user purchasing experience is uniform and lacks individuality. This can lead to decreased user satisfaction and reduced purchasing intent.

[0716] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0717] In this invention, the server includes means for collecting product data from multiple commercial sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring emotional information from the user's facial expressions and voice using an emotion recognition engine, means for generating and providing personalized product recommendation information based on the user's emotional information, means for providing the analysis results, virtual reality simulation images, and product recommendation information based on emotional information to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results, virtual reality simulation images, and product recommendation information based on emotional information. This enables personalized product recommendations that take into account the user's emotional information. As a result, the user's purchasing experience is improved, not only increasing satisfaction but also stimulating purchasing intent.

[0718] "Multiple commercial sites" refers to a general term for websites that sell products on multiple online platforms.

[0719] "Product data" refers to information related to a product, such as price, stock availability, reviews, and warranty information.

[0720] "Generative artificial intelligence" refers to artificial intelligence technology that has the function of analyzing collected data, translating it into language, and generating virtual reality simulation images.

[0721] A "virtual reality simulation image" is an image or video that virtually displays what a product would look like installed in the real world.

[0722] An "emotion recognition engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice.

[0723] "User emotion information" refers to data that indicates the user's current emotional state, obtained by the emotion recognition engine.

[0724] "Personalized product recommendations" refer to product recommendations that are best suited to a specific user, generated by taking into account the user's emotional information.

[0725] "User's device" refers to the computer device used by the user, such as a smartphone or tablet.

[0726] A "search request" refers to an information request based on the product name or category entered by the user in the search bar.

[0727] This invention relates to an "emotion-recognition shopping assist" system that improves the purchasing experience in online shopping. This system combines artificial intelligence (AI) that collects and generates product data from multiple commercial sites with an emotion recognition engine, making it possible to provide personalized product recommendation information based on the user's emotions.

[0728] System Configuration

[0729] This system consists of the following main elements:

[0730] 1. Server

[0731] 2. Artificial Intelligence (AI) Modules to be Generated

[0732] 3. Emotion Recognition Engine

[0733] 4. User terminal

[0734] System Operation Overview

[0735] USER product search

[0736] Users enter product search queries using devices such as smartphones or personal computers. For example, if a user searches for "high-resolution television," their device sends this search request to the server.

[0737] Data collection and analysis

[0738] The server collects product data from multiple commercial websites using APIs. This data includes price, inventory, reviews, and warranty information. The collected data is stored in a database and analyzed by an AI module. The AI ​​module performs price comparisons, review summaries, and extracts pros and cons.

[0739] Generation of virtual reality simulation images

[0740] The AI ​​module further generates virtual reality simulation images. For example, it can generate a simulation image of "what if a high-resolution television were installed in the user's living room."

[0741] emotion recognition

[0742] The emotion recognition engine analyzes the user's facial expressions and voice in real time to obtain the user's emotional state. The acquired emotional information is then passed to the AI ​​module.

[0743] Personalized product recommendations

[0744] The AI ​​module generates personalized product recommendations based on collected product data and emotional information. For example, if a user shows an excited expression, it will recommend a television suitable for enjoying high-definition movies that match that emotion.

[0745] Displaying Results

[0746] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's device. The user's device displays the received information, and the user can receive personalized product recommendations tailored to their emotions.

[0747] Hardware and software used

[0748] Hardware: Servers, user terminals (smartphones, personal computers, etc.)

[0749] Software: AI module, emotion recognition engine, data collection API, database management system

[0750] Specific example

[0751] For example, if a user searches for "high-definition television" and expresses excitement, the following information might be displayed:

[0752] Commercial site A: ¥150,000, in stock, average rating 4.5

[0753] Commercial site B: ¥140,000, out of stock, average rating 4.3

[0754] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0755] Virtual reality simulation image as it would appear when installed in the user's living room.

[0756] Emotion-based recommendation feature

[0757] Examples of prompts for generative AI models

[0758] A user has searched for "high-resolution TV" and is expressing excitement. Generate emotion-based product recommendations for this user.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] USER product search

[0762] The user uses a terminal to enter a product search query (for example, "high-resolution television"). The terminal sends this search request to the server. The input is the user's product search query, and the output is the search request sent to the server.

[0763] Step 2:

[0764] Data collection

[0765] The server collects product data (price, stock, reviews, warranty information, etc.) through APIs of multiple commercial websites. The collected data is stored in a database. The input is product data obtained from the APIs of multiple commercial websites, and the output is organized product data stored in the database.

[0766] Step 3:

[0767] Data Analysis

[0768] The server analyzes the collected product data using an artificial intelligence (AI) module. This analysis includes price comparison, review summaries, and extraction of product advantages and disadvantages. The input is product data stored in a database, and the output is the analysis results (price comparison information, review summaries, and lists of advantages and disadvantages).

[0769] Step 4:

[0770] Generation of virtual reality simulation images

[0771] The AI ​​generates virtual reality simulation images based on the analyzed data. For example, it generates a simulation image of "a high-resolution television installed in the user's living room." The input is the analysis results and the user's environment information, and the output is the virtual reality simulation image.

[0772] Step 5:

[0773] emotion recognition

[0774] The emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional information (e.g., state of excitement). This emotional information is then passed to the AI ​​module that generates the emotional information. The input is the user's facial expressions and voice data, and the output is emotional information.

[0775] Step 6:

[0776] Generating personalized product recommendations

[0777] The generated AI module produces personalized product recommendations based on collected product data and emotional information. For example, if a user is excited, it selects products and features that match their excited state (e.g., recommending high-definition movies). The input is product data and emotional information, and the output is personalized product recommendations.

[0778] Step 7:

[0779] Displaying Results

[0780] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's terminal. The terminal displays the received information and provides it to the user. The inputs are the analysis results, virtual reality simulation images, and personalized product recommendation information, while the output is the information displayed on the user's terminal.

[0781] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0782] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0783] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0784] [Third Embodiment]

[0785] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0786] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0787] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0788] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0789] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0790] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0791] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0792] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0793] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0794] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0795] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0796] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0797] ---

[0798] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, translates it into language, generates virtual reality simulation images, and provides them to the user.

[0799] System Configuration

[0800] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[0801] System Operation Overview

[0802] 1. The user requests product information.

[0803] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0804] 2. The server collects and analyzes the information.

[0805] The server collects requested product information through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data includes price, stock status, reviews, warranty information, etc. The server filters the collected information, removing duplicate and inaccurate data, and stores it in a database.

[0806] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[0807] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[0808] 3. Provide results to the user.

[0809] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[0810] For example, if a user searches for "the latest 4K TV," the device screen will display price comparisons such as "$999 at online shop A, $950 at online shop B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like installed in their home.

[0811] Furthermore, for products that users are interested in, a link is provided that takes them directly to the corresponding online shop's purchase page. This makes it easy for users to select the most suitable product and complete the purchase process.

[0812] Specific example

[0813] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0814] Online shop A: $1000, in stock, average rating 4.5

[0815] Online shop B: $950, out of stock, average rating 4.3

[0816] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0817] AR simulation image as it would appear when installed in the user's living room.

[0818] This makes it easier for users to compare options and make the best choice quickly.

[0819] As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and sense of security in product selection during online shopping.

[0820] ---

[0821] The above is a description of the "mode for carrying out the invention."

[0822] The following describes the processing flow.

[0823] ---

[0824] Step 1:

[0825] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[0826] Step 2:

[0827] The terminal sends the user's search request to the server. The request includes the product name and category.

[0828] Step 3:

[0829] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[0830] Step 4:

[0831] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[0832] Step 5:

[0833] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[0834] Step 6:

[0835] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[0836] Step 7:

[0837] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[0838] Step 8:

[0839] The server sends the generated analysis results and simulation images to the user's terminal.

[0840] Step 9:

[0841] The terminal displays analysis results and simulation images received from the server to the user. For example, it may display price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[0842] Step 10:

[0843] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[0844] ---

[0845] The above outlines the specific steps involved in the program processing of "EC Personal Assist."

[0846] (Example 1)

[0847] Next, we will describe Example 1. 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."

[0848] In online shopping, it is not easy for users to quickly and efficiently select the best products. Currently, it is necessary to individually check multiple e-commerce platforms and manually compare information such as price, availability, and reviews. Another challenge is the difficulty in visualizing how the products will actually be installed. It is necessary to solve these problems and improve the user's purchasing experience.

[0849] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0850] In this invention, the server includes means for collecting product data from multiple e-commerce platforms, means for analyzing the collected data and generating explanations in natural language using artificial intelligence, and means for generating virtual reality simulation images based on the collected data using artificial intelligence. This enables users to quickly and efficiently compare products based on unified information collected from multiple platforms. Furthermore, because the virtual reality simulation images allow users to visually confirm how the products will look when installed, they can make purchasing decisions with greater confidence.

[0851] "Multiple e-commerce platforms" refers to websites and applications used to buy and sell goods and services on multiple internet platforms.

[0852] "Product data" refers to all information related to a specific product, such as price, availability, user reviews, warranty information, and product features.

[0853] "Generative artificial intelligence" refers to an artificial intelligence model that analyzes collected data to generate explanations in natural language or virtual reality simulation images.

[0854] A "virtual reality simulation image" is a virtual visual image of a product as it would appear when actually used or installed, generated based on collected product data.

[0855] A "user's device" refers to a device, such as a smartphone or personal computer, that a user uses to connect to the internet and view and manipulate information.

[0856] "Explanation in natural language" refers to a text-based explanation generated by the artificial intelligence that analyzes the collected data and provides it to the user in an easily understandable format.

[0857] A "prompt message" is an instruction given to an artificial intelligence to perform a specific analysis or generation task.

[0858] "Filtering" is the process of removing duplicate or inaccurate data from collected data.

[0859] "Analysis results" refer to information obtained by analyzing data collected by the generating artificial intelligence, and include price comparisons, review summaries, and product advantages / disadvantages.

[0860] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product-related data from multiple e-commerce platforms, analyzes it using artificial intelligence (AI), translates it into language, generates virtual reality simulation images, and provides them to the user.

[0861] System Configuration

[0862] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[0863] Hardware and software to be used

[0864] User devices: Smartphones and personal computers (iPhone, Android, Windows PC, etc.)

[0865] Servers: Servers for data collection and analysis (AWS, Google Cloud, Microsoft Azure)

[0866] Artificial intelligence to be generated: AI modules for natural language processing and virtual reality simulation image generation (GPT-4, Stable Diffusion, etc.)

[0867] System Operation Overview

[0868] 1. The user requests product information.

[0869] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0870] 2. The server collects and analyzes the information.

[0871] The server collects requested product information through APIs from multiple e-commerce platforms. The collected data includes price, availability, reviews, and warranty information. The server filters the collected information, removing duplicate and inaccurate data before storing it in a database.

[0872] Next, the AI ​​analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. The AI ​​also generates virtual reality simulation images based on the collected data. The following prompts are used for this:

[0873] "Please provide a price comparison and review summary for 4K TVs."

[0874] "Please generate a simulated image of what a 4K TV would look like in a living room."

[0875] 3. Provide results to the user.

[0876] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images. For example, if a user searches for "the latest 4K TV," the terminal screen will display price comparisons such as "$999 on e-commerce platform A, $950 on e-commerce platform B," and descriptions such as "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to get an idea of ​​what the TV would look like in their home.

[0877] Furthermore, for products that users are interested in, a link is provided that takes them directly to the purchase page on the corresponding e-commerce platform. This allows users to easily select the most suitable product and complete the purchase process.

[0878] Specific example

[0879] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0880] E-commerce platform A: $1000, in stock, average rating 4.5

[0881] E-commerce platform B: $950, out of stock, average rating 4.3

[0882] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0883] AR simulation image as it would appear when installed in the user's living room.

[0884] This makes it easier for users to compare and consider options, allowing them to quickly make the best choice. As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and peace of mind of product selection in online shopping.

[0885] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0886] Step 1:

[0887] The user enters the product name or category into the device's search bar and presses the search button. The data entered is the search keyword. The device sends this search request to the server. Here, the input is the search keyword, and the output is sent to the server as an HTTP request.

[0888] Step 2:

[0889] The server sends requests to APIs of multiple e-commerce platforms to collect relevant product data. The input is the data obtained from the APIs, and the output is the collected product data. Specifically, the server retrieves price, inventory status, reviews, warranty information, etc., from each platform and stores it in a database.

[0890] Step 3:

[0891] The server filters the collected data, removing inaccurate and duplicate data. The input is the collected raw data, and the output is the filtered, high-accuracy data. Specifically, it checks for duplicate entries and missing data from the information stored in the database and constructs a normalized dataset.

[0892] Step 4:

[0893] The generating artificial intelligence analyzes the filtered data. The input is product information stored in a database, and the output is price comparisons, review ratings, and product advantages / disadvantages as analysis results. Specifically, the server inputs the following prompts to the generating AI model (e.g., GPT-4):

[0894] "Please provide a price comparison and review summary for 4K TVs."

[0895] Step 5:

[0896] The AI ​​generating the system produces virtual reality simulation images. The input is product information and associated image data retrieved from a database, and the output is a simulated image of what the product would look like if it were installed in the user's environment. Specifically, the server inputs the following prompts to the generating AI model:

[0897] "Please generate a simulated image of what a 4K TV would look like in a living room."

[0898] Step 6:

[0899] The server sends the generated analysis results and simulation images to the user's terminal. The input is the generated analysis results and simulation images, and the output is the data sent to the user's terminal as an HTTP response.

[0900] Step 7:

[0901] The system displays information received by the user's device. Input consists of analysis results and simulation images sent from the server, while output is the information displayed on the user interface. The device displays price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images in a user-friendly format.

[0902] Step 8:

[0903] The user clicks a link to the purchase page for a product they are interested in. The input is the displayed link information, and the output is being redirected to the purchase page of the corresponding e-commerce platform. Specifically, the user clicks the link, and the browser opens the purchase page of the relevant platform.

[0904] The above outlines the specific processing steps of the system.

[0905] (Application Example 1)

[0906] Next, we will explain Application Example 1. In the following explanation, 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."

[0907] In modern online shopping, it is extremely time-consuming for users to cross multiple e-commerce sites to select the best product. Furthermore, it is difficult to evaluate individual product information, compare prices, and visualize how the product would look in one's home. Solving these problems is essential.

[0908] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0909] In this invention, the server includes means for collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using artificial intelligence, and means for generating and displaying virtual reality simulation images based on the collected product data. This makes it possible for users to easily compare information from multiple e-commerce sites, check product reviews and prices, and visually understand how the products will look when installed through virtual reality simulation.

[0910] "Multiple e-commerce sites" refers to multiple online platforms or websites, each selling different products, allowing users to purchase those products via the internet.

[0911] "Generative artificial intelligence" refers to algorithms and programs that analyze collected data and generate explanatory text in natural language or virtual reality simulation images.

[0912] "To verbalize" refers to analyzing collected data and describing the results in natural language that is easily understood by humans.

[0913] A "virtual reality simulation image" is an image that visually simulates how a product selected by the user will look in the environment in which it will actually be used.

[0914] "Providing" refers to sending data and simulation images generated by the server to the user's terminal, making them accessible to the user.

[0915] "Displaying" refers to visually presenting data or simulation images on the user's device.

[0916] "Accepting product search requests" refers to receiving requests from users' devices to search for information about specific products.

[0917] "Price comparison information" refers to information that compares the prices of the same product when it is sold on multiple e-commerce sites.

[0918] "Review information" refers to information containing evaluations and opinions from users about products they have purchased, including feedback on product quality and usability.

[0919] This invention relates to a system that generates natural language descriptions and virtual reality simulation images using a generative AI model based on product data collected by the user from multiple e-commerce sites, and provides them to the user's terminal. The detailed configuration and operation of the system are described below.

[0920] System Configuration

[0921] This system primarily consists of user terminals, a server, and generating artificial intelligence (AI) modules. User terminals are computer devices such as smartphones and personal computers, while the server serves as the infrastructure for data collection, analysis, and result distribution. The generating AI analyzes the collected data and is responsible for generating natural language explanations and virtual reality simulation images.

[0922] System Operation Overview

[0923] 1. The user requests product information.

[0924] The user uses the application on their device to enter the name of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[0925] 2. The server collects and analyzes the information.

[0926] The server collects requested product information through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the collected information to remove duplicate and inaccurate data and stores it in a database.

[0927] Next, the generative AI model analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the generative AI model generates a user-friendly natural language description. For example, if the generative AI model is given input data such as "Product Name: Sony 4K TV, Price: $999, Rating: 4.5", it will generate a description.

[0928] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[0929] 3. Provide results to the user.

[0930] The server sends back information generated by the AI ​​to the user's terminal. This includes price comparison information for products, review summaries, product advantages / disadvantages, and virtual reality simulation images. The user's terminal displays this information, making it easy for the user to compare and consider options.

[0931] Specifically, when a user searches for "the latest 4K TV," the device screen displays price comparisons such as "$999 on e-commerce site A, $950 on e-commerce site B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like in their home.

[0932] Hardware and software to be used

[0933] This system uses the following hardware and software:

[0934] User's device: Smartphone, PC, etc.

[0935] Server: Foundation for data collection and analysis

[0936] Artificial intelligence to be generated: OpenAI API, etc.

[0937] Specific example

[0938] For example, if a user searches for a product called "Sony 4K TV," the server passes the input data to the generating AI model as follows:

[0939] "Product Name: Sony 4K TV, Price: $999, Rating: 4.5"

[0940] The generative AI model uses this to generate the following explanatory text:

[0941] "This television features rich colors and high resolution, making it particularly suitable for watching movies."

[0942] By providing users with this description and virtual reality simulation images, they can quickly select the most suitable product.

[0943] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0944] Step 1:

[0945] The user opens the application on their device and enters the name of the desired product into the search bar. For example, they might enter "latest 4K TV". This search request is sent from the user to the server.

[0946] Input: The name of the product entered by the user.

[0947] Output: Search request from user's terminal to server

[0948] Step 2:

[0949] The server collects data about products requested by users through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the data to remove duplicate and inaccurate data, ensuring clean data.

[0950] Input: Search request sent to the server

[0951] Output: Product data collected from multiple e-commerce sites (price, availability, review information)

[0952] Step 3:

[0953] Next, the server inputs the collected product data into a generative AI model for analysis. The generative AI model compares product prices, aggregates user review ratings, and extracts product advantages and disadvantages. Furthermore, the generative AI model generates natural language descriptions based on these analysis results.

[0954] Input: Filtered product data

[0955] Output: Analyzed product information, generated natural language description

[0956] Step 4:

[0957] The server then generates virtual reality simulation images based on the collected data. These images provide a visual representation of how the product would look when installed in the user's home or usage environment.

[0958] Input: Filtered product data

[0959] Output: Virtual reality simulation image

[0960] Step 5:

[0961] The server sends the description, price comparison information, and virtual reality simulation images generated by the AI ​​model to the user's device. The user's device receives this information and displays it to the user.

[0962] Input: Description text generated by the AI ​​model, price comparison information, and virtual reality simulation images.

[0963] Output: Analysis results and virtual reality simulation images sent to the user's terminal.

[0964] Step 6:

[0965] Users can review and compare product details based on the information displayed on their device screen. Ultimately, they can select the most suitable product and proceed with the purchase.

[0966] Input: Analysis results and virtual reality simulation images displayed on the user's device.

[0967] Output: The user selects the most suitable product and proceeds with the purchase.

[0968] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0969] ---

[0970] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support that responds to the user's emotions. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[0971] System Configuration

[0972] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[0973] System Operation Overview

[0974] 1. The user requests product information.

[0975] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV". The device then sends this search request to the server.

[0976] 2. The server collects and analyzes the information.

[0977] The server collects requested product information (price, stock status, reviews, warranty information, etc.) through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data is stored in a database after removing duplicates and inaccurate information.

[0978] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[0979] Furthermore, the artificial intelligence generates virtual reality simulation images based on the collected data. These images provide an example of what it would look like if, for instance, a 4K television were installed in the user's living room.

[0980] 3. Recognize the user's emotions.

[0981] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional information. This emotional information is passed to the AI ​​that generates product recommendations based on the user's current emotional state.

[0982] 4. Provide results to the user.

[0983] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[0984] For example, if a user searches for "the latest 4K TV" and displays an excited expression, the emotion engine will recognize this excited state, and the generated artificial intelligence will provide explanations and recommendations tailored to that emotion, such as "recommended features for enjoying high-quality video." Similarly, if the user is feeling down, the generated AI will recommend "content recommended for stress relief," enabling the provision of information that takes the user's emotional state into consideration.

[0985] Specific example

[0986] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[0987] Online shop A: $1000, in stock, average rating 4.5

[0988] Online shop B: $950, out of stock, average rating 4.3

[0989] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[0990] AR simulation image as it would appear when installed in the user's living room.

[0991] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[0992] This makes it easier for users to compare options, make the best choice quickly, and receive personalized information tailored to their emotions, thus further enhancing the purchasing experience.

[0993] As described above, the "EC Personal Assist" system according to the present invention not only significantly improves the efficiency and sense of security in product selection during online shopping, but also provides pinpoint support tailored to the user's emotional state.

[0994] ---

[0995] The above is a description of the "mode for carrying out the invention."

[0996] The following describes the processing flow.

[0997] ---

[0998] Step 1:

[0999] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[1000] Step 2:

[1001] The terminal sends the user's search request to the server. The request includes the product name and category.

[1002] Step 3:

[1003] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[1004] Step 4:

[1005] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[1006] Step 5:

[1007] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[1008] Step 6:

[1009] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[1010] Step 7:

[1011] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[1012] Step 8:

[1013] The server allows the emotion engine to acquire real-time emotional data from the user. The emotion engine analyzes the user's facial expressions and voice to obtain emotional information, such as whether the user is excited or depressed.

[1014] Step 9:

[1015] The artificial intelligence, generated based on emotional data acquired from the emotion engine, provides product recommendations tailored to the user's emotions. If the user expresses positive emotions, it offers product descriptions and options that emphasize enjoyable experiences; if the user expresses negative emotions, it offers products and content that promote relaxation.

[1016] Step 10:

[1017] The server sends the generated analysis results, simulation images, and emotion-based recommendation information to the user's terminal.

[1018] Step 11:

[1019] The terminal displays analysis results received from the server, simulation images, and sentiment-based recommendations to the user. These recommendations may include price comparison tables, review summaries, product pros and cons, virtual reality simulation images, and sentiment-based product recommendations.

[1020] Step 12:

[1021] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[1022] ---

[1023] The above outlines the specific processing steps of the "EC Personal Assist" system, which incorporates an emotion engine.

[1024] (Example 2)

[1025] Next, we will describe Example 2. 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."

[1026] Traditional online shopping systems failed to consider the emotions users feel when selecting products, merely providing simple price and review information. As a result, the user's purchasing experience was cold and impersonal, leading to low satisfaction. Furthermore, it was difficult for users to intuitively understand what it would be like to actually use the product. To address this, it is necessary to consider user emotions, provide more personalized support, and improve the purchasing experience.

[1027] The specific processing performed by the specific 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 collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring user emotional information using an emotion engine and passing it to generating artificial intelligence, means for providing the analysis results and virtual reality simulation images to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results and virtual reality simulation images. This makes it possible to provide personalized support that takes into account the user's emotional state and realize an intuitive purchasing experience.

[1028] "Multiple e-commerce sites" refers to a collection of websites or platforms that sell goods and services via the internet.

[1029] "Generative artificial intelligence" refers to a machine learning model that analyzes collected data and generates explanations in natural language or images.

[1030] An "emotion engine" is a combination of software and hardware used to analyze a user's facial expressions and voice to acquire emotional information.

[1031] A "virtual reality simulation image" is an image that generates a visual representation of what a product would look like if it were virtually placed in a real-world environment.

[1032] "User terminal" refers to a computer device used by a user, including smartphones, tablets, and personal computers.

[1033] "Price comparison information" refers to data collected and compared from multiple e-commerce sites regarding the prices and sales conditions of identical or similar products.

[1034] "Review information" refers to ratings and comments posted by users who have previously purchased a product, and is a summary of that information.

[1035] A "search request" is a request in which a user enters the name or category of a product they wish to purchase, and information is collected based on that information.

[1036] "Providing it to the generating artificial intelligence" means providing the emotional information acquired by the emotion engine to the generating artificial intelligence so that it can be used for analysis and information generation.

[1037] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support tailored to the user's emotions. This system collects product data from multiple e-commerce sites, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[1038] System Configuration

[1039] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[1040] Hardware and software to be used

[1041] This invention utilizes the following hardware and software:

[1042] Emotion engine: OpenCV, Dlib

[1043] Data collection and analysis: BeautifulSoup, Requests

[1044] Natural Language Processing: NLTK, GPT-3 (OpenAI)

[1045] Virtual reality simulation: Three.js

[1046] Overview of program processing

[1047] The operation of this system is as follows:

[1048] The user uses their device to enter the name and category of the product they wish to purchase, such as "Sony 4K TV," and sends this as a request to the server. The server collects the requested product information (price, availability, reviews, warranty information, etc.) through APIs of multiple e-commerce sites (e.g., e-commerce site A, e-commerce site B, e-commerce site C). The collected data is then filtered for duplication and inaccuracies using the BeautifulSoup and Requests libraries and stored in a database.

[1049] Next, the AI ​​(GPT-3) analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. Furthermore, the AI ​​generates a virtual reality simulation image based on the collected data using Three.js. This image provides an image of what a Sony 4K TV would look like if it were installed in the user's living room.

[1050] Furthermore, the emotion engine uses OpenCV and Dlib to analyze the user's facial expressions and voice in real time and acquire emotional information. This emotional information is passed to the generating artificial intelligence, which then makes product recommendations based on the user's current emotional state.

[1051] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[1052] Specific example

[1053] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[1054] E-commerce site A: $1000, in stock, average rating 4.5

[1055] E-commerce site B: $950, out of stock, average rating 4.3

[1056] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1057] Virtual reality simulation image as it would appear when installed in the user's living room.

[1058] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[1059] Example of a prompt

[1060] The following is an example of a prompt to input into a generative AI model:

[1061] "Please obtain prices and reviews for Sony 4K TVs from various e-commerce sites (e-commerce site A, e-commerce site B, e-commerce site C) and generate comparative information in a user-friendly format. Furthermore, based on sentiment information from the sentiment engine, please provide product recommendations tailored to the user's current emotions."

[1062] The above is a specific implementation example of the "EC Personal Assist" system.

[1063] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1064] Step 1:

[1065] The user enters and submits product information using their device. Specifically, the user enters the name of the product they wish to purchase, "Sony 4K TV," into the search bar of the dedicated app and presses the submit button. This input is then sent from the device to the server as a request.

[1066] Input: User-entered product name "Sony 4K TV"

[1067] Output: Search request sent to the server

[1068] Step 2:

[1069] The server collects and organizes product information from multiple e-commerce sites. The server uses the Amazon API, Rakuten API, and Yahoo Shopping API to collect information such as price, availability, and reviews for "Sony 4K TV." The collected data is then filtered for duplication and inaccuracies using the BeautifulSoup and Requests libraries, and the organized data is stored in a database.

[1070] Input: Search request based on product name "Sony 4K TV"

[1071] Output: Organized product information (price, availability, reviews)

[1072] Step 3:

[1073] The server uses a generative AI model (GPT-3) to analyze product information. The generative AI model on the server analyzes product information stored in the database, performing price comparisons, summarizing review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, it generates a description in natural language. The server also uses Three.js to generate a virtual reality simulation image of "what if a Sony 4K TV were installed in the user's living room."

[1074] Input: Prepared product information

[1075] Output: Price comparison table, review summary, pros / conveniences, virtual reality simulation images

[1076] Step 4:

[1077] The emotion engine recognizes the user's emotions in real time. Using the OpenCV and Dlib libraries, the emotion engine analyzes the user's facial expressions and voice to obtain emotional information. This emotional information is stored in a database and passed to the generating artificial intelligence.

[1078] Input: User's facial expressions and voice

[1079] Output: Emotional information (e.g., joy, excitement, sadness)

[1080] Step 5:

[1081] The server generates information by combining analysis results and emotional data. The generating AI model takes emotional data into consideration and customizes product recommendations based on the user's current emotional state. For example, if a user searches for "Sony 4K TV" and is excited, it will recommend features that allow them to enjoy high-quality video.

[1082] Input: Analysis results, sentiment information

[1083] Output: Customized recommendations, e.g., "Recommended features for enjoying high-quality video."

[1084] Step 6:

[1085] The server sends generated information and sentiment information to the user's terminal. The server packages the customized information and sentiment information in JSON format and sends it to the user's terminal.

[1086] Input: Customized recommendations, sentiment information

[1087] Output: Information packaged in JSON format

[1088] Step 7:

[1089] The user's device displays the information it receives. The user's device parses the received JSON and converts it into a format that can be displayed in a web browser or application. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations tailored to the user's emotions.

[1090] Input: Information in JSON format

[1091] Output: Information converted into a displayable format (e.g., price comparison table, review summary, virtual reality simulation image, additional recommendation information)

[1092] The above describes the specific processing steps and operation of this system.

[1093] (Application Example 2)

[1094] Next, we will explain application example 2. In the following explanation, 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."

[1095] Traditional online shopping systems have struggled to automatically analyze user emotions when they access product information and provide customized product recommendations based on that analysis. Furthermore, because they rely solely on simple data analysis without considering user emotions, the user purchasing experience is uniform and lacks individuality. This can lead to decreased user satisfaction and reduced purchasing intent.

[1096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1097] In this invention, the server includes means for collecting product data from multiple commercial sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring emotional information from the user's facial expressions and voice using an emotion recognition engine, means for generating and providing personalized product recommendation information based on the user's emotional information, means for providing the analysis results, virtual reality simulation images, and product recommendation information based on emotional information to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results, virtual reality simulation images, and product recommendation information based on emotional information. This enables personalized product recommendations that take into account the user's emotional information. As a result, the user's purchasing experience is improved, not only increasing satisfaction but also stimulating purchasing intent.

[1098] "Multiple commercial sites" refers to a general term for websites that sell products on multiple online platforms.

[1099] "Product data" refers to information related to a product, such as price, stock availability, reviews, and warranty information.

[1100] "Generative artificial intelligence" refers to artificial intelligence technology that has the function of analyzing collected data, translating it into language, and generating virtual reality simulation images.

[1101] A "virtual reality simulation image" is an image or video that virtually displays what a product would look like installed in the real world.

[1102] An "emotion recognition engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice.

[1103] "User emotion information" refers to data that indicates the user's current emotional state, obtained by the emotion recognition engine.

[1104] "Personalized product recommendations" refer to product recommendations that are best suited to a specific user, generated by taking into account the user's emotional information.

[1105] "User's device" refers to the computer device used by the user, such as a smartphone or tablet.

[1106] A "search request" refers to an information request based on the product name or category entered by the user in the search bar.

[1107] This invention relates to an "emotion-recognition shopping assist" system that improves the purchasing experience in online shopping. This system combines artificial intelligence (AI) that collects and generates product data from multiple commercial sites with an emotion recognition engine, making it possible to provide personalized product recommendation information based on the user's emotions.

[1108] System Configuration

[1109] This system consists of the following main elements:

[1110] 1. Server

[1111] 2. Artificial Intelligence (AI) Modules to be Generated

[1112] 3. Emotion Recognition Engine

[1113] 4. User terminal

[1114] System Operation Overview

[1115] USER product search

[1116] Users enter product search queries using devices such as smartphones or personal computers. For example, if a user searches for "high-resolution television," their device sends this search request to the server.

[1117] Data collection and analysis

[1118] The server collects product data from multiple commercial websites using APIs. This data includes price, inventory, reviews, and warranty information. The collected data is stored in a database and analyzed by an AI module. The AI ​​module performs price comparisons, review summaries, and extracts pros and cons.

[1119] Generation of virtual reality simulation images

[1120] The AI ​​module further generates virtual reality simulation images. For example, it can generate a simulation image of "what if a high-resolution television were installed in the user's living room."

[1121] emotion recognition

[1122] The emotion recognition engine analyzes the user's facial expressions and voice in real time to obtain the user's emotional state. The acquired emotional information is then passed to the AI ​​module.

[1123] Personalized product recommendations

[1124] The AI ​​module generates personalized product recommendations based on collected product data and emotional information. For example, if a user shows an excited expression, it will recommend a television suitable for enjoying high-definition movies that match that emotion.

[1125] Displaying Results

[1126] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's device. The user's device displays the received information, and the user can receive personalized product recommendations tailored to their emotions.

[1127] Hardware and software used

[1128] Hardware: Servers, user terminals (smartphones, personal computers, etc.)

[1129] Software: AI module, emotion recognition engine, data collection API, database management system

[1130] Specific example

[1131] For example, if a user searches for "high-definition television" and expresses excitement, the following information might be displayed:

[1132] Commercial site A: ¥150,000, in stock, average rating 4.5

[1133] Commercial site B: ¥140,000, out of stock, average rating 4.3

[1134] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1135] Virtual reality simulation image as it would appear when installed in the user's living room.

[1136] Emotion-based recommendation feature

[1137] Examples of prompts for generative AI models

[1138] A user has searched for "high-resolution TV" and is expressing excitement. Generate emotion-based product recommendations for this user.

[1139] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1140] Step 1:

[1141] USER product search

[1142] The user uses a terminal to enter a product search query (for example, "high-resolution television"). The terminal sends this search request to the server. The input is the user's product search query, and the output is the search request sent to the server.

[1143] Step 2:

[1144] Data collection

[1145] The server collects product data (price, stock, reviews, warranty information, etc.) through APIs of multiple commercial websites. The collected data is stored in a database. The input is product data obtained from the APIs of multiple commercial websites, and the output is organized product data stored in the database.

[1146] Step 3:

[1147] Data Analysis

[1148] The server analyzes the collected product data using an artificial intelligence (AI) module. This analysis includes price comparison, review summaries, and extraction of product advantages and disadvantages. The input is product data stored in a database, and the output is the analysis results (price comparison information, review summaries, and lists of advantages and disadvantages).

[1149] Step 4:

[1150] Generation of virtual reality simulation images

[1151] The AI ​​generates virtual reality simulation images based on the analyzed data. For example, it generates a simulation image of "a high-resolution television installed in the user's living room." The input is the analysis results and the user's environment information, and the output is the virtual reality simulation image.

[1152] Step 5:

[1153] emotion recognition

[1154] The emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional information (e.g., state of excitement). This emotional information is then passed to the AI ​​module that generates the emotional information. The input is the user's facial expressions and voice data, and the output is emotional information.

[1155] Step 6:

[1156] Generating personalized product recommendations

[1157] The generated AI module produces personalized product recommendations based on collected product data and emotional information. For example, if a user is excited, it selects products and features that match their excited state (e.g., recommending high-definition movies). The input is product data and emotional information, and the output is personalized product recommendations.

[1158] Step 7:

[1159] Displaying Results

[1160] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's terminal. The terminal displays the received information and provides it to the user. The inputs are the analysis results, virtual reality simulation images, and personalized product recommendation information, while the output is the information displayed on the user's terminal.

[1161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1162] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1163] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1164] [Fourth Embodiment]

[1165] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1169] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1174] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1175] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1176] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1177] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1178] ---

[1179] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, translates it into language, generates virtual reality simulation images, and provides them to the user.

[1180] System Configuration

[1181] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[1182] System Operation Overview

[1183] 1. The user requests product information.

[1184] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[1185] 2. The server collects and analyzes the information.

[1186] The server collects requested product information through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data includes price, stock status, reviews, warranty information, etc. The server filters the collected information, removing duplicate and inaccurate data, and stores it in a database.

[1187] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[1188] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[1189] 3. Provide results to the user.

[1190] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[1191] For example, if a user searches for "the latest 4K TV," the device screen will display price comparisons such as "$999 at online shop A, $950 at online shop B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like installed in their home.

[1192] Furthermore, for products that users are interested in, a link is provided that takes them directly to the corresponding online shop's purchase page. This makes it easy for users to select the most suitable product and complete the purchase process.

[1193] Specific example

[1194] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[1195] Online shop A: $1000, in stock, average rating 4.5

[1196] Online shop B: $950, out of stock, average rating 4.3

[1197] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1198] AR simulation image as it would appear when installed in the user's living room.

[1199] This makes it easier for users to compare options and make the best choice quickly.

[1200] As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and sense of security in product selection during online shopping.

[1201] ---

[1202] The above is a description of the "mode for carrying out the invention."

[1203] The following describes the processing flow.

[1204] ---

[1205] Step 1:

[1206] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[1207] Step 2:

[1208] The terminal sends the user's search request to the server. The request includes the product name and category.

[1209] Step 3:

[1210] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[1211] Step 4:

[1212] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[1213] Step 5:

[1214] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[1215] Step 6:

[1216] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[1217] Step 7:

[1218] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[1219] Step 8:

[1220] The server sends the generated analysis results and simulation images to the user's terminal.

[1221] Step 9:

[1222] The terminal displays analysis results and simulation images received from the server to the user. For example, it may display price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images.

[1223] Step 10:

[1224] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[1225] ---

[1226] The above outlines the specific steps involved in the program processing of "EC Personal Assist."

[1227] (Example 1)

[1228] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1229] In online shopping, it is not easy for users to quickly and efficiently select the best products. Currently, it is necessary to individually check multiple e-commerce platforms and manually compare information such as price, availability, and reviews. Another challenge is the difficulty in visualizing how the products will actually be installed. It is necessary to solve these problems and improve the user's purchasing experience.

[1230] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1231] In this invention, the server includes means for collecting product data from multiple e-commerce platforms, means for analyzing the collected data and generating explanations in natural language using artificial intelligence, and means for generating virtual reality simulation images based on the collected data using artificial intelligence. This enables users to quickly and efficiently compare products based on unified information collected from multiple platforms. Furthermore, because the virtual reality simulation images allow users to visually confirm how the products will look when installed, they can make purchasing decisions with greater confidence.

[1232] "Multiple e-commerce platforms" refers to websites and applications used to buy and sell goods and services on multiple internet platforms.

[1233] "Product data" refers to all information related to a specific product, such as price, availability, user reviews, warranty information, and product features.

[1234] "Generative artificial intelligence" refers to an artificial intelligence model that analyzes collected data to generate explanations in natural language or virtual reality simulation images.

[1235] A "virtual reality simulation image" is a virtual visual image of a product as it would appear when actually used or installed, generated based on collected product data.

[1236] A "user's device" refers to a device, such as a smartphone or personal computer, that a user uses to connect to the internet and view and manipulate information.

[1237] "Explanation in natural language" refers to a text-based explanation generated by the artificial intelligence that analyzes the collected data and provides it to the user in an easily understandable format.

[1238] A "prompt message" is an instruction given to an artificial intelligence to perform a specific analysis or generation task.

[1239] "Filtering" is the process of removing duplicate or inaccurate data from collected data.

[1240] "Analysis results" refer to information obtained by analyzing data collected by the generating artificial intelligence, and include price comparisons, review summaries, and product advantages / disadvantages.

[1241] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. This system collects product-related data from multiple e-commerce platforms, analyzes it using artificial intelligence (AI), translates it into language, generates virtual reality simulation images, and provides them to the user.

[1242] System Configuration

[1243] This system consists of a user terminal, a server, and an artificial intelligence (AI) module that generates data. The user terminal is a device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generated AI analyzes the collected data and provides information to the user in an easy-to-understand format.

[1244] Hardware and software to be used

[1245] User devices: Smartphones and personal computers (iPhone, Android, Windows PC, etc.)

[1246] Servers: Servers for data collection and analysis (AWS, Google Cloud, Microsoft Azure)

[1247] Artificial intelligence to be generated: AI modules for natural language processing and virtual reality simulation image generation (GPT-4, Stable Diffusion, etc.)

[1248] System Operation Overview

[1249] 1. The user requests product information.

[1250] The user uses their device to enter the name or category of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[1251] 2. The server collects and analyzes the information.

[1252] The server collects requested product information through APIs from multiple e-commerce platforms. The collected data includes price, availability, reviews, and warranty information. The server filters the collected information, removing duplicate and inaccurate data before storing it in a database.

[1253] Next, the AI ​​analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. The AI ​​also generates virtual reality simulation images based on the collected data. The following prompts are used for this:

[1254] "Please provide a price comparison and review summary for 4K TVs."

[1255] "Please generate a simulated image of what a 4K TV would look like in a living room."

[1256] 3. Provide results to the user.

[1257] The server sends the information generated by the AI ​​back to the user's terminal. The user's terminal displays the received information. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images. For example, if a user searches for "the latest 4K TV," the terminal screen will display price comparisons such as "$999 on e-commerce platform A, $950 on e-commerce platform B," and descriptions such as "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to get an idea of ​​what the TV would look like in their home.

[1258] Furthermore, for products that users are interested in, a link is provided that takes them directly to the purchase page on the corresponding e-commerce platform. This allows users to easily select the most suitable product and complete the purchase process.

[1259] Specific example

[1260] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[1261] E-commerce platform A: $1000, in stock, average rating 4.5

[1262] E-commerce platform B: $950, out of stock, average rating 4.3

[1263] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1264] AR simulation image as it would appear when installed in the user's living room.

[1265] This makes it easier for users to compare and consider options, allowing them to quickly make the best choice. As described above, the "EC Personal Assist" system according to the present invention significantly improves the efficiency and peace of mind of product selection in online shopping.

[1266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1267] Step 1:

[1268] The user enters the product name or category into the device's search bar and presses the search button. The data entered is the search keyword. The device sends this search request to the server. Here, the input is the search keyword, and the output is sent to the server as an HTTP request.

[1269] Step 2:

[1270] The server sends requests to APIs of multiple e-commerce platforms to collect relevant product data. The input is the data obtained from the APIs, and the output is the collected product data. Specifically, the server retrieves price, inventory status, reviews, warranty information, etc., from each platform and stores it in a database.

[1271] Step 3:

[1272] The server filters the collected data, removing inaccurate and duplicate data. The input is the collected raw data, and the output is the filtered, high-accuracy data. Specifically, it checks for duplicate entries and missing data from the information stored in the database and constructs a normalized dataset.

[1273] Step 4:

[1274] The generating artificial intelligence analyzes the filtered data. The input is product information stored in a database, and the output is price comparisons, review ratings, and product advantages / disadvantages as analysis results. Specifically, the server inputs the following prompts to the generating AI model (e.g., GPT-4):

[1275] "Please provide a price comparison and review summary for 4K TVs."

[1276] Step 5:

[1277] The AI ​​generating the system produces virtual reality simulation images. The input is product information and associated image data retrieved from a database, and the output is a simulated image of what the product would look like if it were installed in the user's environment. Specifically, the server inputs the following prompts to the generating AI model:

[1278] "Please generate a simulated image of what a 4K TV would look like in a living room."

[1279] Step 6:

[1280] The server sends the generated analysis results and simulation images to the user's terminal. The input is the generated analysis results and simulation images, and the output is the data sent to the user's terminal as an HTTP response.

[1281] Step 7:

[1282] The system displays information received by the user's device. Input consists of analysis results and simulation images sent from the server, while output is the information displayed on the user interface. The device displays price comparison tables, review summaries, product advantages / disadvantages, and virtual reality simulation images in a user-friendly format.

[1283] Step 8:

[1284] The user clicks a link to the purchase page for a product they are interested in. The input is the displayed link information, and the output is being redirected to the purchase page of the corresponding e-commerce platform. Specifically, the user clicks the link, and the browser opens the purchase page of the relevant platform.

[1285] The above outlines the specific processing steps of the system.

[1286] (Application Example 1)

[1287] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1288] In modern online shopping, it is extremely time-consuming for users to cross multiple e-commerce sites to select the best product. Furthermore, it is difficult to evaluate individual product information, compare prices, and visualize how the product would look in one's home. Solving these problems is essential.

[1289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1290] In this invention, the server includes means for collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using artificial intelligence, and means for generating and displaying virtual reality simulation images based on the collected product data. This makes it possible for users to easily compare information from multiple e-commerce sites, check product reviews and prices, and visually understand how the products will look when installed through virtual reality simulation.

[1291] "Multiple e-commerce sites" refers to multiple online platforms or websites, each selling different products, allowing users to purchase those products via the internet.

[1292] "Generative artificial intelligence" refers to algorithms and programs that analyze collected data and generate explanatory text in natural language or virtual reality simulation images.

[1293] "To verbalize" refers to analyzing collected data and describing the results in natural language that is easily understood by humans.

[1294] A "virtual reality simulation image" is an image that visually simulates how a product selected by the user will look in the environment in which it will actually be used.

[1295] "Providing" refers to sending data and simulation images generated by the server to the user's terminal, making them accessible to the user.

[1296] "Displaying" refers to visually presenting data or simulation images on the user's device.

[1297] "Accepting product search requests" refers to receiving requests from users' devices to search for information about specific products.

[1298] "Price comparison information" refers to information that compares the prices of the same product when it is sold on multiple e-commerce sites.

[1299] "Review information" refers to information containing evaluations and opinions from users about products they have purchased, including feedback on product quality and usability.

[1300] This invention relates to a system that generates natural language descriptions and virtual reality simulation images using a generative AI model based on product data collected by the user from multiple e-commerce sites, and provides them to the user's terminal. The detailed configuration and operation of the system are described below.

[1301] System Configuration

[1302] This system primarily consists of user terminals, a server, and generating artificial intelligence (AI) modules. User terminals are computer devices such as smartphones and personal computers, while the server serves as the infrastructure for data collection, analysis, and result distribution. The generating AI analyzes the collected data and is responsible for generating natural language explanations and virtual reality simulation images.

[1303] System Operation Overview

[1304] 1. The user requests product information.

[1305] The user uses the application on their device to enter the name of the desired product into the search bar. For example, if the user enters "I'm looking for the latest 4K TV," the device sends this search request to the server.

[1306] 2. The server collects and analyzes the information.

[1307] The server collects requested product information through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the collected information to remove duplicate and inaccurate data and stores it in a database.

[1308] Next, the generative AI model analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the generative AI model generates a user-friendly natural language description. For example, if the generative AI model is given input data such as "Product Name: Sony 4K TV, Price: $999, Rating: 4.5", it will generate a description.

[1309] Furthermore, the AI ​​generates virtual reality simulation images based on the collected data. These images, for example, provide an image of what it would look like if a 4K TV were installed in the user's living room.

[1310] 3. Provide results to the user.

[1311] The server sends back information generated by the AI ​​to the user's terminal. This includes price comparison information for products, review summaries, product advantages / disadvantages, and virtual reality simulation images. The user's terminal displays this information, making it easy for the user to compare and consider options.

[1312] Specifically, when a user searches for "the latest 4K TV," the device screen displays price comparisons such as "$999 on e-commerce site A, $950 on e-commerce site B," as well as descriptions like "This TV has excellent picture quality and is especially ideal for watching movies." Virtual reality simulation images are also displayed, allowing the user to visualize what the TV would look like in their home.

[1313] Hardware and software to be used

[1314] This system uses the following hardware and software:

[1315] User's device: Smartphone, PC, etc.

[1316] Server: Foundation for data collection and analysis

[1317] Artificial intelligence to be generated: OpenAI API, etc.

[1318] Specific example

[1319] For example, if a user searches for a product called "Sony 4K TV," the server passes the input data to the generating AI model as follows:

[1320] "Product Name: Sony 4K TV, Price: $999, Rating: 4.5"

[1321] The generative AI model uses this to generate the following explanatory text:

[1322] "This television features rich colors and high resolution, making it particularly suitable for watching movies."

[1323] By providing users with this description and virtual reality simulation images, they can quickly select the most suitable product.

[1324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1325] Step 1:

[1326] The user opens the application on their device and enters the name of the desired product into the search bar. For example, they might enter "latest 4K TV". This search request is sent from the user to the server.

[1327] Input: The name of the product entered by the user.

[1328] Output: Search request from user's terminal to server

[1329] Step 2:

[1330] The server collects data about products requested by users through APIs from multiple e-commerce sites. The collected data includes price, availability, and review information. The server filters the data to remove duplicate and inaccurate data, ensuring clean data.

[1331] Input: Search request sent to the server

[1332] Output: Product data collected from multiple e-commerce sites (price, availability, review information)

[1333] Step 3:

[1334] Next, the server inputs the collected product data into a generative AI model for analysis. The generative AI model compares product prices, aggregates user review ratings, and extracts product advantages and disadvantages. Furthermore, the generative AI model generates natural language descriptions based on these analysis results.

[1335] Input: Filtered product data

[1336] Output: Analyzed product information, generated natural language description

[1337] Step 4:

[1338] The server then generates virtual reality simulation images based on the collected data. These images provide a visual representation of how the product would look when installed in the user's home or usage environment.

[1339] Input: Filtered product data

[1340] Output: Virtual reality simulation image

[1341] Step 5:

[1342] The server sends the description, price comparison information, and virtual reality simulation images generated by the AI ​​model to the user's device. The user's device receives this information and displays it to the user.

[1343] Input: Description text generated by the AI ​​model, price comparison information, and virtual reality simulation images.

[1344] Output: Analysis results and virtual reality simulation images sent to the user's terminal.

[1345] Step 6:

[1346] Users can review and compare product details based on the information displayed on their device screen. Ultimately, they can select the most suitable product and proceed with the purchase.

[1347] Input: Analysis results and virtual reality simulation images displayed on the user's device.

[1348] Output: The user selects the most suitable product and proceeds with the purchase.

[1349] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1350] ---

[1351] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support that responds to the user's emotions. This system collects product data from multiple online shops, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[1352] System Configuration

[1353] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[1354] System Operation Overview

[1355] 1. The user requests product information.

[1356] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV". The device then sends this search request to the server.

[1357] 2. The server collects and analyzes the information.

[1358] The server collects requested product information (price, stock status, reviews, warranty information, etc.) through APIs from multiple online shops (e.g., online shop A, online shop B, online shop C). The collected data is stored in a database after removing duplicates and inaccurate information.

[1359] Next, the AI ​​analyzes the product information stored in the database. This analysis includes comparing product prices, aggregating user review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language.

[1360] Furthermore, the artificial intelligence generates virtual reality simulation images based on the collected data. These images provide an example of what it would look like if, for instance, a 4K television were installed in the user's living room.

[1361] 3. Recognize the user's emotions.

[1362] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional information. This emotional information is passed to the AI ​​that generates product recommendations based on the user's current emotional state.

[1363] 4. Provide results to the user.

[1364] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[1365] For example, if a user searches for "the latest 4K TV" and displays an excited expression, the emotion engine will recognize this excited state, and the generated artificial intelligence will provide explanations and recommendations tailored to that emotion, such as "recommended features for enjoying high-quality video." Similarly, if the user is feeling down, the generated AI will recommend "content recommended for stress relief," enabling the provision of information that takes the user's emotional state into consideration.

[1366] Specific example

[1367] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[1368] Online shop A: $1000, in stock, average rating 4.5

[1369] Online shop B: $950, out of stock, average rating 4.3

[1370] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1371] AR simulation image as it would appear when installed in the user's living room.

[1372] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[1373] This makes it easier for users to compare options, make the best choice quickly, and receive personalized information tailored to their emotions, thus further enhancing the purchasing experience.

[1374] As described above, the "EC Personal Assist" system according to the present invention not only significantly improves the efficiency and sense of security in product selection during online shopping, but also provides pinpoint support tailored to the user's emotional state.

[1375] ---

[1376] The above is a description of the "mode for carrying out the invention."

[1377] The following describes the processing flow.

[1378] ---

[1379] Step 1:

[1380] The user uses their device to enter the name or category of the product they wish to purchase into the search bar. For example, they might enter "Sony 4K TV".

[1381] Step 2:

[1382] The terminal sends the user's search request to the server. The request includes the product name and category.

[1383] Step 3:

[1384] The server accesses the APIs of multiple online shops and collects requested product information (price, stock status, reviews, warranty information, etc.). For example, it retrieves information on "Sony 4K TV" from online shops A, B, and C.

[1385] Step 4:

[1386] The server analyzes the collected data and stores it in a database. Since the data may contain duplicates or inaccurate information, filtering is performed.

[1387] Step 5:

[1388] The server passes the stored data to an artificial intelligence (AI) that generates and analyzes it. The AI ​​performs price comparisons, aggregates review ratings, and extracts the advantages and disadvantages of products.

[1389] Step 6:

[1390] The AI ​​generates explanations in natural language based on the analysis results. For example, it might generate information such as, "Sony 4K TVs are high-resolution and ideal for watching movies. The current lowest price is AA yen at online shop B."

[1391] Step 7:

[1392] The artificial intelligence generates virtual reality simulation images based on collected data. Specifically, it creates an image of what a "Sony 4K TV" would look like installed in the user's living room.

[1393] Step 8:

[1394] The server allows the emotion engine to acquire real-time emotional data from the user. The emotion engine analyzes the user's facial expressions and voice to obtain emotional information, such as whether the user is excited or depressed.

[1395] Step 9:

[1396] The artificial intelligence, generated based on emotional data acquired from the emotion engine, provides product recommendations tailored to the user's emotions. If the user expresses positive emotions, it offers product descriptions and options that emphasize enjoyable experiences; if the user expresses negative emotions, it offers products and content that promote relaxation.

[1397] Step 10:

[1398] The server sends the generated analysis results, simulation images, and emotion-based recommendation information to the user's terminal.

[1399] Step 11:

[1400] The terminal displays analysis results received from the server, simulation images, and sentiment-based recommendations to the user. These recommendations may include price comparison tables, review summaries, product pros and cons, virtual reality simulation images, and sentiment-based product recommendations.

[1401] Step 12:

[1402] The device provides the user with a link to purchase the product and redirects them to the appropriate online shop's purchase page. For example, a link that says "Click here to purchase from online shop B" might be displayed.

[1403] ---

[1404] The above outlines the specific processing steps of the "EC Personal Assist" system, which incorporates an emotion engine.

[1405] (Example 2)

[1406] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1407] Traditional online shopping systems failed to consider the emotions users feel when selecting products, merely providing simple price and review information. As a result, the user's purchasing experience was cold and impersonal, leading to low satisfaction. Furthermore, it was difficult for users to intuitively understand what it would be like to actually use the product. To address this, it is necessary to consider user emotions, provide more personalized support, and improve the purchasing experience.

[1408] The specific processing performed by the specific 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 collecting product data from multiple e-commerce sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring user emotional information using an emotion engine and passing it to generating artificial intelligence, means for providing the analysis results and virtual reality simulation images to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results and virtual reality simulation images. This makes it possible to provide personalized support that takes into account the user's emotional state and realize an intuitive purchasing experience.

[1409] "Multiple e-commerce sites" refers to a collection of websites or platforms that sell goods and services via the internet.

[1410] "Generative artificial intelligence" refers to a machine learning model that analyzes collected data and generates explanations in natural language or images.

[1411] An "emotion engine" is a combination of software and hardware used to analyze a user's facial expressions and voice to acquire emotional information.

[1412] A "virtual reality simulation image" is an image that generates a visual representation of what a product would look like if it were virtually placed in a real-world environment.

[1413] "User terminal" refers to a computer device used by a user, including smartphones, tablets, and personal computers.

[1414] "Price comparison information" refers to data collected and compared from multiple e-commerce sites regarding the prices and sales conditions of identical or similar products.

[1415] "Review information" refers to ratings and comments posted by users who have previously purchased a product, and is a summary of that information.

[1416] A "search request" is a request in which a user enters the name or category of a product they wish to purchase, and information is collected based on that information.

[1417] "Providing it to the generating artificial intelligence" means providing the emotional information acquired by the emotion engine to the generating artificial intelligence so that it can be used for analysis and information generation.

[1418] This invention relates to "EC Personal Assist," a system for improving the purchasing experience in online shopping. In particular, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide individualized support tailored to the user's emotions. This system collects product data from multiple e-commerce sites, analyzes the data using artificial intelligence, verbalizes it, generates virtual reality simulation images, and provides them to the user.

[1419] System Configuration

[1420] This system consists of a user terminal, a server, an artificial intelligence (AI) module that generates data, and an emotion engine. The user terminal is a computer device such as a smartphone or personal computer, and the server serves as the foundation for data collection and analysis. The generating AI analyzes the collected data and provides information to the user in an easy-to-understand format. The emotion engine analyzes the user's facial expressions and voice and adds that emotional information to the database.

[1421] Hardware and software to be used

[1422] This invention utilizes the following hardware and software:

[1423] Emotion engine: OpenCV, Dlib

[1424] Data collection and analysis: BeautifulSoup, Requests

[1425] Natural Language Processing: NLTK, GPT-3 (OpenAI)

[1426] Virtual reality simulation: Three.js

[1427] Overview of program processing

[1428] The operation of this system is as follows:

[1429] The user uses their device to enter the name and category of the product they wish to purchase, such as "Sony 4K TV," and sends this as a request to the server. The server collects the requested product information (price, availability, reviews, warranty information, etc.) through APIs of multiple e-commerce sites (e.g., e-commerce site A, e-commerce site B, e-commerce site C). The collected data is then filtered for duplication and inaccuracies using the BeautifulSoup and Requests libraries and stored in a database.

[1430] Next, the AI ​​(GPT-3) analyzes the product information stored in the database. This analysis includes price comparisons, aggregation of user review ratings, and extraction of product advantages and disadvantages. Based on the analysis results, the AI ​​generates a user-friendly explanation in natural language. Furthermore, the AI ​​generates a virtual reality simulation image based on the collected data using Three.js. This image provides an image of what a Sony 4K TV would look like if it were installed in the user's living room.

[1431] Furthermore, the emotion engine uses OpenCV and Dlib to analyze the user's facial expressions and voice in real time and acquire emotional information. This emotional information is passed to the generating artificial intelligence, which then makes product recommendations based on the user's current emotional state.

[1432] The server sends back information generated by the generation AI and emotional information from the emotion engine to the user's terminal. The user's terminal displays the received information. The displayed content includes a price comparison table, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations based on the user's emotions.

[1433] Specific example

[1434] For example, when a user searches for "Sony 4K TV," the following information will be displayed:

[1435] E-commerce site A: $1000, in stock, average rating 4.5

[1436] E-commerce site B: $950, out of stock, average rating 4.3

[1437] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1438] Virtual reality simulation image as it would appear when installed in the user's living room.

[1439] If the user has a happy expression, additional information such as "Recommended applications suitable for watching movies" will be provided.

[1440] Example of a prompt

[1441] The following is an example of a prompt to input into a generative AI model:

[1442] "Please obtain prices and reviews for Sony 4K TVs from various e-commerce sites (e-commerce site A, e-commerce site B, e-commerce site C) and generate comparative information in a user-friendly format. Furthermore, based on sentiment information from the sentiment engine, please provide product recommendations tailored to the user's current emotions."

[1443] The above is a specific implementation example of the "EC Personal Assist" system.

[1444] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1445] Step 1:

[1446] The user enters and submits product information using their device. Specifically, the user enters the name of the product they wish to purchase, "Sony 4K TV," into the search bar of the dedicated app and presses the submit button. This input is then sent from the device to the server as a request.

[1447] Input: User-entered product name "Sony 4K TV"

[1448] Output: Search request sent to the server

[1449] Step 2:

[1450] The server collects and organizes product information from multiple e-commerce sites. The server uses the Amazon API, Rakuten API, and Yahoo Shopping API to collect information such as price, availability, and reviews for "Sony 4K TV." The collected data is then filtered for duplication and inaccuracies using the BeautifulSoup and Requests libraries, and the organized data is stored in a database.

[1451] Input: Search request based on product name "Sony 4K TV"

[1452] Output: Organized product information (price, availability, reviews)

[1453] Step 3:

[1454] The server uses a generative AI model (GPT-3) to analyze product information. The generative AI model on the server analyzes product information stored in the database, performing price comparisons, summarizing review ratings, and extracting the product's advantages and disadvantages. Based on the analysis results, it generates a description in natural language. The server also uses Three.js to generate a virtual reality simulation image of "what if a Sony 4K TV were installed in the user's living room."

[1455] Input: Prepared product information

[1456] Output: Price comparison table, review summary, pros / conveniences, virtual reality simulation images

[1457] Step 4:

[1458] The emotion engine recognizes the user's emotions in real time. Using the OpenCV and Dlib libraries, the emotion engine analyzes the user's facial expressions and voice to obtain emotional information. This emotional information is stored in a database and passed to the generating artificial intelligence.

[1459] Input: User's facial expressions and voice

[1460] Output: Emotional information (e.g., joy, excitement, sadness)

[1461] Step 5:

[1462] The server generates information by combining analysis results and emotional data. The generating AI model takes emotional data into consideration and customizes product recommendations based on the user's current emotional state. For example, if a user searches for "Sony 4K TV" and is excited, it will recommend features that allow them to enjoy high-quality video.

[1463] Input: Analysis results, sentiment information

[1464] Output: Customized recommendations, e.g., "Recommended features for enjoying high-quality video."

[1465] Step 6:

[1466] The server sends generated information and sentiment information to the user's terminal. The server packages the customized information and sentiment information in JSON format and sends it to the user's terminal.

[1467] Input: Customized recommendations, sentiment information

[1468] Output: Information packaged in JSON format

[1469] Step 7:

[1470] The user's device displays the information it receives. The user's device parses the received JSON and converts it into a format that can be displayed in a web browser or application. The displayed content includes price comparison tables, review summaries, product advantages / disadvantages, virtual reality simulation images, and additional recommendations tailored to the user's emotions.

[1471] Input: Information in JSON format

[1472] Output: Information converted into a displayable format (e.g., price comparison table, review summary, virtual reality simulation image, additional recommendation information)

[1473] The above describes the specific processing steps and operation of this system.

[1474] (Application Example 2)

[1475] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1476] Traditional online shopping systems have struggled to automatically analyze user emotions when they access product information and provide customized product recommendations based on that analysis. Furthermore, because they rely solely on simple data analysis without considering user emotions, the user purchasing experience is uniform and lacks individuality. This can lead to decreased user satisfaction and reduced purchasing intent.

[1477] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1478] In this invention, the server includes means for collecting product data from multiple commercial sites, means for analyzing and verbalizing the collected data using generating artificial intelligence, means for generating virtual reality simulation images based on the collected data using generating artificial intelligence, means for acquiring emotional information from the user's facial expressions and voice using an emotion recognition engine, means for generating and providing personalized product recommendation information based on the user's emotional information, means for providing the analysis results, virtual reality simulation images, and product recommendation information based on emotional information to the user's terminal, and means for receiving product search requests from the user's terminal and displaying the analysis results, virtual reality simulation images, and product recommendation information based on emotional information. This enables personalized product recommendations that take into account the user's emotional information. As a result, the user's purchasing experience is improved, not only increasing satisfaction but also stimulating purchasing intent.

[1479] "Multiple commercial sites" refers to a general term for websites that sell products on multiple online platforms.

[1480] "Product data" refers to information related to a product, such as price, stock availability, reviews, and warranty information.

[1481] "Generative artificial intelligence" refers to artificial intelligence technology that has the function of analyzing collected data, translating it into language, and generating virtual reality simulation images.

[1482] A "virtual reality simulation image" is an image or video that virtually displays what a product would look like installed in the real world.

[1483] An "emotion recognition engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice.

[1484] "User emotion information" refers to data that indicates the user's current emotional state, obtained by the emotion recognition engine.

[1485] "Personalized product recommendations" refer to product recommendations that are best suited to a specific user, generated by taking into account the user's emotional information.

[1486] "User's device" refers to the computer device used by the user, such as a smartphone or tablet.

[1487] A "search request" refers to an information request based on the product name or category entered by the user in the search bar.

[1488] This invention relates to an "emotion-recognition shopping assist" system that improves the purchasing experience in online shopping. This system combines artificial intelligence (AI) that collects and generates product data from multiple commercial sites with an emotion recognition engine, making it possible to provide personalized product recommendation information based on the user's emotions.

[1489] System Configuration

[1490] This system consists of the following main elements:

[1491] 1. Server

[1492] 2. Artificial Intelligence (AI) Modules to be Generated

[1493] 3. Emotion Recognition Engine

[1494] 4. User terminal

[1495] System Operation Overview

[1496] USER product search

[1497] Users enter product search queries using devices such as smartphones or personal computers. For example, if a user searches for "high-resolution television," their device sends this search request to the server.

[1498] Data collection and analysis

[1499] The server collects product data from multiple commercial websites using APIs. This data includes price, inventory, reviews, and warranty information. The collected data is stored in a database and analyzed by an AI module. The AI ​​module performs price comparisons, review summaries, and extracts pros and cons.

[1500] Generation of virtual reality simulation images

[1501] The AI ​​module further generates virtual reality simulation images. For example, it can generate a simulation image of "what if a high-resolution television were installed in the user's living room."

[1502] emotion recognition

[1503] The emotion recognition engine analyzes the user's facial expressions and voice in real time to obtain the user's emotional state. The acquired emotional information is then passed to the AI ​​module.

[1504] Personalized product recommendations

[1505] The AI ​​module generates personalized product recommendations based on collected product data and emotional information. For example, if a user shows an excited expression, it will recommend a television suitable for enjoying high-definition movies that match that emotion.

[1506] Displaying Results

[1507] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's device. The user's device displays the received information, and the user can receive personalized product recommendations tailored to their emotions.

[1508] Hardware and software used

[1509] Hardware: Servers, user terminals (smartphones, personal computers, etc.)

[1510] Software: AI module, emotion recognition engine, data collection API, database management system

[1511] Specific example

[1512] For example, if a user searches for "high-definition television" and expresses excitement, the following information might be displayed:

[1513] Commercial site A: ¥150,000, in stock, average rating 4.5

[1514] Commercial site B: ¥140,000, out of stock, average rating 4.3

[1515] This television features rich colors and high resolution, making it particularly suitable for watching movies.

[1516] Virtual reality simulation image as it would appear when installed in the user's living room.

[1517] Emotion-based recommendation feature

[1518] Examples of prompts for generative AI models

[1519] A user has searched for "high-resolution TV" and is expressing excitement. Generate emotion-based product recommendations for this user.

[1520] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1521] Step 1:

[1522] USER product search

[1523] The user uses a terminal to enter a product search query (for example, "high-resolution television"). The terminal sends this search request to the server. The input is the user's product search query, and the output is the search request sent to the server.

[1524] Step 2:

[1525] Data collection

[1526] The server collects product data (price, stock, reviews, warranty information, etc.) through APIs of multiple commercial websites. The collected data is stored in a database. The input is product data obtained from the APIs of multiple commercial websites, and the output is organized product data stored in the database.

[1527] Step 3:

[1528] Data Analysis

[1529] The server analyzes the collected product data using an artificial intelligence (AI) module. This analysis includes price comparison, review summaries, and extraction of product advantages and disadvantages. The input is product data stored in a database, and the output is the analysis results (price comparison information, review summaries, and lists of advantages and disadvantages).

[1530] Step 4:

[1531] Generation of virtual reality simulation images

[1532] The AI ​​generates virtual reality simulation images based on the analyzed data. For example, it generates a simulation image of "a high-resolution television installed in the user's living room." The input is the analysis results and the user's environment information, and the output is the virtual reality simulation image.

[1533] Step 5:

[1534] emotion recognition

[1535] The emotion recognition engine analyzes the user's facial expressions and voice to obtain emotional information (e.g., state of excitement). This emotional information is then passed to the AI ​​module that generates the emotional information. The input is the user's facial expressions and voice data, and the output is emotional information.

[1536] Step 6:

[1537] Generating personalized product recommendations

[1538] The generated AI module produces personalized product recommendations based on collected product data and emotional information. For example, if a user is excited, it selects products and features that match their excited state (e.g., recommending high-definition movies). The input is product data and emotional information, and the output is personalized product recommendations.

[1539] Step 7:

[1540] Displaying Results

[1541] The server sends back the analysis results, virtual reality simulation images, and product recommendation information based on emotional data to the user's terminal. The terminal displays the received information and provides it to the user. The inputs are the analysis results, virtual reality simulation images, and personalized product recommendation information, while the output is the information displayed on the user's terminal.

[1542] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1543] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1544] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1545] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1546] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1547] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1548] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1549] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1550] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1551] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1552] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1553] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1554] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1555] 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.

[1556] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1557] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1558] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1559] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1560] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1561] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1562] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1563] The following is further disclosed regarding the embodiments described above.

[1564] (Claim 1)

[1565] [Methods for collecting product data from multiple online shops,

[1566] [A means of analyzing and verbalizing collected data using generating artificial intelligence,

[1567] [A means of generating virtual reality simulation images based on collected data using artificial intelligence,

[1568] [Means for providing the above analysis results and virtual reality simulation images to the user's terminal,

[1569] [Means for receiving product search requests from the user's terminal and displaying the above analysis results and virtual reality simulation images,

[1570] A system that includes this.

[1571] (Claim 2)

[1572] [A system according to claim 1 for analyzing price comparison information of products.]

[1573] (Claim 3)

[1574] [A system according to claim 1 that analyzes and summarizes product review information.

[1575] "Example 1"

[1576] (Claim 1)

[1577] [Means for collecting product data from multiple e-commerce platforms,

[1578] [A means of analyzing collected data and generating explanations in natural language using artificial intelligence that generates data,

[1579] [A means of generating virtual reality simulation images based on collected data using artificial intelligence,

[1580] [Means for providing the above analysis results and virtual reality simulation images to the user's terminal,

[1581] [Means for receiving product search requests from the user's terminal and displaying the above analysis results and virtual reality simulation images,

[1582] [Means for generating prompt sentences to be input to the artificial intelligence model to be generated,

[1583] [Methods for filtering collected data and eliminating inaccurate data,

[1584] A system that includes this.

[1585] (Claim 2)

[1586] [A system according to claim 1 for analyzing price comparison information of products.]

[1587] (Claim 3)

[1588] [A system according to claim 1 that analyzes and summarizes product review information.

[1589] "Application Example 1"

[1590] (Claim 1)

[1591] [Methods for collecting product data from multiple e-commerce sites,

[1592] [A means of analyzing and verbalizing collected data using generating artificial intelligence,

[1593] [A means of generating virtual reality simulation images based on collected data using artificial intelligence,

[1594] [Means for providing the above analysis results and virtual reality simulation images to the user's terminal,

[1595] [Means for receiving product search requests from the user's terminal and displaying the above analysis results and virtual reality simulation images,

[1596] [Means for displaying descriptions generated based on product data collected on the user's device,

[1597] [A means for generating virtual reality simulation images based on collected product data and displaying those images,

[1598] A system that includes this.

[1599] (Claim 2)

[1600] [A system according to claim 1 that displays product price comparison information and a description generated based on collected product data.

[1601] (Claim 3)

[1602] [The system according to claim 1, which analyzes and summarizes product review information and displays it together with collected product data and virtual reality simulation images.

[1603] "Example 2 of combining an emotion engine"

[1604] (Claim 1)

[1605] [Methods for collecting product data from multiple e-commerce sites,

[1606] [A means of analyzing and verbalizing collected data using generating artificial intelligence,

[1607] [A means of generating virtual reality simulation images based on collected data using artificial intelligence,

[1608] [A means of obtaining user emotional information using an emotion engine and passing it to an artificial intelligence that generates it,

[1609] [Means for providing the above analysis results and virtual reality simulation images to the user's terminal,

[1610] [Means for receiving product search requests from the user's terminal and displaying the above analysis results and virtual reality simulation images,

[1611] A system that includes this.

[1612] (Claim 2)

[1613] [A system according to claim 1 for analyzing price comparison information of products.]

[1614] (Claim 3)

[1615] [A system according to claim 1 that analyzes and summarizes product review information.

[1616] (Claim 4)

[1617] [The system according to claim 1, which generates product recommendation information tailored to the user's emotions based on their emotional information.

[1618] "Application example 2 when combining with an emotional engine"

[1619] (Claim 1)

[1620] [Methods for collecting product data from multiple commercial sites,

[1621] [A means of analyzing and verbalizing collected data using generating artificial intelligence,

[1622] [A means of generating virtual reality simulation images based on collected data using artificial intelligence,

[1623] [A means of acquiring emotional information from the user's facial expressions and voice using an emotion recognition engine,

[1624] [Means for generating and providing personalized product recommendation information based on user sentiment information,

[1625] [Means for providing the above analysis results, virtual reality simulation images, and product recommendation information based on emotional information to the user's terminal,

[1626] [Means for receiving product search requests from the user's terminal and displaying the above analysis results, virtual reality simulation images, and product recommendation information based on sentiment information,

[1627] A system that includes this.

[1628] (Claim 2)

[1629] [A system according to claim 1 for analyzing price comparison information of products.]

[1630] (Claim 3)

[1631] [A system according to claim 1 that analyzes and summarizes product review information. [Explanation of Symbols]

[1632] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting product data from multiple online shops, A means of analyzing and verbalizing collected data using generating artificial intelligence, A means for generating virtual reality simulation images based on collected data using artificial intelligence, A means for providing the above analysis results and virtual reality simulation images to the user's terminal, A means for receiving product search requests from the user's terminal and displaying the above analysis results and virtual reality simulation images, A system that includes this.

2. The system according to claim 1 for analyzing price comparison information of products.

3. The system according to claim 1, which analyzes and summarizes product review information.

Images