system

Abstract

We provide the system. [Solution] A means of receiving requests from users related to online sales, A means of analyzing received requests and extracting classifications and conditions, A means for collecting product information and evaluation information from multiple sources based on extracted classifications and conditions, A means of analyzing the collected information to identify and rank products that meet user requirements, An information display means that presents the analysis results to the user in an easy-to-understand visual way, In order to provide decision-making support functions via terminals, a means of providing links to streamline buying and selling procedures, A system that includes this.

Description

Technical Field

[0001] The technology of this 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 modern online shopping, there is a problem that when a user selects a product, they are faced with a vast amount of information and it requires a lot of time and effort to deal with it. Especially in the comparison of product reviews, price information, and understanding of features, it is difficult for users to make a quick and efficient judgment. Under such circumstances, there is a need for means to support users to make a quick and reasonable purchase decision.

Means for Solving the Problems

[0005] This invention provides a system that analyzes online purchase requests received from users and collects relevant product and evaluation information from a database based on specified categories and conditions. Furthermore, this system analyzes the collected information, scores and summarizes it based on specific conditions, and identifies and ranks the most suitable products for the user's requests. By presenting these results to the user in a user-friendly format, the system helps users make quick purchase decisions and enables a smooth transition to the product purchase process.

[0006] A "user" is an entity that makes online purchases, and is someone who selects products and completes the purchase process through the system.

[0007] "Requests" refer to the demands and conditions that users make to the system regarding online shopping.

[0008] "Analysis" is the process of processing user requests as structured information, and includes the act of extracting categories and conditions.

[0009] A "category" is a set of criteria used to classify products and is an element used to identify a specific group of products.

[0010] "Conditions" refer to specific criteria or constraints that are considered when selecting products based on user requirements.

[0011] A "database" refers to a collection of external or internal information platforms that a system accesses to retrieve product information and evaluation data.

[0012] "Information gathering" refers to the act of obtaining necessary product information and evaluation information from relevant databases based on user requests.

[0013] "Analysis results" refer to the results of identifying and ranking products that meet user requirements based on the collected information.

[0014] A "user interface" is a design element that enables interaction between a system and a user, and it is a medium for receiving and displaying information.

[0015] A "purchase page" refers to a specific section of an online site where users can actually complete the purchase process for selected products. [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 a data processing device and a 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] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 Example 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the 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] This invention provides an advanced AI-powered information analysis system to address the information overload users face when shopping online. This system receives user requests, efficiently collects and analyzes product and review information based on those requests, and recommends the most suitable products.

[0038] Specifically, the server analyzes requests received from users using natural language processing technology. This analysis extracts categories and desired conditions. Based on the extracted information, the server collects product information and reviews from multiple e-commerce sites via relevant databases and APIs.

[0039] Next, the server analyzes this information and applies an algorithm to summarize the collected evaluation data, extracting key points. This allows it to identify common positive or negative opinions from numerous reviews, such as those regarding "camera performance," and evaluate the product through scoring.

[0040] The server then filters the scored products to match the user's criteria and creates a ranking. This result is displayed to the user via the terminal. The user interface organizes the information and presents it in a visually easy-to-understand format so that the user can easily compare products.

[0041] The user can make a final purchase decision based on the information presented, and the device provides a link to the purchase page related to the selected product, allowing for a simple checkout process.

[0042] As a concrete example, suppose a user enters the request, "I want to find a smartphone with a good camera performance within a budget of 50,000 yen." In this case, the server analyzes this request and suggests several smartphones with high camera performance ratings within the 50,000 yen budget. For each smartphone, the server summarizes the camera performance review and presents it to the user.

[0043] Through the above processes, users can efficiently gather information and make quick purchasing decisions. This system greatly improves the online shopping experience by providing users with the information they need, neither too much nor too little.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device receives requests from users regarding online shopping. Users use input forms or voice input to send requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen."

[0047] Step 2:

[0048] The server analyzes the user's request received from the terminal using natural language processing technology. This analysis extracts specific elements such as "Category: Smartphone," "Condition: Budget under 50,000 yen," and "Interest: Camera performance."

[0049] Step 3:

[0050] The server uses APIs from e-commerce sites, social media platforms, and video streaming sites to collect product information and related reviews from databases in order to obtain product information that matches categories and conditions based on the analysis.

[0051] Step 4:

[0052] The server applies algorithms to summarize and organize the collected information. In particular, it extracts opinions related to specific features (e.g., camera performance) from numerous ratings and reviews, and calculates a product score based on them.

[0053] Step 5:

[0054] The server organizes products into a ranking format, scoring them to best match the user's requirements. This ranking is adjusted based on the user's specified budget and preferences.

[0055] Step 6:

[0056] The terminal displays product rankings and summary information received from the server in a visually easy-to-understand format for the user. It provides an interface that allows users to compare the features and scores of each product.

[0057] Step 7:

[0058] The user makes a final purchase decision based on the information presented. For selected products, the device provides a link to the relevant purchase page, assisting the user in proceeding directly with the purchase process.

[0059] (Example 1)

[0060] 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."

[0061] When shopping online, users are often overwhelmed by the vast amount of product information and reviews, making it difficult to make the best choice. Furthermore, quickly finding the right product requires organizing and comparing information, which is time-consuming and laborious. Therefore, there is a growing need for systems that can efficiently search for products and provide product information tailored to user needs.

[0062] 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.

[0063] In this invention, the server includes means for acquiring purchase-related requests received from users via an information processing device, means for analyzing the requests using a generative AI model and extracting categories and conditions, and means for collecting product information and evaluations via information sources based on the extracted categories and conditions. This enables users to save time and effort while reliably acquiring necessary product information and making optimal purchasing decisions.

[0064] A "user" refers to an individual or group that uses the system to search for and purchase products online.

[0065] An "information processing device" refers to a device used by users to input requests and transmit them to a server, and includes computers, smartphones, tablets, and other similar devices.

[0066] A "generative AI model" refers to an artificial intelligence algorithm or program used for natural language processing, which analyzes input text information to extract categories and conditions.

[0067] A "category" refers to a set of criteria or groups used to classify products based on user requirements.

[0068] "Conditions" refer to the elements that serve as criteria for selecting a product based on user requirements, and include price, performance, and functionality.

[0069] "Information sources" refer to external databases, APIs, and online platforms that provide product information and reviews.

[0070] "Product information" refers to data such as product characteristics, specifications, and price, which users refer to when making purchasing decisions.

[0071] "Reviews" refer to product reviews and feedback related to user requirements, and include opinions on product quality and performance.

[0072] A "server" refers to a central computing system that analyzes requests received from users, collects necessary information, and processes it based on that information.

[0073] "User interface" refers to the screens and operating methods that allow users to visually confirm and operate the system's inputs and outputs.

[0074] This invention is an information processing system designed to solve the problem of information overload in online shopping, and to support users in more efficiently selecting products and making purchasing decisions.

[0075] The system first receives the user's purchase request via a terminal. The user inputs their request into the terminal in natural language, providing content such as, "I want a smartphone with good camera performance for under 50,000 yen." This can be done using a general information processing device such as a PC, smartphone, or tablet.

[0076] The request is sent to the server and analyzed using a generative AI model. This AI model uses software with natural language processing technology, such as OpenAI's GPT-4®. The server extracts categories (e.g., smartphones) and conditions (e.g., budget, performance) from the input request.

[0077] Based on the extracted information, the server accesses various e-commerce platforms and databases to collect product information and ratings. This operation utilizes data acquisition functions such as the Amazon Product Advertising API and Rakuten API. The collected data, including review information, is used to score product performance.

[0078] The analyzed information is scored by the server, and the product best suited to the request is selected. The optimized list is presented to the user via the user interface. It is ranked in a visually easy-to-understand format and arranged to allow the user to easily compare and consider the products.

[0079] The terminal provides easy access to selected products as a purchasing aid. It offers links to purchase pages for the products selected by the user, facilitating a quick and efficient purchase process.

[0080] As a concrete example, a user might input a request into their device such as, "I want to find a smartphone with a good camera and a budget of 50,000 yen or less." The server would then analyze the request and present the user with a list of products that meet the criteria. An example of a prompt might be, "Please suggest a smartphone with a good camera and a budget of 50,000 yen or less."

[0081] As a result, users can quickly obtain information based on their needs and make purchasing decisions efficiently. This system enables accurate information collection and processing, significantly improving the online shopping experience.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user enters a purchase request using a terminal. This input is in natural language and may include specific requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen." The entered information is sent from the terminal to the server. In this step, the user's request is received as input data and prepared for analysis on the server.

[0085] Step 2:

[0086] The server analyzes requests received from users using a generation AI model. Specifically, it processes requests using natural language processing algorithms to extract categories and conditions. This data processing includes text analysis, resulting in output information such as categories (e.g., smartphones) and conditions (e.g., budget under 50,000 yen, camera performance).

[0087] Step 3:

[0088] The server accesses databases and APIs, which are the source of information, to collect product information and ratings based on the extracted categories and conditions. Data processing used includes API calls and data filtering, specifically the Amazon Product Advertising API and the Rakuten API. The input is the extracted categories and conditions, and the output is a list of detailed product information and reviews for the relevant products.

[0089] Step 4:

[0090] The server analyzes the collected product information and reviews. This analysis uses a text analysis algorithm to summarize the review content and process the data to score the product's performance. The input data is review information, and the output is the summarized reviews and the score for each product. Specifically, positive opinions about "camera performance" are aggregated and expressed as a numerical score.

[0091] Step 5:

[0092] The server ranks products that match the user's requirements based on the analysis results. Specifically, it sorts the scored products according to the user's criteria and lists them in the optimal order. Here, the ranking of products is output in order of score.

[0093] Step 6:

[0094] The terminal presents ranking information transmitted from the server to the user in a visually easy-to-understand format. This presentation includes information organization and visual design. The input is ranked product information, and the output is in a comparable display format. Specifically, it uses tables and graphs to highlight product features.

[0095] Step 7:

[0096] The user makes a final purchase decision based on the information displayed on the device. For the selected product, the device provides a link to the purchase page. This allows the user to access the specified online sales page with a click and proceed with the purchase. The input is the user's selection, and the output is a link to the purchase page.

[0097] (Application Example 1)

[0098] 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."

[0099] In online sales, many users often find it difficult to choose the best product due to information overload. Furthermore, the cumbersome purchasing process can diminish their desire to buy. Additionally, it's challenging to provide users with valuable purchasing support while efficiently utilizing summarized evaluation information. Against this backdrop, there is a need for a system that allows users to make efficient decisions and complete the purchasing process smoothly.

[0100] 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.

[0101] In this invention, the server includes means for receiving requests from users related to online sales, means for analyzing the received requests and extracting classifications and conditions, means for collecting product information and evaluation information from multiple information sources based on the extracted classifications and conditions, means for analyzing the collected information to identify and rank products that meet the user's requirements, means for displaying information to present the analysis results to the user in an easy-to-understand visual format, means for providing links to streamline buying and selling procedures in order to provide decision-making support functions via the terminal, and means for summarizing the collected evaluation information, assigning indicators in association with specific conditions during analysis, and further generating and utilizing prompt sentences using a generative AI model to support interaction with the user. This enables users to efficiently select the optimal product and smoothly complete the purchasing process.

[0102] A "user" is someone who uses the system to purchase goods online.

[0103] "Requests related to online sales" refers to information that describes the specific conditions and preferences of the products a user wishes to purchase.

[0104] "Classification" refers to the metrics and criteria used to categorize related products based on user requirements.

[0105] "Conditions" refer to specific specifications or requirements that users prioritize when choosing a product.

[0106] "Information sources" refer to multiple databases, APIs, etc., used to obtain product information and evaluation data.

[0107] "Product information" refers to detailed information about a specific product, including technical specifications, price, and availability.

[0108] "Evaluation information" refers to opinions and reviews from consumers and experts regarding the user experience and quality of a product.

[0109] "Analyzing collected information" is the process of processing data to extract useful meanings and patterns.

[0110] "Presenting information visually in an easy-to-understand manner" refers to displaying information using graphs, charts, and other visual aids so that users can easily understand and utilize it.

[0111] A "decision support function" is a feature that provides advice and recommendations to help users select the most suitable product.

[0112] "Providing links" means setting up a means of connecting to a webpage so that users can directly access the purchase page of a product they are interested in.

[0113] A "generative AI model" is an artificial intelligence technology used to generate text data in tasks such as natural language processing.

[0114] A "prompt message" is a sentence created by a generative AI model that serves as a response or suggestion to support the user's conversation and decision-making.

[0115] The system implementing this invention assists users in selecting the optimal product for online sales and efficiently proceeding with the purchasing process. The server first receives requests from users via a terminal and analyzes these requests using natural language processing technology. During the analysis process, classifications and conditions based on the user's requests are extracted.

[0116] Next, the server gathers product and evaluation information from multiple sources based on the collected classifications and conditions. These sources include various databases and APIs. The collected information is analyzed by the server to identify products that match the user's requirements and rank them. Evaluation information is summarized, and prompt messages are generated using a generative AI model. This ensures that information is provided in a format easily understood by the user.

[0117] The user interface displays product information in a visually easy-to-understand format. Furthermore, the terminal provides links to the purchase page for the product selected by the user, streamlining the purchasing process. For example, if a user enters "I'm looking for headphones with good sound quality for under 50,000 yen," the server suggests relevant products and generates a prompt summarizing review information. Based on this prompt, the user can easily make a decision. The conversation then progresses with prompts such as, "I'm looking for headphones with good sound quality for under 30,000 yen. Please tell me about related products and their ratings."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server receives online sales-related requests from users via terminals. These requests include specific conditions and characteristics of the products the user wishes to purchase. The server receives natural language text as input, analyzes the request using natural language processing techniques, and extracts the classifications and conditions requested by the user. The output is the generated classifications and conditions.

[0121] Step 2:

[0122] The server collects product and evaluation information from multiple sources based on the classifications and conditions extracted in Step 1. These sources consist of product databases and APIs. This process retrieves relevant data from the sources and aggregates it on the server. The inputs are the extracted classifications and conditions, and the outputs are the collected product and evaluation information.

[0123] Step 3:

[0124] The server analyzes collected product and evaluation information to identify products that match the user's criteria. This analysis utilizes statistical methods and machine learning algorithms. Specifically, it comprehensively compares and analyzes product performance, price, and evaluation scores. The input is all collected information, and the output is a list of identified products.

[0125] Step 4:

[0126] The server summarizes the evaluation information of the identified product and uses a generative AI model to generate prompt sentences suitable for user interaction. This process summarizes a vast amount of review information, extracts important opinions and evaluations, and converts them into prompt sentences. The input is the evaluation information, and the output is the generated prompt sentences.

[0127] Step 5:

[0128] The terminal builds a user interface to visually present the analysis results received from the server to the user in an easy-to-understand manner. This process uses graphs, lists, highlighting, etc., to make the information easy for the user to understand. The input is the analyzed product information and prompt text, and the output is the display of the information on the user interface.

[0129] Step 6:

[0130] The terminal provides a purchase link to the product selected by the user, streamlining the purchasing process. In this step, a direct link to the purchase page of the selected product is generated and presented to the user. The input is the user's product selection information, and the output is the purchase link.

[0131] These steps enable users to efficiently select the optimal product online and easily complete the purchase process.

[0132] 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.

[0133] This invention aims to provide more personalized product recommendations and information presentation in an online shopping system that supports users' purchasing activities by integrating an emotion engine that recognizes users' emotions. This system provides product information while considering not only the user's requests but also their emotional state.

[0134] Specifically, the server receives purchase requests from users, analyzes these requests using natural language processing, and extracts categories and conditions. This forms the basis for collecting information about products that the user is interested in. Furthermore, the system uses an emotion recognition engine to infer the user's emotional state. This emotion recognition process is carried out by measuring text and voice data entered by the user, as well as biometric information such as facial expressions.

[0135] The server collects relevant product information based on the extracted categories and conditions. This includes product information, reviews, and ratings obtained through multiple databases and APIs. The sentiment information provided by the sentiment engine is used in the product recommendation process to influence the user's preferences, stress levels, and willingness to purchase. For example, when the user is relaxed, detailed specifications are presented, while when the user is highly stressed, concise and to-the-point recommendations are given.

[0136] Based on the collected and analyzed information, the server selects the most suitable products based on the user's requests and emotions, and organizes them in a ranking format. This product information is presented to the user visually and audibly through the terminal. The user interface dynamically changes the tone and layout of the display according to the user's emotional state to improve the user experience.

[0137] For example, if a user requests, "I want a perfume with a scent that will lift my spirits in my busy daily life," the server analyzes the request and prepares perfumes with high relaxation effects. If emotion recognition detects that the user is showing signs of fatigue, the device simply presents perfume options and provides a concise explanation of their effects, thereby reducing stress and encouraging purchase.

[0138] Thus, by taking into account the user's emotions, the present invention can not only recommend products but also provide an optimal purchasing experience tailored to the user's psychological state.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The device receives requests from the user regarding online shopping. At this stage, the user sends a request using text input or voice input, for example, "I want you to find me a perfume that makes me feel good."

[0142] Step 2:

[0143] The server analyzes the user's request sent from the terminal using natural language processing technology to extract categories and conditions. This identifies specific data such as "Category: Perfume" and "Purpose: To improve mood."

[0144] Step 3:

[0145] The device uses built-in or external sensors to acquire the user's emotional state. This includes methods such as text analysis, voice tone analysis, and data acquisition from biosensors.

[0146] Step 4:

[0147] The server uses an emotion recognition engine to analyze emotional data acquired from the terminal. This analysis determines the user's current emotional state (e.g., relaxed, stressed, excited, etc.).

[0148] Step 5:

[0149] The server collects relevant product and review information from multiple e-commerce sites and product databases based on the extracted categories and conditions, as well as the emotional state.

[0150] Step 6:

[0151] The server analyzes the collected information and scores the product list. Taking into account the results of the emotion engine, it assigns a high score to the product that best fits the user's emotions.

[0152] Step 7:

[0153] The server filters products based on the scoring results and creates a list of recommended products. This list is ranked according to the user's emotional state.

[0154] Step 8:

[0155] The terminal presents the user with a product list provided by the server. Here, the user interface optimizes the visual presentation according to the user's emotional state, providing a simple layout for relaxed browsing and stress avoidance.

[0156] Step 9:

[0157] The user selects an item to purchase from the displayed products. The device provides a link to the purchase page associated with that item, allowing the user to complete the purchase process smoothly.

[0158] (Example 2)

[0159] 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".

[0160] In modern online shopping, it has become difficult for users to efficiently select the right product from a vast amount of product information during their purchasing process. Furthermore, because information is presented uniformly without considering the user's emotional state, there is a problem in effectively stimulating their purchasing intent. Traditional systems can only personalize information to a limited extent in response to user requests, lacking the ability to present optimal information based on psychological state.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for receiving requests from users related to online purchases, means for analyzing the received requests to extract the attributes and conditions of items, means for collecting product information and opinion information from multiple sources based on the extracted attributes and conditions, means for inferring the emotional state based on the user's biometric information and incorporating the emotional information into the analysis to identify products, and means for ranking the identified products according to the user's emotional state and presenting them in an appropriate display format. This enables personalized product recommendations and information presentation that take into account the user's emotional state, thereby improving the user's purchasing experience.

[0163] A "user" is someone who uses an online system to obtain product information and consider making a purchase.

[0164] "Online purchase-related requests" refer to purchasing interests expressed by users through the internet, specifically indicating their desires and needs.

[0165] "Item attributes" refer to information related to the characteristics and features of a product.

[0166] "Conditions" refer to specific criteria or constraints that should be considered when selecting a product.

[0167] "Information sources" refer to databases or external systems from which product information and opinions are obtained.

[0168] "Product information" refers to information such as detailed descriptions, prices, characteristics, and availability of a product.

[0169] "Opinion information" refers to opinions about a product based on reviews and ratings provided by users or third parties.

[0170] "Biometric information" refers to data related to the user's body, such as their heart rate and facial expressions.

[0171] "Emotional state" refers to the psychological or emotional condition a user exhibits at a particular point in time.

[0172] "Display format" refers to the form or method by which collected information is presented to the user visually or audibly.

[0173] This invention relates to a system designed to support user purchasing activities in an online shopping system. The server receives user requests via an interface, analyzes the text using natural language processing techniques, and extracts attributes and conditions related to the products. General-purpose natural language processing software is used for this purpose; specifically, natural language APIs and data analysis tools are employed.

[0174] Next, the server interacts with multiple information sources and external databases to collect product information and reviews. Common database communication protocols and APIs are implemented here. The collected data is input into an integrated emotion recognition engine, which infers the user's emotional state. This process uses technology to identify emotional states based on biometric information provided by the user.

[0175] For example, if a user requests to "relax," the server analyzes this request and collects relevant perfume product information. Then, an emotion recognition engine detects the user's fatigue level from their facial expressions and entered text. Based on this information, it becomes possible to concisely present perfume options.

[0176] The terminal presents the product information received from the server during this process to the user visually or audibly. The user interface dynamically changes according to the user's emotional state to provide a better experience. For example, by inputting a prompt message such as "List recommended products when the user is feeling stressed and briefly explain the reasons" into the generating AI model, optimal product recommendations can be obtained. In this way, the present invention realizes an interactive shopping experience that takes the user's emotions into consideration.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server receives purchase requests from users via terminals. The input consists of requests provided by the user in text or voice. This input is analyzed using natural language processing techniques to identify product categories and related conditions. The output is a list of the analyzed categories and conditions.

[0180] Step 2:

[0181] The server collects product and opinion information from databases and external sources based on the analysis results. It uses a list of categories and conditions as input. It retrieves information via a database communication protocol and performs data calculations to format the necessary data. The output is a set of collected product and opinion information.

[0182] Step 3:

[0183] The server inputs biometric information from the user into an emotion recognition engine to infer the user's emotional state. The input consists of facial expression data and voice data acquired from the device's camera and microphone. An emotion analysis algorithm is applied to perform data calculations that evaluate the user's psychological state. As output, a label indicating the emotional state is generated.

[0184] Step 4:

[0185] The server integrates collected product information and sentiment information to identify and rank products according to the user's emotional state. It inputs prompt sentences into a generating AI model to provide optimal product recommendations. The input consists of a product information set and sentiment labels. A product ranking algorithm that considers sentiment information is applied to process the data and select recommendation candidates. The output is a list of recommended products.

[0186] Step 5:

[0187] The terminal dynamically changes the display format based on product information sent from the server, according to the user's emotional state, and presents it to the user. Inputs are a list of recommended products and emotional labels. The terminal adjusts the user interface and performs specific actions to provide information visually and aurally. The output is optimized product information presented to the user.

[0188] (Application Example 2)

[0189] 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".

[0190] While current online shopping systems offer product recommendations based on user requests, they lack sufficient personalization that takes into account the user's emotional state. As a result, users often experience stress during the purchase decision process and have difficulty finding suitable products. This issue particularly impacts the consistency of the user interface and the way information is presented, posing a barrier to providing an optimal shopping experience.

[0191] 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.

[0192] In this invention, the server includes means for receiving user purchase requests, means for analyzing requests and extracting categories and conditions, means for collecting product information based on the extracted categories and conditions, means for estimating emotions from user input data and making product recommendations based on emotional information, and means for dynamically changing the display tone and layout when presenting information according to the user's emotional state. This enables users to quickly find the optimal product while reducing stress according to their emotional state.

[0193] "Means of receiving requests from users related to online purchases" refers to interfaces and processes for receiving requests from users of online shopping platforms regarding product searches and purchases.

[0194] "Methods for analyzing requests and extracting categories and conditions" refers to the process of analyzing requests received from users using technologies such as natural language processing to identify the category of the requested product and specific conditions.

[0195] "Means for collecting product information based on extracted categories and conditions" refers to a function that retrieves relevant product and evaluation information from databases or external sources according to the obtained categories and conditions.

[0196] "A means of estimating emotions from user input data and making product recommendations based on emotional information" refers to a system that uses text, voice, and facial expression data provided by the user to infer the user's emotions, and then selects and suggests the most suitable product for the user based on the results.

[0197] "Means of dynamically changing the display tone and layout when presenting information according to the user's emotional state" refers to technology that adjusts the color scheme and arrangement of information displayed on the screen in real time, taking the user's emotions into consideration.

[0198] The system for realizing this invention includes a program that recommends products by taking into account the user's needs and emotional state in order to improve the user's online shopping experience.

[0199] The server receives requests from users related to online purchases, including text input and voice data. The server analyzes these requests using a natural language processing engine to extract product categories and conditions. This method forms the basis for efficiently collecting specific product information from multiple databases. Furthermore, the server runs an emotion recognition engine to estimate emotions from the user's input data. This process is based on nuances in text and voice, and in some cases, facial expression images captured by the device.

[0200] The server then adjusts the product recommendation process based on emotional information. For example, if the emotional engine determines that the user is relaxed, it will present detailed product specifications. On the other hand, if it detects that the user is stressed, it will provide concise information summarizing the key points of the product. This helps to reduce stress caused by information overload.

[0201] The tone and layout of the information presented are dynamically changed by the device in response to the user's emotions. This adjustment makes the user experience more personalized and, as a result, contributes to increasing the user's willingness to purchase.

[0202] For example, if a user requests a book to help them relax on their day off, the system will recommend relaxing books (such as essays or short stories) and provide a concise summary. This ensures that the tired user receives appropriate product information, leading to a quicker purchase decision.

[0203] By utilizing generative AI models, even more accurate product recommendations become possible. A concrete example of a prompt would be, "Please tell me how to use an emotion recognition engine to recommend products suitable for relaxation based on the user's request to 'want to relax.'"

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server receives requests from users regarding online shopping. The input includes the user's product preferences, entered via text or voice. This allows the system to obtain initial data for the next process.

[0207] Step 2:

[0208] The server uses a natural language processing engine to parse incoming requests. During this process, product categories and conditions are extracted. The input is the user's request text, and the output is the parsed product categories and conditions. This information forms the basis for product information collection.

[0209] Step 3:

[0210] The server searches and collects relevant product information and reviews from the database based on the analyzed categories and conditions. The input is the output of step 2, and the output provides detailed product data and review information collected. This ensures that product information that matches the user's needs is gathered.

[0211] Step 4:

[0212] The server uses an emotion recognition engine to estimate the user's current emotional state from the user's input data. The input is the user's text or voice data received in step 1, and the output is the estimated user's emotional information. This information is used to refine product recommendations.

[0213] Step 5:

[0214] The server makes product recommendations based on emotional information. If fatigue or stress is detected, the recommended product information is condensed for clarity. The input is the output from steps 3 and 4, and an optimized product recommendation list is created as the output.

[0215] Step 6:

[0216] The device dynamically changes the display tone and layout of information, taking into account the user's emotional state. The input is the output of step 5, and it is output as an easy-to-view and emotionally sensitive interface. This speeds up the purchasing decision process.

[0217] Step 7:

[0218] Users select and purchase products based on the product information displayed on their devices. The final output is the completion of the user's purchasing action. By utilizing a generative AI model, it is expected that user satisfaction will be further improved.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention provides an advanced AI-powered information analysis system to address the information overload users face when shopping online. This system receives user requests, efficiently collects and analyzes product and review information based on those requests, and recommends the most suitable products.

[0236] Specifically, the server analyzes requests received from users using natural language processing technology. This analysis extracts categories and desired conditions. Based on the extracted information, the server collects product information and reviews from multiple e-commerce sites via relevant databases and APIs.

[0237] Next, the server analyzes this information and applies an algorithm to summarize the collected evaluation data, extracting key points. This allows it to identify common positive or negative opinions from numerous reviews, such as those regarding "camera performance," and evaluate the product through scoring.

[0238] The server then filters the scored products to match the user's criteria and creates a ranking. This result is displayed to the user via the terminal. The user interface organizes the information and presents it in a visually easy-to-understand format so that the user can easily compare products.

[0239] The user can make a final purchase decision based on the information presented, and the device provides a link to the purchase page related to the selected product, allowing for a simple checkout process.

[0240] As a concrete example, suppose a user enters the request, "I want to find a smartphone with a good camera performance within a budget of 50,000 yen." In this case, the server analyzes this request and suggests several smartphones with high camera performance ratings within the 50,000 yen budget. For each smartphone, the server summarizes the camera performance review and presents it to the user.

[0241] Through the above processes, users can efficiently gather information and make quick purchasing decisions. This system greatly improves the online shopping experience by providing users with the information they need, neither too much nor too little.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The device receives requests from users regarding online shopping. Users use input forms or voice input to send requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen."

[0245] Step 2:

[0246] The server analyzes the user's request received from the terminal using natural language processing technology. This analysis extracts specific elements such as "Category: Smartphone," "Condition: Budget under 50,000 yen," and "Interest: Camera performance."

[0247] Step 3:

[0248] The server uses APIs from e-commerce sites, social media platforms, and video streaming sites to collect product information and related reviews from databases in order to obtain product information that matches categories and conditions based on the analysis.

[0249] Step 4:

[0250] The server applies algorithms to summarize and organize the collected information. In particular, it extracts opinions related to specific features (e.g., camera performance) from numerous ratings and reviews, and calculates a product score based on them.

[0251] Step 5:

[0252] The server organizes products into a ranking format, scoring them to best match the user's requirements. This ranking is adjusted based on the user's specified budget and preferences.

[0253] Step 6:

[0254] The terminal displays product rankings and summary information received from the server in a visually easy-to-understand format for the user. It provides an interface that allows users to compare the features and scores of each product.

[0255] Step 7:

[0256] The user makes a final purchase decision based on the information presented. For selected products, the device provides a link to the relevant purchase page, assisting the user in proceeding directly with the purchase process.

[0257] (Example 1)

[0258] 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."

[0259] When shopping online, users are often overwhelmed by the vast amount of product information and reviews, making it difficult to make the best choice. Furthermore, quickly finding the right product requires organizing and comparing information, which is time-consuming and laborious. Therefore, there is a growing need for systems that can efficiently search for products and provide product information tailored to user needs.

[0260] 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.

[0261] In this invention, the server includes means for acquiring purchase-related requests received from users via an information processing device, means for analyzing the requests using a generative AI model and extracting categories and conditions, and means for collecting product information and evaluations via information sources based on the extracted categories and conditions. This enables users to save time and effort while reliably acquiring necessary product information and making optimal purchasing decisions.

[0262] A "user" refers to an individual or group that uses the system to search for and purchase products online.

[0263] An "information processing device" refers to a device used by users to input requests and transmit them to a server, and includes computers, smartphones, tablets, and other similar devices.

[0264] A "generative AI model" refers to an artificial intelligence algorithm or program used for natural language processing, which analyzes input text information to extract categories and conditions.

[0265] A "category" refers to a set of criteria or groups used to classify products based on user requirements.

[0266] "Conditions" refer to the elements that serve as criteria for selecting a product based on user requirements, and include price, performance, and functionality.

[0267] "Information sources" refer to external databases, APIs, and online platforms that provide product information and reviews.

[0268] "Product information" refers to data such as product characteristics, specifications, and price, which users refer to when making purchasing decisions.

[0269] "Reviews" refer to product reviews and feedback related to user requirements, and include opinions on product quality and performance.

[0270] A "server" refers to a central computing system that analyzes requests received from users, collects necessary information, and processes it based on that information.

[0271] "User interface" refers to the screens and operating methods that allow users to visually confirm and operate the system's inputs and outputs.

[0272] This invention is an information processing system designed to solve the problem of information overload in online shopping, and to support users in more efficiently selecting products and making purchasing decisions.

[0273] The system first receives the user's purchase request via a terminal. The user inputs their request into the terminal in natural language, providing content such as, "I want a smartphone with good camera performance for under 50,000 yen." This can be done using a general information processing device such as a PC, smartphone, or tablet.

[0274] The request is sent to the server and analyzed using a generative AI model. This AI model uses software with natural language processing technology, such as OpenAI's GPT-4. The server extracts categories (e.g., smartphones) and conditions (e.g., budget, performance) from the input request.

[0275] Based on the extracted information, the server accesses various e-commerce platforms and databases to collect product information and ratings. This operation utilizes data acquisition functions such as the Amazon Product Advertising API and Rakuten API. The collected data, including review information, is used to score product performance.

[0276] The analyzed information is scored by the server, and the product best suited to the request is selected. The optimized list is presented to the user via the user interface. It is ranked in a visually easy-to-understand format and arranged to allow the user to easily compare and consider the products.

[0277] The terminal provides easy access to selected products as a purchasing aid. It offers links to purchase pages for the products selected by the user, facilitating a quick and efficient purchase process.

[0278] As a concrete example, a user might input a request into their device such as, "I want to find a smartphone with a good camera and a budget of 50,000 yen or less." The server would then analyze the request and present the user with a list of products that meet the criteria. An example of a prompt might be, "Please suggest a smartphone with a good camera and a budget of 50,000 yen or less."

[0279] As described above, users can quickly obtain information based on their own needs and make purchase decisions efficiently. This system enables accurate collection and processing of information, significantly improving the online shopping experience.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user uses the terminal to input a purchase request. This input is made in natural language, specifically something like "I want to find a smartphone with excellent camera performance within a budget of 50,000 yen." The input information is sent from the terminal to the server. In this step, the user's request is obtained as input data in preparation for analysis on the server.

[0283] Step 2:

[0284] The server analyzes the request received from the user using the generated AI model. Specifically, it processes the request with natural language processing algorithms to extract categories and conditions. The data processing here includes text analysis, and as a result, information such as categories (e.g., smartphone) and conditions (e.g., within a budget of 50,000 yen, camera performance) is output.

[0285] Step 3:

[0286] Based on the extracted categories and conditions, the server accesses databases and APIs that are information sources to collect information and evaluations about products. The data processing used includes API calls and data filtering. Specifically, the Amazon Product Advertising API and the Rakuten API are used. The input is the extracted categories and conditions, and the output is a list of detailed information and reviews of the corresponding products.

[0287] Step 4:

[0288] The server analyzes the collected product information and reviews. This analysis uses a text analysis algorithm to summarize the review content and process the data to score the product's performance. The input data is review information, and the output is the summarized reviews and the score for each product. Specifically, positive opinions about "camera performance" are aggregated and expressed as a numerical score.

[0289] Step 5:

[0290] The server ranks products that match the user's requirements based on the analysis results. Specifically, it sorts the scored products according to the user's criteria and lists them in the optimal order. Here, the ranking of products is output in order of score.

[0291] Step 6:

[0292] The terminal presents ranking information transmitted from the server to the user in a visually easy-to-understand format. This presentation includes information organization and visual design. The input is ranked product information, and the output is in a comparable display format. Specifically, it uses tables and graphs to highlight product features.

[0293] Step 7:

[0294] The user makes a final purchase decision based on the information displayed on the device. For the selected product, the device provides a link to the purchase page. This allows the user to access the specified online sales page with a click and proceed with the purchase. The input is the user's selection, and the output is a link to the purchase page.

[0295] (Application Example 1)

[0296] 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."

[0297] In online sales, many users often find it difficult to choose the best product due to information overload. Furthermore, the cumbersome purchasing process can diminish their desire to buy. Additionally, it's challenging to provide users with valuable purchasing support while efficiently utilizing summarized evaluation information. Against this backdrop, there is a need for a system that allows users to make efficient decisions and complete the purchasing process smoothly.

[0298] 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.

[0299] In this invention, the server includes means for receiving requests from users related to online sales, means for analyzing the received requests and extracting classifications and conditions, means for collecting product information and evaluation information from multiple information sources based on the extracted classifications and conditions, means for analyzing the collected information to identify and rank products that meet the user's requirements, means for displaying information to present the analysis results to the user in an easy-to-understand visual format, means for providing links to streamline buying and selling procedures in order to provide decision-making support functions via the terminal, and means for summarizing the collected evaluation information, assigning indicators in association with specific conditions during analysis, and further generating and utilizing prompt sentences using a generative AI model to support interaction with the user. This enables users to efficiently select the optimal product and smoothly complete the purchasing process.

[0300] A "user" is someone who uses the system to purchase goods online.

[0301] "Requests related to online sales" refers to information that describes the specific conditions and preferences of the products a user wishes to purchase.

[0302] "Classification" refers to the metrics and criteria used to categorize related products based on user requirements.

[0303] "Condition" refers to specific specifications or requirements that users prioritize when selecting products.

[0304] "Information source" refers to multiple databases, APIs, etc. used to obtain product information and evaluation information.

[0305] "Product information" refers to detailed information about a specific product, including technical specifications, price, inventory status, etc.

[0306] "Evaluation information" refers to opinions and reviews on the usability and quality of products by consumers and experts.

[0307] "Analyzing the collected information" is a process of processing data to extract useful meanings and patterns.

[0308] "Visually presenting clearly" means displaying information using graphs, charts, etc. so that users can easily understand and utilize it.

[0309] "Decision-making support function" is a function that provides advice and recommendations for users to select the optimal product.

[0310] "Providing a link" means installing a connection means to a web page so that users can directly access the purchase page of the product they are interested in.

[0311] "Generative AI model" is an artificial intelligence technology used to generate text data in tasks such as natural language processing.

[0312] "Prompt text" is a text of responses and suggestions created by a generative AI model to assist users' conversations and decision-making.

[0313] The system implementing this invention assists users in selecting the optimal product for online sales and efficiently proceeding with the purchasing process. The server first receives requests from users via a terminal and analyzes these requests using natural language processing technology. During the analysis process, classifications and conditions based on the user's requests are extracted.

[0314] Next, the server gathers product and evaluation information from multiple sources based on the collected classifications and conditions. These sources include various databases and APIs. The collected information is analyzed by the server to identify products that match the user's requirements and rank them. Evaluation information is summarized, and prompt messages are generated using a generative AI model. This ensures that information is provided in a format easily understood by the user.

[0315] The user interface displays product information in a visually easy-to-understand format. Furthermore, the terminal provides links to the purchase page for the product selected by the user, streamlining the purchasing process. For example, if a user enters "I'm looking for headphones with good sound quality for under 50,000 yen," the server suggests relevant products and generates a prompt summarizing review information. Based on this prompt, the user can easily make a decision. The conversation then progresses with prompts such as, "I'm looking for headphones with good sound quality for under 30,000 yen. Please tell me about related products and their ratings."

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The server receives online sales-related requests from users via terminals. These requests include specific conditions and characteristics of the products the user wishes to purchase. The server receives natural language text as input, analyzes the request using natural language processing techniques, and extracts the classifications and conditions requested by the user. The output is the generated classifications and conditions.

[0319] Step 2:

[0320] The server collects product and evaluation information from multiple sources based on the classifications and conditions extracted in Step 1. These sources consist of product databases and APIs. This process retrieves relevant data from the sources and aggregates it on the server. The inputs are the extracted classifications and conditions, and the outputs are the collected product and evaluation information.

[0321] Step 3:

[0322] The server analyzes collected product and evaluation information to identify products that match the user's criteria. This analysis utilizes statistical methods and machine learning algorithms. Specifically, it comprehensively compares and analyzes product performance, price, and evaluation scores. The input is all collected information, and the output is a list of identified products.

[0323] Step 4:

[0324] The server summarizes the evaluation information of the identified product and uses a generative AI model to generate prompt sentences suitable for user interaction. This process summarizes a vast amount of review information, extracts important opinions and evaluations, and converts them into prompt sentences. The input is the evaluation information, and the output is the generated prompt sentences.

[0325] Step 5:

[0326] The terminal builds a user interface to visually present the analysis results received from the server to the user in an easy-to-understand manner. This process uses graphs, lists, highlighting, etc., to make the information easy for the user to understand. The input is the analyzed product information and prompt text, and the output is the display of the information on the user interface.

[0327] Step 6:

[0328] The terminal provides a purchase link to the product selected by the user, streamlining the purchasing process. In this step, a direct link to the purchase page of the selected product is generated and presented to the user. The input is the user's product selection information, and the output is the purchase link.

[0329] These steps enable users to efficiently select the optimal product online and easily complete the purchase process.

[0330] 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.

[0331] This invention aims to provide more personalized product recommendations and information presentation in an online shopping system that supports users' purchasing activities by integrating an emotion engine that recognizes users' emotions. This system provides product information while considering not only the user's requests but also their emotional state.

[0332] Specifically, the server receives purchase requests from users, analyzes these requests using natural language processing, and extracts categories and conditions. This forms the basis for collecting information about products that the user is interested in. Furthermore, the system uses an emotion recognition engine to infer the user's emotional state. This emotion recognition process is carried out by measuring text and voice data entered by the user, as well as biometric information such as facial expressions.

[0333] The server collects relevant product information based on the extracted categories and conditions. This includes product information, reviews, and ratings obtained through multiple databases and APIs. The sentiment information provided by the sentiment engine is used in the product recommendation process to influence the user's preferences, stress levels, and willingness to purchase. For example, when the user is relaxed, detailed specifications are presented, while when the user is highly stressed, concise and to-the-point recommendations are given.

[0334] Based on the collected and analyzed information, the server selects the most suitable products based on the user's requests and emotions, and organizes them in a ranking format. This product information is presented to the user visually and audibly through the terminal. The user interface dynamically changes the tone and layout of the display according to the user's emotional state to improve the user experience.

[0335] For example, if a user requests, "I want a perfume with a scent that will lift my spirits in my busy daily life," the server analyzes the request and prepares perfumes with high relaxation effects. If emotion recognition detects that the user is showing signs of fatigue, the device simply presents perfume options and provides a concise explanation of their effects, thereby reducing stress and encouraging purchase.

[0336] Thus, by taking into account the user's emotions, the present invention can not only recommend products but also provide an optimal purchasing experience tailored to the user's psychological state.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] The device receives requests from the user regarding online shopping. At this stage, the user sends a request using text input or voice input, for example, "I want you to find me a perfume that makes me feel good."

[0340] Step 2:

[0341] The server analyzes the user's request sent from the terminal using natural language processing technology to extract categories and conditions. This identifies specific data such as "Category: Perfume" and "Purpose: To improve mood."

[0342] Step 3:

[0343] The device uses built-in or external sensors to acquire the user's emotional state. This includes methods such as text analysis, voice tone analysis, and data acquisition from biosensors.

[0344] Step 4:

[0345] The server uses an emotion recognition engine to analyze emotional data acquired from the terminal. This analysis determines the user's current emotional state (e.g., relaxed, stressed, excited, etc.).

[0346] Step 5:

[0347] The server collects relevant product and review information from multiple e-commerce sites and product databases based on the extracted categories and conditions, as well as the emotional state.

[0348] Step 6:

[0349] The server analyzes the collected information and scores the product list. Taking into account the results of the emotion engine, it assigns a high score to the product that best fits the user's emotions.

[0350] Step 7:

[0351] The server filters products based on the scoring results and creates a list of recommended products. This list is ranked according to the user's emotional state.

[0352] Step 8:

[0353] The terminal presents the user with a product list provided by the server. Here, the user interface optimizes the visual presentation according to the user's emotional state, providing a simple layout for relaxed browsing and stress avoidance.

[0354] Step 9:

[0355] The user selects an item to purchase from the displayed products. The device provides a link to the purchase page associated with that item, allowing the user to complete the purchase process smoothly.

[0356] (Example 2)

[0357] 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".

[0358] In modern online shopping, it has become difficult for users to efficiently select the right product from a vast amount of product information during their purchasing process. Furthermore, because information is presented uniformly without considering the user's emotional state, there is a problem in effectively stimulating their purchasing intent. Traditional systems can only personalize information to a limited extent in response to user requests, lacking the ability to present optimal information based on psychological state.

[0359] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0360] In this invention, the server includes means for receiving requests from users related to online purchases, means for analyzing the received requests to extract the attributes and conditions of items, means for collecting product information and opinion information from multiple sources based on the extracted attributes and conditions, means for inferring the emotional state based on the user's biometric information and incorporating the emotional information into the analysis to identify products, and means for ranking the identified products according to the user's emotional state and presenting them in an appropriate display format. This enables personalized product recommendations and information presentation that take into account the user's emotional state, thereby improving the user's purchasing experience.

[0361] A "user" is someone who uses an online system to obtain product information and consider making a purchase.

[0362] "Online purchase-related requests" refer to purchasing interests expressed by users through the internet, specifically indicating their desires and needs.

[0363] "Item attributes" refer to information related to the characteristics and features of a product.

[0364] "Conditions" refer to specific criteria or constraints that should be considered when selecting a product.

[0365] "Information sources" refer to databases or external systems from which product information and opinions are obtained.

[0366] "Product information" refers to information such as detailed descriptions, prices, characteristics, and availability of a product.

[0367] "Opinion information" refers to opinions about a product based on reviews and ratings provided by users or third parties.

[0368] "Biometric information" refers to data related to the user's body, such as their heart rate and facial expressions.

[0369] "Emotional state" refers to the psychological or emotional condition a user exhibits at a particular point in time.

[0370] "Display format" refers to the form or method by which collected information is presented to the user visually or audibly.

[0371] This invention relates to a system designed to support user purchasing activities in an online shopping system. The server receives user requests via an interface, analyzes the text using natural language processing techniques, and extracts attributes and conditions related to the products. General-purpose natural language processing software is used for this purpose; specifically, natural language APIs and data analysis tools are employed.

[0372] Next, the server interacts with multiple information sources and external databases to collect product information and reviews. Common database communication protocols and APIs are implemented here. The collected data is input into an integrated emotion recognition engine, which infers the user's emotional state. This process uses technology to identify emotional states based on biometric information provided by the user.

[0373] For example, if a user requests to "relax," the server analyzes this request and collects relevant perfume product information. Then, an emotion recognition engine detects the user's fatigue level from their facial expressions and entered text. Based on this information, it becomes possible to concisely present perfume options.

[0374] The terminal presents the product information received from the server during this process to the user visually or audibly. The user interface dynamically changes according to the user's emotional state to provide a better experience. For example, by inputting a prompt message such as "List recommended products when the user is feeling stressed and briefly explain the reasons" into the generating AI model, optimal product recommendations can be obtained. In this way, the present invention realizes an interactive shopping experience that takes the user's emotions into consideration.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The server receives purchase requests from users via terminals. The input consists of requests provided by the user in text or voice. This input is analyzed using natural language processing techniques to identify product categories and related conditions. The output is a list of the analyzed categories and conditions.

[0378] Step 2:

[0379] The server collects product and opinion information from databases and external sources based on the analysis results. It uses a list of categories and conditions as input. It retrieves information via a database communication protocol and performs data calculations to format the necessary data. The output is a set of collected product and opinion information.

[0380] Step 3:

[0381] The server inputs biometric information from the user into an emotion recognition engine to infer the user's emotional state. The input consists of facial expression data and voice data acquired from the device's camera and microphone. An emotion analysis algorithm is applied to perform data calculations that evaluate the user's psychological state. As output, a label indicating the emotional state is generated.

[0382] Step 4:

[0383] The server integrates collected product information and sentiment information to identify and rank products according to the user's emotional state. It inputs prompt sentences into a generating AI model to provide optimal product recommendations. The input consists of a product information set and sentiment labels. A product ranking algorithm that considers sentiment information is applied to process the data and select recommendation candidates. The output is a list of recommended products.

[0384] Step 5:

[0385] The terminal dynamically changes the display format based on product information sent from the server, according to the user's emotional state, and presents it to the user. Inputs are a list of recommended products and emotional labels. The terminal adjusts the user interface and performs specific actions to provide information visually and aurally. The output is optimized product information presented to the user.

[0386] (Application Example 2)

[0387] 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."

[0388] While current online shopping systems offer product recommendations based on user requests, they lack sufficient personalization that takes into account the user's emotional state. As a result, users often experience stress during the purchase decision process and have difficulty finding suitable products. This issue particularly impacts the consistency of the user interface and the way information is presented, posing a barrier to providing an optimal shopping experience.

[0389] 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.

[0390] In this invention, the server includes means for receiving user purchase requests, means for analyzing requests and extracting categories and conditions, means for collecting product information based on the extracted categories and conditions, means for estimating emotions from user input data and making product recommendations based on emotional information, and means for dynamically changing the display tone and layout when presenting information according to the user's emotional state. This enables users to quickly find the optimal product while reducing stress according to their emotional state.

[0391] "Means of receiving requests from users related to online purchases" refers to interfaces and processes for receiving requests from users of online shopping platforms regarding product searches and purchases.

[0392] "Methods for analyzing requests and extracting categories and conditions" refers to the process of analyzing requests received from users using technologies such as natural language processing to identify the category of the requested product and specific conditions.

[0393] "Means for collecting product information based on extracted categories and conditions" refers to a function that retrieves relevant product and evaluation information from databases or external sources according to the obtained categories and conditions.

[0394] "A means of estimating emotions from user input data and making product recommendations based on emotional information" refers to a system that uses text, voice, and facial expression data provided by the user to infer the user's emotions, and then selects and suggests the most suitable product for the user based on the results.

[0395] "Means of dynamically changing the display tone and layout when presenting information according to the user's emotional state" refers to technology that adjusts the color scheme and arrangement of information displayed on the screen in real time, taking the user's emotions into consideration.

[0396] The system for realizing this invention includes a program that recommends products by taking into account the user's needs and emotional state in order to improve the user's online shopping experience.

[0397] The server receives requests from users related to online purchases, including text input and voice data. The server analyzes these requests using a natural language processing engine to extract product categories and conditions. This method forms the basis for efficiently collecting specific product information from multiple databases. Furthermore, the server runs an emotion recognition engine to estimate emotions from the user's input data. This process is based on nuances in text and voice, and in some cases, facial expression images captured by the device.

[0398] The server then adjusts the product recommendation process based on emotional information. For example, if the emotional engine determines that the user is relaxed, it will present detailed product specifications. On the other hand, if it detects that the user is stressed, it will provide concise information summarizing the key points of the product. This helps to reduce stress caused by information overload.

[0399] The tone and layout of the information presented are dynamically changed by the device in response to the user's emotions. This adjustment makes the user experience more personalized and, as a result, contributes to increasing the user's willingness to purchase.

[0400] For example, if a user requests a book to help them relax on their day off, the system will recommend relaxing books (such as essays or short stories) and provide a concise summary. This ensures that the tired user receives appropriate product information, leading to a quicker purchase decision.

[0401] By utilizing generative AI models, even more accurate product recommendations become possible. A concrete example of a prompt would be, "Please tell me how to use an emotion recognition engine to recommend products suitable for relaxation based on the user's request to 'want to relax.'"

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server receives requests from users regarding online shopping. The input includes the user's product preferences, entered via text or voice. This allows the system to obtain initial data for the next process.

[0405] Step 2:

[0406] The server uses a natural language processing engine to parse incoming requests. During this process, product categories and conditions are extracted. The input is the user's request text, and the output is the parsed product categories and conditions. This information forms the basis for product information collection.

[0407] Step 3:

[0408] The server searches and collects relevant product information and reviews from the database based on the analyzed categories and conditions. The input is the output of step 2, and the output provides detailed product data and review information collected. This ensures that product information that matches the user's needs is gathered.

[0409] Step 4:

[0410] The server uses an emotion recognition engine to estimate the user's current emotional state from the user's input data. The input is the user's text or voice data received in step 1, and the output is the estimated user's emotional information. This information is used to refine product recommendations.

[0411] Step 5:

[0412] The server makes product recommendations based on emotional information. If fatigue or stress is detected, the recommended product information is condensed for clarity. The input is the output from steps 3 and 4, and an optimized product recommendation list is created as the output.

[0413] Step 6:

[0414] The device dynamically changes the display tone and layout of information, taking into account the user's emotional state. The input is the output of step 5, and it is output as an easy-to-view and emotionally sensitive interface. This speeds up the purchasing decision process.

[0415] Step 7:

[0416] Users select and purchase products based on the product information displayed on their devices. The final output is the completion of the user's purchasing action. By utilizing a generative AI model, it is expected that user satisfaction will be further improved.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention provides an advanced AI-powered information analysis system to address the information overload users face when shopping online. This system receives user requests, efficiently collects and analyzes product and review information based on those requests, and recommends the most suitable products.

[0434] Specifically, the server analyzes requests received from users using natural language processing technology. This analysis extracts categories and desired conditions. Based on the extracted information, the server collects product information and reviews from multiple e-commerce sites via relevant databases and APIs.

[0435] Next, the server analyzes this information and applies an algorithm to summarize the collected evaluation data, extracting key points. This allows it to identify common positive or negative opinions from numerous reviews, such as those regarding "camera performance," and evaluate the product through scoring.

[0436] The server then filters the scored products to match the user's criteria and creates a ranking. This result is displayed to the user via the terminal. The user interface organizes the information and presents it in a visually easy-to-understand format so that the user can easily compare products.

[0437] The user can make a final purchase decision based on the information presented, and the device provides a link to the purchase page related to the selected product, allowing for a simple checkout process.

[0438] As a concrete example, suppose a user enters the request, "I want to find a smartphone with a good camera performance within a budget of 50,000 yen." In this case, the server analyzes this request and suggests several smartphones with high camera performance ratings within the 50,000 yen budget. For each smartphone, the server summarizes the camera performance review and presents it to the user.

[0439] Through the above processes, users can efficiently gather information and make quick purchasing decisions. This system greatly improves the online shopping experience by providing users with the information they need, neither too much nor too little.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The device receives requests from users regarding online shopping. Users use input forms or voice input to send requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen."

[0443] Step 2:

[0444] The server analyzes the user's request received from the terminal using natural language processing technology. This analysis extracts specific elements such as "Category: Smartphone," "Condition: Budget under 50,000 yen," and "Interest: Camera performance."

[0445] Step 3:

[0446] The server uses APIs from e-commerce sites, social media platforms, and video streaming sites to collect product information and related reviews from databases in order to obtain product information that matches categories and conditions based on the analysis.

[0447] Step 4:

[0448] The server applies algorithms to summarize and organize the collected information. In particular, it extracts opinions related to specific features (e.g., camera performance) from numerous ratings and reviews, and calculates a product score based on them.

[0449] Step 5:

[0450] The server organizes products into a ranking format, scoring them to best match the user's requirements. This ranking is adjusted based on the user's specified budget and preferences.

[0451] Step 6:

[0452] The terminal displays product rankings and summary information received from the server in a visually easy-to-understand format for the user. It provides an interface that allows users to compare the features and scores of each product.

[0453] Step 7:

[0454] The user makes a final purchase decision based on the information presented. For selected products, the device provides a link to the relevant purchase page, assisting the user in proceeding directly with the purchase process.

[0455] (Example 1)

[0456] 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."

[0457] When shopping online, users are often overwhelmed by the vast amount of product information and reviews, making it difficult to make the best choice. Furthermore, quickly finding the right product requires organizing and comparing information, which is time-consuming and laborious. Therefore, there is a growing need for systems that can efficiently search for products and provide product information tailored to user needs.

[0458] 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.

[0459] In this invention, the server includes means for acquiring purchase-related requests received from users via an information processing device, means for analyzing the requests using a generative AI model and extracting categories and conditions, and means for collecting product information and evaluations via information sources based on the extracted categories and conditions. This enables users to save time and effort while reliably acquiring necessary product information and making optimal purchasing decisions.

[0460] A "user" refers to an individual or group that uses the system to search for and purchase products online.

[0461] An "information processing device" refers to a device used by users to input requests and transmit them to a server, and includes computers, smartphones, tablets, and other similar devices.

[0462] A "generative AI model" refers to an artificial intelligence algorithm or program used for natural language processing, which analyzes input text information to extract categories and conditions.

[0463] A "category" refers to a set of criteria or groups used to classify products based on user requirements.

[0464] "Conditions" refer to the elements that serve as criteria for selecting a product based on user requirements, and include price, performance, and functionality.

[0465] "Information sources" refer to external databases, APIs, and online platforms that provide product information and reviews.

[0466] "Product information" refers to data such as product characteristics, specifications, and price, which users refer to when making purchasing decisions.

[0467] "Reviews" refer to product reviews and feedback related to user requirements, and include opinions on product quality and performance.

[0468] A "server" refers to a central computing system that analyzes requests received from users, collects necessary information, and processes it based on that information.

[0469] "User interface" refers to the screens and operating methods that allow users to visually confirm and operate the system's inputs and outputs.

[0470] This invention is an information processing system designed to solve the problem of information overload in online shopping, and to support users in more efficiently selecting products and making purchasing decisions.

[0471] The system first receives the user's purchase request via a terminal. The user inputs their request into the terminal in natural language, providing content such as, "I want a smartphone with good camera performance for under 50,000 yen." This can be done using a general information processing device such as a PC, smartphone, or tablet.

[0472] The request is sent to the server and analyzed using a generative AI model. This AI model uses software with natural language processing technology, such as OpenAI's GPT-4. The server extracts categories (e.g., smartphones) and conditions (e.g., budget, performance) from the input request.

[0473] Based on the extracted information, the server accesses various e-commerce platforms and databases to collect product information and ratings. This operation utilizes data acquisition functions such as the Amazon Product Advertising API and Rakuten API. The collected data, including review information, is used to score product performance.

[0474] The analyzed information is scored by the server, and the product best suited to the request is selected. The optimized list is presented to the user via the user interface. It is ranked in a visually easy-to-understand format and arranged to allow the user to easily compare and consider the products.

[0475] The terminal provides easy access to selected products as a purchasing aid. It offers links to purchase pages for the products selected by the user, facilitating a quick and efficient purchase process.

[0476] As a concrete example, a user might input a request into their device such as, "I want to find a smartphone with a good camera and a budget of 50,000 yen or less." The server would then analyze the request and present the user with a list of products that meet the criteria. An example of a prompt might be, "Please suggest a smartphone with a good camera and a budget of 50,000 yen or less."

[0477] As a result, users can quickly obtain information based on their needs and make purchasing decisions efficiently. This system enables accurate information collection and processing, significantly improving the online shopping experience.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The user enters a purchase request using a terminal. This input is in natural language and may include specific requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen." The entered information is sent from the terminal to the server. In this step, the user's request is received as input data and prepared for analysis on the server.

[0481] Step 2:

[0482] The server analyzes requests received from users using a generation AI model. Specifically, it processes requests using natural language processing algorithms to extract categories and conditions. This data processing includes text analysis, resulting in output information such as categories (e.g., smartphones) and conditions (e.g., budget under 50,000 yen, camera performance).

[0483] Step 3:

[0484] The server accesses databases and APIs, which are the source of information, to collect product information and ratings based on the extracted categories and conditions. Data processing used includes API calls and data filtering, specifically the Amazon Product Advertising API and the Rakuten API. The input is the extracted categories and conditions, and the output is a list of detailed product information and reviews for the relevant products.

[0485] Step 4:

[0486] The server analyzes the collected product information and reviews. This analysis uses a text analysis algorithm to summarize the review content and process the data to score the product's performance. The input data is review information, and the output is the summarized reviews and the score for each product. Specifically, positive opinions about "camera performance" are aggregated and expressed as a numerical score.

[0487] Step 5:

[0488] The server ranks products that match the user's requirements based on the analysis results. Specifically, it sorts the scored products according to the user's criteria and lists them in the optimal order. Here, the ranking of products is output in order of score.

[0489] Step 6:

[0490] The terminal presents ranking information transmitted from the server to the user in a visually easy-to-understand format. This presentation includes information organization and visual design. The input is ranked product information, and the output is in a comparable display format. Specifically, it uses tables and graphs to highlight product features.

[0491] Step 7:

[0492] The user makes a final purchase decision based on the information displayed on the device. For the selected product, the device provides a link to the purchase page. This allows the user to access the specified online sales page with a click and proceed with the purchase. The input is the user's selection, and the output is a link to the purchase page.

[0493] (Application Example 1)

[0494] 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."

[0495] In online sales, many users often find it difficult to choose the best product due to information overload. Furthermore, the cumbersome purchasing process can diminish their desire to buy. Additionally, it's challenging to provide users with valuable purchasing support while efficiently utilizing summarized evaluation information. Against this backdrop, there is a need for a system that allows users to make efficient decisions and complete the purchasing process smoothly.

[0496] 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.

[0497] In this invention, the server includes means for receiving requests from users related to online sales, means for analyzing the received requests and extracting classifications and conditions, means for collecting product information and evaluation information from multiple information sources based on the extracted classifications and conditions, means for analyzing the collected information to identify and rank products that meet the user's requirements, means for displaying information to present the analysis results to the user in an easy-to-understand visual format, means for providing links to streamline buying and selling procedures in order to provide decision-making support functions via the terminal, and means for summarizing the collected evaluation information, assigning indicators in association with specific conditions during analysis, and further generating and utilizing prompt sentences using a generative AI model to support interaction with the user. This enables users to efficiently select the optimal product and smoothly complete the purchasing process.

[0498] A "user" is someone who uses the system to purchase goods online.

[0499] "Requests related to online sales" refers to information that describes the specific conditions and preferences of the products a user wishes to purchase.

[0500] "Classification" refers to the metrics and criteria used to categorize related products based on user requirements.

[0501] "Conditions" refer to specific specifications or requirements that users prioritize when choosing a product.

[0502] "Information sources" refer to multiple databases, APIs, etc., used to obtain product information and evaluation data.

[0503] "Product information" refers to detailed information about a specific product, including technical specifications, price, and availability.

[0504] "Evaluation information" refers to opinions and reviews from consumers and experts regarding the user experience and quality of a product.

[0505] "Analyzing collected information" is the process of processing data to extract useful meanings and patterns.

[0506] "Presenting information visually in an easy-to-understand manner" refers to displaying information using graphs, charts, and other visual aids so that users can easily understand and utilize it.

[0507] A "decision support function" is a feature that provides advice and recommendations to help users select the most suitable product.

[0508] "Providing links" means setting up a means of connecting to a webpage so that users can directly access the purchase page of a product they are interested in.

[0509] A "generative AI model" is an artificial intelligence technology used to generate text data in tasks such as natural language processing.

[0510] A "prompt message" is a sentence created by a generative AI model that serves as a response or suggestion to support the user's conversation and decision-making.

[0511] The system implementing this invention assists users in selecting the optimal product for online sales and efficiently proceeding with the purchasing process. The server first receives requests from users via a terminal and analyzes these requests using natural language processing technology. During the analysis process, classifications and conditions based on the user's requests are extracted.

[0512] Next, the server gathers product and evaluation information from multiple sources based on the collected classifications and conditions. These sources include various databases and APIs. The collected information is analyzed by the server to identify products that match the user's requirements and rank them. Evaluation information is summarized, and prompt messages are generated using a generative AI model. This ensures that information is provided in a format easily understood by the user.

[0513] The user interface displays product information in a visually easy-to-understand format. Furthermore, the terminal provides links to the purchase page for the product selected by the user, streamlining the purchasing process. For example, if a user enters "I'm looking for headphones with good sound quality for under 50,000 yen," the server suggests relevant products and generates a prompt summarizing review information. Based on this prompt, the user can easily make a decision. The conversation then progresses with prompts such as, "I'm looking for headphones with good sound quality for under 30,000 yen. Please tell me about related products and their ratings."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The server receives online sales-related requests from users via terminals. These requests include specific conditions and characteristics of the products the user wishes to purchase. The server receives natural language text as input, analyzes the request using natural language processing techniques, and extracts the classifications and conditions requested by the user. The output is the generated classifications and conditions.

[0517] Step 2:

[0518] The server collects product and evaluation information from multiple sources based on the classifications and conditions extracted in Step 1. These sources consist of product databases and APIs. This process retrieves relevant data from the sources and aggregates it on the server. The inputs are the extracted classifications and conditions, and the outputs are the collected product and evaluation information.

[0519] Step 3:

[0520] The server analyzes collected product and evaluation information to identify products that match the user's criteria. This analysis utilizes statistical methods and machine learning algorithms. Specifically, it comprehensively compares and analyzes product performance, price, and evaluation scores. The input is all collected information, and the output is a list of identified products.

[0521] Step 4:

[0522] The server summarizes the evaluation information of the identified product and uses a generative AI model to generate prompt sentences suitable for user interaction. This process summarizes a vast amount of review information, extracts important opinions and evaluations, and converts them into prompt sentences. The input is the evaluation information, and the output is the generated prompt sentences.

[0523] Step 5:

[0524] The terminal builds a user interface to visually present the analysis results received from the server to the user in an easy-to-understand manner. This process uses graphs, lists, highlighting, etc., to make the information easy for the user to understand. The input is the analyzed product information and prompt text, and the output is the display of the information on the user interface.

[0525] Step 6:

[0526] The terminal provides a purchase link to the product selected by the user, streamlining the purchasing process. In this step, a direct link to the purchase page of the selected product is generated and presented to the user. The input is the user's product selection information, and the output is the purchase link.

[0527] These steps enable users to efficiently select the optimal product online and easily complete the purchase process.

[0528] 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.

[0529] This invention aims to provide more personalized product recommendations and information presentation in an online shopping system that supports users' purchasing activities by integrating an emotion engine that recognizes users' emotions. This system provides product information while considering not only the user's requests but also their emotional state.

[0530] Specifically, the server receives purchase requests from users, analyzes these requests using natural language processing, and extracts categories and conditions. This forms the basis for collecting information about products that the user is interested in. Furthermore, the system uses an emotion recognition engine to infer the user's emotional state. This emotion recognition process is carried out by measuring text and voice data entered by the user, as well as biometric information such as facial expressions.

[0531] The server collects relevant product information based on the extracted categories and conditions. This includes product information, reviews, and ratings obtained through multiple databases and APIs. The sentiment information provided by the sentiment engine is used in the product recommendation process to influence the user's preferences, stress levels, and willingness to purchase. For example, when the user is relaxed, detailed specifications are presented, while when the user is highly stressed, concise and to-the-point recommendations are given.

[0532] Based on the collected and analyzed information, the server selects the most suitable products based on the user's requests and emotions, and organizes them in a ranking format. This product information is presented to the user visually and audibly through the terminal. The user interface dynamically changes the tone and layout of the display according to the user's emotional state to improve the user experience.

[0533] For example, if a user requests, "I want a perfume with a scent that will lift my spirits in my busy daily life," the server analyzes the request and prepares perfumes with high relaxation effects. If emotion recognition detects that the user is showing signs of fatigue, the device simply presents perfume options and provides a concise explanation of their effects, thereby reducing stress and encouraging purchase.

[0534] Thus, by taking into account the user's emotions, the present invention can not only recommend products but also provide an optimal purchasing experience tailored to the user's psychological state.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] The device receives requests from the user regarding online shopping. At this stage, the user sends a request using text input or voice input, for example, "I want you to find me a perfume that makes me feel good."

[0538] Step 2:

[0539] The server analyzes the user's request sent from the terminal using natural language processing technology to extract categories and conditions. This identifies specific data such as "Category: Perfume" and "Purpose: To improve mood."

[0540] Step 3:

[0541] The device uses built-in or external sensors to acquire the user's emotional state. This includes methods such as text analysis, voice tone analysis, and data acquisition from biosensors.

[0542] Step 4:

[0543] The server uses an emotion recognition engine to analyze emotional data acquired from the terminal. This analysis determines the user's current emotional state (e.g., relaxed, stressed, excited, etc.).

[0544] Step 5:

[0545] The server collects relevant product and review information from multiple e-commerce sites and product databases based on the extracted categories and conditions, as well as the emotional state.

[0546] Step 6:

[0547] The server analyzes the collected information and scores the product list. Taking into account the results of the emotion engine, it assigns a high score to the product that best fits the user's emotions.

[0548] Step 7:

[0549] The server filters products based on the scoring results and creates a list of recommended products. This list is ranked according to the user's emotional state.

[0550] Step 8:

[0551] The terminal presents the user with a product list provided by the server. Here, the user interface optimizes the visual presentation according to the user's emotional state, providing a simple layout for relaxed browsing and stress avoidance.

[0552] Step 9:

[0553] The user selects an item to purchase from the displayed products. The device provides a link to the purchase page associated with that item, allowing the user to complete the purchase process smoothly.

[0554] (Example 2)

[0555] 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."

[0556] In modern online shopping, it has become difficult for users to efficiently select the right product from a vast amount of product information during their purchasing process. Furthermore, because information is presented uniformly without considering the user's emotional state, there is a problem in effectively stimulating their purchasing intent. Traditional systems can only personalize information to a limited extent in response to user requests, lacking the ability to present optimal information based on psychological state.

[0557] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0558] In this invention, the server includes means for receiving requests from users related to online purchases, means for analyzing the received requests to extract the attributes and conditions of items, means for collecting product information and opinion information from multiple sources based on the extracted attributes and conditions, means for inferring the emotional state based on the user's biometric information and incorporating the emotional information into the analysis to identify products, and means for ranking the identified products according to the user's emotional state and presenting them in an appropriate display format. This enables personalized product recommendations and information presentation that take into account the user's emotional state, thereby improving the user's purchasing experience.

[0559] A "user" is someone who uses an online system to obtain product information and consider making a purchase.

[0560] "Online purchase-related requests" refer to purchasing interests expressed by users through the internet, specifically indicating their desires and needs.

[0561] "Item attributes" refer to information related to the characteristics and features of a product.

[0562] "Conditions" refer to specific criteria or constraints that should be considered when selecting a product.

[0563] "Information sources" refer to databases or external systems from which product information and opinions are obtained.

[0564] "Product information" refers to information such as detailed descriptions, prices, characteristics, and availability of a product.

[0565] "Opinion information" refers to opinions about a product based on reviews and ratings provided by users or third parties.

[0566] "Biometric information" refers to data related to the user's body, such as their heart rate and facial expressions.

[0567] "Emotional state" refers to the psychological or emotional condition a user exhibits at a particular point in time.

[0568] "Display format" refers to the form or method by which collected information is presented to the user visually or audibly.

[0569] This invention relates to a system designed to support user purchasing activities in an online shopping system. The server receives user requests via an interface, analyzes the text using natural language processing techniques, and extracts attributes and conditions related to the products. General-purpose natural language processing software is used for this purpose; specifically, natural language APIs and data analysis tools are employed.

[0570] Next, the server interacts with multiple information sources and external databases to collect product information and reviews. Common database communication protocols and APIs are implemented here. The collected data is input into an integrated emotion recognition engine, which infers the user's emotional state. This process uses technology to identify emotional states based on biometric information provided by the user.

[0571] For example, if a user requests to "relax," the server analyzes this request and collects relevant perfume product information. Then, an emotion recognition engine detects the user's fatigue level from their facial expressions and entered text. Based on this information, it becomes possible to concisely present perfume options.

[0572] The terminal presents the product information received from the server during this process to the user visually or audibly. The user interface dynamically changes according to the user's emotional state to provide a better experience. For example, by inputting a prompt message such as "List recommended products when the user is feeling stressed and briefly explain the reasons" into the generating AI model, optimal product recommendations can be obtained. In this way, the present invention realizes an interactive shopping experience that takes the user's emotions into consideration.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The server receives purchase requests from users via terminals. The input consists of requests provided by the user in text or voice. This input is analyzed using natural language processing techniques to identify product categories and related conditions. The output is a list of the analyzed categories and conditions.

[0576] Step 2:

[0577] The server collects product and opinion information from databases and external sources based on the analysis results. It uses a list of categories and conditions as input. It retrieves information via a database communication protocol and performs data calculations to format the necessary data. The output is a set of collected product and opinion information.

[0578] Step 3:

[0579] The server inputs biometric information from the user into an emotion recognition engine to infer the user's emotional state. The input consists of facial expression data and voice data acquired from the device's camera and microphone. An emotion analysis algorithm is applied to perform data calculations that evaluate the user's psychological state. As output, a label indicating the emotional state is generated.

[0580] Step 4:

[0581] The server integrates collected product information and sentiment information to identify and rank products according to the user's emotional state. It inputs prompt sentences into a generating AI model to provide optimal product recommendations. The input consists of a product information set and sentiment labels. A product ranking algorithm that considers sentiment information is applied to process the data and select recommendation candidates. The output is a list of recommended products.

[0582] Step 5:

[0583] The terminal dynamically changes the display format based on product information sent from the server, according to the user's emotional state, and presents it to the user. Inputs are a list of recommended products and emotional labels. The terminal adjusts the user interface and performs specific actions to provide information visually and aurally. The output is optimized product information presented to the user.

[0584] (Application Example 2)

[0585] 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."

[0586] While current online shopping systems offer product recommendations based on user requests, they lack sufficient personalization that takes into account the user's emotional state. As a result, users often experience stress during the purchase decision process and have difficulty finding suitable products. This issue particularly impacts the consistency of the user interface and the way information is presented, posing a barrier to providing an optimal shopping experience.

[0587] 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.

[0588] In this invention, the server includes means for receiving user purchase requests, means for analyzing requests and extracting categories and conditions, means for collecting product information based on the extracted categories and conditions, means for estimating emotions from user input data and making product recommendations based on emotional information, and means for dynamically changing the display tone and layout when presenting information according to the user's emotional state. This enables users to quickly find the optimal product while reducing stress according to their emotional state.

[0589] "Means of receiving requests from users related to online purchases" refers to interfaces and processes for receiving requests from users of online shopping platforms regarding product searches and purchases.

[0590] "Methods for analyzing requests and extracting categories and conditions" refers to the process of analyzing requests received from users using technologies such as natural language processing to identify the category of the requested product and specific conditions.

[0591] "Means for collecting product information based on extracted categories and conditions" refers to a function that retrieves relevant product and evaluation information from databases or external sources according to the obtained categories and conditions.

[0592] "A means of estimating emotions from user input data and making product recommendations based on emotional information" refers to a system that uses text, voice, and facial expression data provided by the user to infer the user's emotions, and then selects and suggests the most suitable product for the user based on the results.

[0593] "Means of dynamically changing the display tone and layout when presenting information according to the user's emotional state" refers to technology that adjusts the color scheme and arrangement of information displayed on the screen in real time, taking the user's emotions into consideration.

[0594] The system for realizing this invention includes a program that recommends products by taking into account the user's needs and emotional state in order to improve the user's online shopping experience.

[0595] The server receives requests from users related to online purchases, including text input and voice data. The server analyzes these requests using a natural language processing engine to extract product categories and conditions. This method forms the basis for efficiently collecting specific product information from multiple databases. Furthermore, the server runs an emotion recognition engine to estimate emotions from the user's input data. This process is based on nuances in text and voice, and in some cases, facial expression images captured by the device.

[0596] The server then adjusts the product recommendation process based on emotional information. For example, if the emotional engine determines that the user is relaxed, it will present detailed product specifications. On the other hand, if it detects that the user is stressed, it will provide concise information summarizing the key points of the product. This helps to reduce stress caused by information overload.

[0597] The tone and layout of the information presented are dynamically changed by the device in response to the user's emotions. This adjustment makes the user experience more personalized and, as a result, contributes to increasing the user's willingness to purchase.

[0598] For example, if a user requests a book to help them relax on their day off, the system will recommend relaxing books (such as essays or short stories) and provide a concise summary. This ensures that the tired user receives appropriate product information, leading to a quicker purchase decision.

[0599] By utilizing generative AI models, even more accurate product recommendations become possible. A concrete example of a prompt would be, "Please tell me how to use an emotion recognition engine to recommend products suitable for relaxation based on the user's request to 'want to relax.'"

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The server receives requests from users regarding online shopping. The input includes the user's product preferences, entered via text or voice. This allows the system to obtain initial data for the next process.

[0603] Step 2:

[0604] The server uses a natural language processing engine to parse incoming requests. During this process, product categories and conditions are extracted. The input is the user's request text, and the output is the parsed product categories and conditions. This information forms the basis for product information collection.

[0605] Step 3:

[0606] The server searches and collects relevant product information and reviews from the database based on the analyzed categories and conditions. The input is the output of step 2, and the output provides detailed product data and review information collected. This ensures that product information that matches the user's needs is gathered.

[0607] Step 4:

[0608] The server uses an emotion recognition engine to estimate the user's current emotional state from the user's input data. The input is the user's text or voice data received in step 1, and the output is the estimated user's emotional information. This information is used to refine product recommendations.

[0609] Step 5:

[0610] The server makes product recommendations based on emotional information. If fatigue or stress is detected, the recommended product information is condensed for clarity. The input is the output from steps 3 and 4, and an optimized product recommendation list is created as the output.

[0611] Step 6:

[0612] The device dynamically changes the display tone and layout of information, taking into account the user's emotional state. The input is the output of step 5, and it is output as an easy-to-view and emotionally sensitive interface. This speeds up the purchasing decision process.

[0613] Step 7:

[0614] Users select and purchase products based on the product information displayed on their devices. The final output is the completion of the user's purchasing action. By utilizing a generative AI model, it is expected that user satisfaction will be further improved.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention provides an advanced AI-powered information analysis system to address the information overload users face when shopping online. This system receives user requests, efficiently collects and analyzes product and review information based on those requests, and recommends the most suitable products.

[0633] Specifically, the server analyzes requests received from users using natural language processing technology. This analysis extracts categories and desired conditions. Based on the extracted information, the server collects product information and reviews from multiple e-commerce sites via relevant databases and APIs.

[0634] Next, the server analyzes this information and applies an algorithm to summarize the collected evaluation data, extracting key points. This allows it to identify common positive or negative opinions from numerous reviews, such as those regarding "camera performance," and evaluate the product through scoring.

[0635] The server then filters the scored products to match the user's criteria and creates a ranking. This result is displayed to the user via the terminal. The user interface organizes the information and presents it in a visually easy-to-understand format so that the user can easily compare products.

[0636] The user can make a final purchase decision based on the information presented, and the device provides a link to the purchase page related to the selected product, allowing for a simple checkout process.

[0637] As a concrete example, suppose a user enters the request, "I want to find a smartphone with a good camera performance within a budget of 50,000 yen." In this case, the server analyzes this request and suggests several smartphones with high camera performance ratings within the 50,000 yen budget. For each smartphone, the server summarizes the camera performance review and presents it to the user.

[0638] Through the above processes, users can efficiently gather information and make quick purchasing decisions. This system greatly improves the online shopping experience by providing users with the information they need, neither too much nor too little.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The device receives requests from users regarding online shopping. Users use input forms or voice input to send requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen."

[0642] Step 2:

[0643] The server analyzes the user's request received from the terminal using natural language processing technology. This analysis extracts specific elements such as "Category: Smartphone," "Condition: Budget under 50,000 yen," and "Interest: Camera performance."

[0644] Step 3:

[0645] The server uses APIs from e-commerce sites, social media platforms, and video streaming sites to collect product information and related reviews from databases in order to obtain product information that matches categories and conditions based on the analysis.

[0646] Step 4:

[0647] The server applies algorithms to summarize and organize the collected information. In particular, it extracts opinions related to specific features (e.g., camera performance) from numerous ratings and reviews, and calculates a product score based on them.

[0648] Step 5:

[0649] The server organizes products into a ranking format, scoring them to best match the user's requirements. This ranking is adjusted based on the user's specified budget and preferences.

[0650] Step 6:

[0651] The terminal displays product rankings and summary information received from the server in a visually easy-to-understand format for the user. It provides an interface that allows users to compare the features and scores of each product.

[0652] Step 7:

[0653] The user makes a final purchase decision based on the information presented. For selected products, the device provides a link to the relevant purchase page, assisting the user in proceeding directly with the purchase process.

[0654] (Example 1)

[0655] 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".

[0656] When shopping online, users are often overwhelmed by the vast amount of product information and reviews, making it difficult to make the best choice. Furthermore, quickly finding the right product requires organizing and comparing information, which is time-consuming and laborious. Therefore, there is a growing need for systems that can efficiently search for products and provide product information tailored to user needs.

[0657] 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.

[0658] In this invention, the server includes means for acquiring purchase-related requests received from users via an information processing device, means for analyzing the requests using a generative AI model and extracting categories and conditions, and means for collecting product information and evaluations via information sources based on the extracted categories and conditions. This enables users to save time and effort while reliably acquiring necessary product information and making optimal purchasing decisions.

[0659] A "user" refers to an individual or group that uses the system to search for and purchase products online.

[0660] An "information processing device" refers to a device used by users to input requests and transmit them to a server, and includes computers, smartphones, tablets, and other similar devices.

[0661] A "generative AI model" refers to an artificial intelligence algorithm or program used for natural language processing, which analyzes input text information to extract categories and conditions.

[0662] A "category" refers to a set of criteria or groups used to classify products based on user requirements.

[0663] "Conditions" refer to the elements that serve as criteria for selecting a product based on user requirements, and include price, performance, and functionality.

[0664] "Information sources" refer to external databases, APIs, and online platforms that provide product information and reviews.

[0665] "Product information" refers to data such as product characteristics, specifications, and price, which users refer to when making purchasing decisions.

[0666] "Reviews" refer to product reviews and feedback related to user requirements, and include opinions on product quality and performance.

[0667] A "server" refers to a central computing system that analyzes requests received from users, collects necessary information, and processes it based on that information.

[0668] "User interface" refers to the screens and operating methods that allow users to visually confirm and operate the system's inputs and outputs.

[0669] This invention is an information processing system designed to solve the problem of information overload in online shopping, and to support users in more efficiently selecting products and making purchasing decisions.

[0670] The system first receives the user's purchase request via a terminal. The user inputs their request into the terminal in natural language, providing content such as, "I want a smartphone with good camera performance for under 50,000 yen." This can be done using a general information processing device such as a PC, smartphone, or tablet.

[0671] The request is sent to the server and analyzed using a generative AI model. This AI model uses software with natural language processing technology, such as OpenAI's GPT-4. The server extracts categories (e.g., smartphones) and conditions (e.g., budget, performance) from the input request.

[0672] Based on the extracted information, the server accesses various e-commerce platforms and databases to collect product information and ratings. This operation utilizes data acquisition functions such as the Amazon Product Advertising API and Rakuten API. The collected data, including review information, is used to score product performance.

[0673] The analyzed information is scored by the server, and the product best suited to the request is selected. The optimized list is presented to the user via the user interface. It is ranked in a visually easy-to-understand format and arranged to allow the user to easily compare and consider the products.

[0674] The terminal provides easy access to selected products as a purchasing aid. It offers links to purchase pages for the products selected by the user, facilitating a quick and efficient purchase process.

[0675] As a concrete example, a user might input a request into their device such as, "I want to find a smartphone with a good camera and a budget of 50,000 yen or less." The server would then analyze the request and present the user with a list of products that meet the criteria. An example of a prompt might be, "Please suggest a smartphone with a good camera and a budget of 50,000 yen or less."

[0676] As a result, users can quickly obtain information based on their needs and make purchasing decisions efficiently. This system enables accurate information collection and processing, significantly improving the online shopping experience.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user enters a purchase request using a terminal. This input is in natural language and may include specific requests such as, "I'd like to find a smartphone with a good camera and a budget of under 50,000 yen." The entered information is sent from the terminal to the server. In this step, the user's request is received as input data and prepared for analysis on the server.

[0680] Step 2:

[0681] The server analyzes requests received from users using a generation AI model. Specifically, it processes requests using natural language processing algorithms to extract categories and conditions. This data processing includes text analysis, resulting in output information such as categories (e.g., smartphones) and conditions (e.g., budget under 50,000 yen, camera performance).

[0682] Step 3:

[0683] The server accesses databases and APIs, which are the source of information, to collect product information and ratings based on the extracted categories and conditions. Data processing used includes API calls and data filtering, specifically the Amazon Product Advertising API and the Rakuten API. The input is the extracted categories and conditions, and the output is a list of detailed product information and reviews for the relevant products.

[0684] Step 4:

[0685] The server analyzes the collected product information and reviews. This analysis uses a text analysis algorithm to summarize the review content and process the data to score the product's performance. The input data is review information, and the output is the summarized reviews and the score for each product. Specifically, positive opinions about "camera performance" are aggregated and expressed as a numerical score.

[0686] Step 5:

[0687] The server ranks products that match the user's requirements based on the analysis results. Specifically, it sorts the scored products according to the user's criteria and lists them in the optimal order. Here, the ranking of products is output in order of score.

[0688] Step 6:

[0689] The terminal presents ranking information transmitted from the server to the user in a visually easy-to-understand format. This presentation includes information organization and visual design. The input is ranked product information, and the output is in a comparable display format. Specifically, it uses tables and graphs to highlight product features.

[0690] Step 7:

[0691] The user makes a final purchase decision based on the information displayed on the device. For the selected product, the device provides a link to the purchase page. This allows the user to access the specified online sales page with a click and proceed with the purchase. The input is the user's selection, and the output is a link to the purchase page.

[0692] (Application Example 1)

[0693] 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".

[0694] In online sales, many users often find it difficult to choose the best product due to information overload. Furthermore, the cumbersome purchasing process can diminish their desire to buy. Additionally, it's challenging to provide users with valuable purchasing support while efficiently utilizing summarized evaluation information. Against this backdrop, there is a need for a system that allows users to make efficient decisions and complete the purchasing process smoothly.

[0695] 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.

[0696] In this invention, the server includes means for receiving requests from users related to online sales, means for analyzing the received requests and extracting classifications and conditions, means for collecting product information and evaluation information from multiple information sources based on the extracted classifications and conditions, means for analyzing the collected information to identify and rank products that meet the user's requirements, means for displaying information to present the analysis results to the user in an easy-to-understand visual format, means for providing links to streamline buying and selling procedures in order to provide decision-making support functions via the terminal, and means for summarizing the collected evaluation information, assigning indicators in association with specific conditions during analysis, and further generating and utilizing prompt sentences using a generative AI model to support interaction with the user. This enables users to efficiently select the optimal product and smoothly complete the purchasing process.

[0697] A "user" is someone who uses the system to purchase goods online.

[0698] "Requests related to online sales" refers to information that describes the specific conditions and preferences of the products a user wishes to purchase.

[0699] "Classification" refers to the metrics and criteria used to categorize related products based on user requirements.

[0700] "Conditions" refer to specific specifications or requirements that users prioritize when choosing a product.

[0701] "Information sources" refer to multiple databases, APIs, etc., used to obtain product information and evaluation data.

[0702] "Product information" refers to detailed information about a specific product, including technical specifications, price, and availability.

[0703] "Evaluation information" refers to opinions and reviews from consumers and experts regarding the user experience and quality of a product.

[0704] "Analyzing collected information" is the process of processing data to extract useful meanings and patterns.

[0705] "Presenting information visually in an easy-to-understand manner" refers to displaying information using graphs, charts, and other visual aids so that users can easily understand and utilize it.

[0706] A "decision support function" is a feature that provides advice and recommendations to help users select the most suitable product.

[0707] "Providing links" means setting up a means of connecting to a webpage so that users can directly access the purchase page of a product they are interested in.

[0708] A "generative AI model" is an artificial intelligence technology used to generate text data in tasks such as natural language processing.

[0709] A "prompt message" is a sentence created by a generative AI model that serves as a response or suggestion to support the user's conversation and decision-making.

[0710] The system implementing this invention assists users in selecting the optimal product for online sales and efficiently proceeding with the purchasing process. The server first receives requests from users via a terminal and analyzes these requests using natural language processing technology. During the analysis process, classifications and conditions based on the user's requests are extracted.

[0711] Next, the server gathers product and evaluation information from multiple sources based on the collected classifications and conditions. These sources include various databases and APIs. The collected information is analyzed by the server to identify products that match the user's requirements and rank them. Evaluation information is summarized, and prompt messages are generated using a generative AI model. This ensures that information is provided in a format easily understood by the user.

[0712] The user interface displays product information in a visually easy-to-understand format. Furthermore, the terminal provides links to the purchase page for the product selected by the user, streamlining the purchasing process. For example, if a user enters "I'm looking for headphones with good sound quality for under 50,000 yen," the server suggests relevant products and generates a prompt summarizing review information. Based on this prompt, the user can easily make a decision. The conversation then progresses with prompts such as, "I'm looking for headphones with good sound quality for under 30,000 yen. Please tell me about related products and their ratings."

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The server receives online sales-related requests from users via terminals. These requests include specific conditions and characteristics of the products the user wishes to purchase. The server receives natural language text as input, analyzes the request using natural language processing techniques, and extracts the classifications and conditions requested by the user. The output is the generated classifications and conditions.

[0716] Step 2:

[0717] The server collects product and evaluation information from multiple sources based on the classifications and conditions extracted in Step 1. These sources consist of product databases and APIs. This process retrieves relevant data from the sources and aggregates it on the server. The inputs are the extracted classifications and conditions, and the outputs are the collected product and evaluation information.

[0718] Step 3:

[0719] The server analyzes collected product and evaluation information to identify products that match the user's criteria. This analysis utilizes statistical methods and machine learning algorithms. Specifically, it comprehensively compares and analyzes product performance, price, and evaluation scores. The input is all collected information, and the output is a list of identified products.

[0720] Step 4:

[0721] The server summarizes the evaluation information of the identified product and uses a generative AI model to generate prompt sentences suitable for user interaction. This process summarizes a vast amount of review information, extracts important opinions and evaluations, and converts them into prompt sentences. The input is the evaluation information, and the output is the generated prompt sentences.

[0722] Step 5:

[0723] The terminal builds a user interface to visually present the analysis results received from the server to the user in an easy-to-understand manner. This process uses graphs, lists, highlighting, etc., to make the information easy for the user to understand. The input is the analyzed product information and prompt text, and the output is the display of the information on the user interface.

[0724] Step 6:

[0725] The terminal provides a purchase link to the product selected by the user, streamlining the purchasing process. In this step, a direct link to the purchase page of the selected product is generated and presented to the user. The input is the user's product selection information, and the output is the purchase link.

[0726] These steps enable users to efficiently select the optimal product online and easily complete the purchase process.

[0727] 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.

[0728] This invention aims to provide more personalized product recommendations and information presentation in an online shopping system that supports users' purchasing activities by integrating an emotion engine that recognizes users' emotions. This system provides product information while considering not only the user's requests but also their emotional state.

[0729] Specifically, the server receives purchase requests from users, analyzes these requests using natural language processing, and extracts categories and conditions. This forms the basis for collecting information about products that the user is interested in. Furthermore, the system uses an emotion recognition engine to infer the user's emotional state. This emotion recognition process is carried out by measuring text and voice data entered by the user, as well as biometric information such as facial expressions.

[0730] The server collects relevant product information based on the extracted categories and conditions. This includes product information, reviews, and ratings obtained through multiple databases and APIs. The sentiment information provided by the sentiment engine is used in the product recommendation process to influence the user's preferences, stress levels, and willingness to purchase. For example, when the user is relaxed, detailed specifications are presented, while when the user is highly stressed, concise and to-the-point recommendations are given.

[0731] Based on the collected and analyzed information, the server selects the most suitable products based on the user's requests and emotions, and organizes them in a ranking format. This product information is presented to the user visually and audibly through the terminal. The user interface dynamically changes the tone and layout of the display according to the user's emotional state to improve the user experience.

[0732] For example, if a user requests, "I want a perfume with a scent that will lift my spirits in my busy daily life," the server analyzes the request and prepares perfumes with high relaxation effects. If emotion recognition detects that the user is showing signs of fatigue, the device simply presents perfume options and provides a concise explanation of their effects, thereby reducing stress and encouraging purchase.

[0733] Thus, by taking into account the user's emotions, the present invention can not only recommend products but also provide an optimal purchasing experience tailored to the user's psychological state.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] The device receives requests from the user regarding online shopping. At this stage, the user sends a request using text input or voice input, for example, "I want you to find me a perfume that makes me feel good."

[0737] Step 2:

[0738] The server analyzes the user's request sent from the terminal using natural language processing technology to extract categories and conditions. This identifies specific data such as "Category: Perfume" and "Purpose: To improve mood."

[0739] Step 3:

[0740] The device uses built-in or external sensors to acquire the user's emotional state. This includes methods such as text analysis, voice tone analysis, and data acquisition from biosensors.

[0741] Step 4:

[0742] The server uses an emotion recognition engine to analyze emotional data acquired from the terminal. This analysis determines the user's current emotional state (e.g., relaxed, stressed, excited, etc.).

[0743] Step 5:

[0744] The server collects relevant product and review information from multiple e-commerce sites and product databases based on the extracted categories and conditions, as well as the emotional state.

[0745] Step 6:

[0746] The server analyzes the collected information and scores the product list. Taking into account the results of the emotion engine, it assigns a high score to the product that best fits the user's emotions.

[0747] Step 7:

[0748] The server filters products based on the scoring results and creates a list of recommended products. This list is ranked according to the user's emotional state.

[0749] Step 8:

[0750] The terminal presents the user with a product list provided by the server. Here, the user interface optimizes the visual presentation according to the user's emotional state, providing a simple layout for relaxed browsing and stress avoidance.

[0751] Step 9:

[0752] The user selects an item to purchase from the displayed products. The device provides a link to the purchase page associated with that item, allowing the user to complete the purchase process smoothly.

[0753] (Example 2)

[0754] 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".

[0755] In modern online shopping, it has become difficult for users to efficiently select the right product from a vast amount of product information during their purchasing process. Furthermore, because information is presented uniformly without considering the user's emotional state, there is a problem in effectively stimulating their purchasing intent. Traditional systems can only personalize information to a limited extent in response to user requests, lacking the ability to present optimal information based on psychological state.

[0756] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0757] In this invention, the server includes means for receiving requests from users related to online purchases, means for analyzing the received requests to extract the attributes and conditions of items, means for collecting product information and opinion information from multiple sources based on the extracted attributes and conditions, means for inferring the emotional state based on the user's biometric information and incorporating the emotional information into the analysis to identify products, and means for ranking the identified products according to the user's emotional state and presenting them in an appropriate display format. This enables personalized product recommendations and information presentation that take into account the user's emotional state, thereby improving the user's purchasing experience.

[0758] A "user" is someone who uses an online system to obtain product information and consider making a purchase.

[0759] "Online purchase-related requests" refer to purchasing interests expressed by users through the internet, specifically indicating their desires and needs.

[0760] "Item attributes" refer to information related to the characteristics and features of a product.

[0761] "Conditions" refer to specific criteria or constraints that should be considered when selecting a product.

[0762] "Information sources" refer to databases or external systems from which product information and opinions are obtained.

[0763] "Product information" refers to information such as detailed descriptions, prices, characteristics, and availability of a product.

[0764] "Opinion information" refers to opinions about a product based on reviews and ratings provided by users or third parties.

[0765] "Biometric information" refers to data related to the user's body, such as their heart rate and facial expressions.

[0766] "Emotional state" refers to the psychological or emotional condition a user exhibits at a particular point in time.

[0767] "Display format" refers to the form or method by which collected information is presented to the user visually or audibly.

[0768] This invention relates to a system designed to support user purchasing activities in an online shopping system. The server receives user requests via an interface, analyzes the text using natural language processing techniques, and extracts attributes and conditions related to the products. General-purpose natural language processing software is used for this purpose; specifically, natural language APIs and data analysis tools are employed.

[0769] Next, the server interacts with multiple information sources and external databases to collect product information and reviews. Common database communication protocols and APIs are implemented here. The collected data is input into an integrated emotion recognition engine, which infers the user's emotional state. This process uses technology to identify emotional states based on biometric information provided by the user.

[0770] For example, if a user requests to "relax," the server analyzes this request and collects relevant perfume product information. Then, an emotion recognition engine detects the user's fatigue level from their facial expressions and entered text. Based on this information, it becomes possible to concisely present perfume options.

[0771] The terminal presents the product information received from the server during this process to the user visually or audibly. The user interface dynamically changes according to the user's emotional state to provide a better experience. For example, by inputting a prompt message such as "List recommended products when the user is feeling stressed and briefly explain the reasons" into the generating AI model, optimal product recommendations can be obtained. In this way, the present invention realizes an interactive shopping experience that takes the user's emotions into consideration.

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The server receives purchase requests from users via terminals. The input consists of requests provided by the user in text or voice. This input is analyzed using natural language processing techniques to identify product categories and related conditions. The output is a list of the analyzed categories and conditions.

[0775] Step 2:

[0776] The server collects product and opinion information from databases and external sources based on the analysis results. It uses a list of categories and conditions as input. It retrieves information via a database communication protocol and performs data calculations to format the necessary data. The output is a set of collected product and opinion information.

[0777] Step 3:

[0778] The server inputs biometric information from the user into an emotion recognition engine to infer the user's emotional state. The input consists of facial expression data and voice data acquired from the device's camera and microphone. An emotion analysis algorithm is applied to perform data calculations that evaluate the user's psychological state. As output, a label indicating the emotional state is generated.

[0779] Step 4:

[0780] The server integrates collected product information and sentiment information to identify and rank products according to the user's emotional state. It inputs prompt sentences into a generating AI model to provide optimal product recommendations. The input consists of a product information set and sentiment labels. A product ranking algorithm that considers sentiment information is applied to process the data and select recommendation candidates. The output is a list of recommended products.

[0781] Step 5:

[0782] The terminal dynamically changes the display format based on product information sent from the server, according to the user's emotional state, and presents it to the user. Inputs are a list of recommended products and emotional labels. The terminal adjusts the user interface and performs specific actions to provide information visually and aurally. The output is optimized product information presented to the user.

[0783] (Application Example 2)

[0784] 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".

[0785] While current online shopping systems offer product recommendations based on user requests, they lack sufficient personalization that takes into account the user's emotional state. As a result, users often experience stress during the purchase decision process and have difficulty finding suitable products. This issue particularly impacts the consistency of the user interface and the way information is presented, posing a barrier to providing an optimal shopping experience.

[0786] 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.

[0787] In this invention, the server includes means for receiving user purchase requests, means for analyzing requests and extracting categories and conditions, means for collecting product information based on the extracted categories and conditions, means for estimating emotions from user input data and making product recommendations based on emotional information, and means for dynamically changing the display tone and layout when presenting information according to the user's emotional state. This enables users to quickly find the optimal product while reducing stress according to their emotional state.

[0788] "Means of receiving requests from users related to online purchases" refers to interfaces and processes for receiving requests from users of online shopping platforms regarding product searches and purchases.

[0789] "Methods for analyzing requests and extracting categories and conditions" refers to the process of analyzing requests received from users using technologies such as natural language processing to identify the category of the requested product and specific conditions.

[0790] "Means for collecting product information based on extracted categories and conditions" refers to a function that retrieves relevant product and evaluation information from databases or external sources according to the obtained categories and conditions.

[0791] "A means of estimating emotions from user input data and making product recommendations based on emotional information" refers to a system that uses text, voice, and facial expression data provided by the user to infer the user's emotions, and then selects and suggests the most suitable product for the user based on the results.

[0792] "Means of dynamically changing the display tone and layout when presenting information according to the user's emotional state" refers to technology that adjusts the color scheme and arrangement of information displayed on the screen in real time, taking the user's emotions into consideration.

[0793] The system for realizing this invention includes a program that recommends products by taking into account the user's needs and emotional state in order to improve the user's online shopping experience.

[0794] The server receives requests from users related to online purchases, including text input and voice data. The server analyzes these requests using a natural language processing engine to extract product categories and conditions. This method forms the basis for efficiently collecting specific product information from multiple databases. Furthermore, the server runs an emotion recognition engine to estimate emotions from the user's input data. This process is based on nuances in text and voice, and in some cases, facial expression images captured by the device.

[0795] The server then adjusts the product recommendation process based on emotional information. For example, if the emotional engine determines that the user is relaxed, it will present detailed product specifications. On the other hand, if it detects that the user is stressed, it will provide concise information summarizing the key points of the product. This helps to reduce stress caused by information overload.

[0796] The tone and layout of the information presented are dynamically changed by the device in response to the user's emotions. This adjustment makes the user experience more personalized and, as a result, contributes to increasing the user's willingness to purchase.

[0797] For example, if a user requests a book to help them relax on their day off, the system will recommend relaxing books (such as essays or short stories) and provide a concise summary. This ensures that the tired user receives appropriate product information, leading to a quicker purchase decision.

[0798] By utilizing generative AI models, even more accurate product recommendations become possible. A concrete example of a prompt would be, "Please tell me how to use an emotion recognition engine to recommend products suitable for relaxation based on the user's request to 'want to relax.'"

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The server receives requests from users regarding online shopping. The input includes the user's product preferences, entered via text or voice. This allows the system to obtain initial data for the next process.

[0802] Step 2:

[0803] The server uses a natural language processing engine to parse incoming requests. During this process, product categories and conditions are extracted. The input is the user's request text, and the output is the parsed product categories and conditions. This information forms the basis for product information collection.

[0804] Step 3:

[0805] The server searches and collects relevant product information and reviews from the database based on the analyzed categories and conditions. The input is the output of step 2, and the output provides detailed product data and review information collected. This ensures that product information that matches the user's needs is gathered.

[0806] Step 4:

[0807] The server uses an emotion recognition engine to estimate the user's current emotional state from the user's input data. The input is the user's text or voice data received in step 1, and the output is the estimated user's emotional information. This information is used to refine product recommendations.

[0808] Step 5:

[0809] The server makes product recommendations based on emotional information. If fatigue or stress is detected, the recommended product information is condensed for clarity. The input is the output from steps 3 and 4, and an optimized product recommendation list is created as the output.

[0810] Step 6:

[0811] The device dynamically changes the display tone and layout of information, taking into account the user's emotional state. The input is the output of step 5, and it is output as an easy-to-view and emotionally sensitive interface. This speeds up the purchasing decision process.

[0812] Step 7:

[0813] Users select and purchase products based on the product information displayed on their devices. The final output is the completion of the user's purchasing action. By utilizing a generative AI model, it is expected that user satisfaction will be further improved.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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 as being incorporated by reference.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means of receiving requests from users related to online purchases,

[0838] A means of analyzing the received request and extracting categories and conditions,

[0839] A means for collecting product information and evaluation information from multiple databases based on extracted categories and conditions,

[0840] A means of analyzing the collected information to identify and rank products that meet user requirements,

[0841] A system that includes means for presenting analysis results to the user.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising means for summarizing collected evaluation information and assigning scores to it in relation to specific conditions during analysis.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for providing a link to a purchase page via a user interface for assisting in the purchase of specified products.

[0846] "Example 1"

[0847] (Claim 1)

[0848] A means for obtaining purchase-related requests received from a user via an information processing device,

[0849] A method for analyzing requests using a generative AI model and extracting categories and conditions,

[0850] A means of collecting information and evaluations of products through information sources based on extracted categories and conditions,

[0851] A means of analyzing collected evaluation information to score and rank products that match user requirements,

[0852] A system that includes means for displaying information through a user interface based on analysis results.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for summarizing collected evaluation information using a text analysis algorithm and assigning scores based on the analyzed information in association with specific conditions.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for providing a link to a purchase page so that the purchase procedure can proceed easily via a user interface in order to support the purchase decision of identified products.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A means of receiving requests from users related to online sales,

[0860] A means of analyzing received requests and extracting classifications and conditions,

[0861] A means for collecting product information and evaluation information from multiple sources based on extracted classifications and conditions,

[0862] A means of analyzing the collected information to identify and rank products that meet user requirements,

[0863] An information display means that presents the analysis results to the user in an easy-to-understand visual way,

[0864] In order to provide decision-making support functions via terminals, a means of providing links to streamline buying and selling procedures,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for summarizing collected evaluation information, assigning indicators in association with specific conditions during analysis, and further generating and utilizing prompt sentences using a generative AI model to support interaction with the user.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising means for providing access to a purchase page and optimizing the user experience through an interactive user interface for assisting in the purchase of identified products.

[0870] "Example 2 of combining an emotion engine"

[0871] (Claim 1)

[0872] A means of receiving requests from users related to online purchases,

[0873] A means of analyzing the received request and extracting the attributes and conditions of the item,

[0874] A means for collecting product information and opinion information from multiple sources based on extracted attributes and conditions,

[0875] A method for inferring emotional states based on a user's biometric information, incorporating emotional information into analysis, and identifying products.

[0876] A system that includes means for ranking identified products according to the user's emotional state and presenting them in an appropriate display format.

[0877] (Claim 2)

[0878] The system according to claim 1, comprising means for summarizing collected opinion information and assigning scores to it in relation to specific conditions during analysis.

[0879] (Claim 3)

[0880] The system according to claim 1, comprising means for providing a connection to the purchase procedure via a user display device for assisting the purchase of specified products.

[0881] "Application example 2 when combining with an emotional engine"

[0882] (Claim 1)

[0883] A means of receiving requests from users related to online purchases,

[0884] A means of analyzing the received request and extracting categories and conditions,

[0885] A means for collecting product information and evaluation information from multiple databases based on extracted categories and conditions,

[0886] A means of analyzing the collected information to identify and rank products that meet user requirements,

[0887] A method for estimating emotions from user input data and making product recommendations based on emotional information,

[0888] A means of dynamically changing the display tone and layout when presenting information according to the user's emotional state,

[0889] A system that includes means for presenting analysis results to the user.

[0890] (Claim 2)

[0891] The system according to claim 1, comprising means for summarizing collected evaluation information, assigning scores in association with specific conditions during analysis, and further optimizing and presenting the information based on the user's emotional state.

[0892] (Claim 3)

[0893] The system according to claim 1, comprising a user interface for assisting in the purchase of specified products, providing a link to a purchase page, and a method for improving the interface according to the user's emotional state. [Explanation of symbols]

[0894] 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 receiving requests from users related to online sales, A means of analyzing received requests and extracting classifications and conditions, A means for collecting product information and evaluation information from multiple sources based on extracted classifications and conditions, A means of analyzing the collected information to identify and rank products that meet user requirements, An information display means that presents the analysis results to the user in an easy-to-understand visual way, In order to provide decision-making support functions via terminals, a means of providing links to streamline buying and selling procedures, A system that includes this.

2. The system according to claim 1, comprising means for summarizing collected evaluation information, assigning indicators in association with specific conditions during analysis, and further generating and utilizing prompt sentences using a generative AI model to support interaction with the user.

3. The system according to claim 1, comprising means for providing access to a purchase page and optimizing the user experience through an interactive user interface for assisting in the purchase of identified products.

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