system

Abstract

We provide the system. [Solution] The information processing device provides a means for receiving product information entered by the user, A means for obtaining product-related data from multiple information sources based on the aforementioned product information, A means for analyzing acquired data and performing price comparisons and summarizing reputation information, A means of presenting analysis results to users and supporting their purchasing decisions, A means of extracting and summarizing the content of text data using natural language processing technology, A means of displaying analysis results in an easy-to-understand visual format, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern consumers are required to make an optimal choice from a variety of products in the online market, but it takes a lot of effort and time to compare prices and confirm the reliability of stores in the process. Also, it is difficult to obtain accurate information from all information sources, and there is a risk of making a purchase decision based on incorrect information. Furthermore, there is a problem that products that do not fully meet the needs of consumers may be displayed, and an optimal shopping experience cannot be obtained.

Means for Solving the Problems

[0005] This system utilizes an information processing device to receive product information entered by consumers. Based on this information, it acquires comprehensive product-related data from multiple sources and uses artificial intelligence to perform data analysis, thereby providing price comparisons and summaries of reputation information. This system visually displays the analysis results to consumers, facilitating purchasing decisions. Furthermore, it solves the aforementioned problems by suggesting alternative or related products as needed, providing consumers with an optimal purchasing plan that meets their needs.

[0006] An "information processing device" is an electronic device that processes input data from users and performs various calculations, data collection, and analysis.

[0007] "User" refers to an individual or group that uses the system to input product information and make purchasing decisions.

[0008] "Product information" refers to data about the name, features, and specifications of a product that a user is considering purchasing.

[0009] "Information source" refers to websites or services that provide data about products online.

[0010] "Product-related data" refers to data directly related to a product, including information such as price, ratings, customer reviews, and stock availability.

[0011] "Analysis" is the process of extracting meaningful information from acquired data using methods such as calculations and statistics.

[0012] "Price comparison" is the act of researching and comparing the prices of the same or similar products from different vendors.

[0013] "Reputation information" refers to information about the reliability and quality of a product, as indicated by reviews and ratings from past buyers.

[0014] "Replacement product" refers to a product that, while different from the product initially considered, can fulfill the same purpose in response to the needs of the user.

[0015] "Purchase plan" refers to the proposed content that presents the optimal purchase method and combination from among multiple products.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in 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 one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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 system is designed to help consumers make optimal purchasing decisions by collecting data from multiple sources based on product information entered by the user and analyzing that data using artificial intelligence.

[0038] During the process, the user first uses their device to enter the name or keywords of the product they are considering purchasing. The device then sends this information to the server. Based on the entered information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data, including product price, ratings, availability, and vendor information.

[0039] Next, the server organizes the collected data and sends it to the AI ​​module. This module performs price comparisons and summarizes reviews and ratings. Here, natural language processing techniques are used to extract the content of the text data and summarize it in a format that is easy for consumers to understand.

[0040] Based on the analysis results, the server generates information to support the user's purchasing decision. Specifically, it generates data including price comparison tables for each product, store reputations, and cashback or point reward information.

[0041] Reflecting these results, the device displays the findings to the user in a visually clear and easy-to-understand manner. Furthermore, it may suggest alternative or related products to broaden the user's options. It can also suggest the optimal time and place to purchase.

[0042] For example, if a user is looking for a "high-performance laptop," this system can retrieve data from various online shops, compare prices, ratings, and availability in detail, and present the user with the most cost-effective option. Furthermore, by selecting reputable stores, it can help prevent problems after purchase.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses their device to enter the name or keywords of the product they are considering purchasing and selects the shopping sites they want to compare. The device then sends the input data to the server.

[0046] Step 2:

[0047] The server accesses multiple selected information sources based on the product information received from the terminal. It collects relevant data such as product prices, inventory, ratings, and reviews by using shopping site APIs or web scraping techniques.

[0048] Step 3:

[0049] The server organizes and formats the collected product-related data. It eliminates data duplication, converts it to a consistent format, and prepares it for transmission to the AI ​​module.

[0050] Step 4:

[0051] The server's AI module analyzes the organized data and generates product rankings based on price. It also uses natural language processing technology to summarize product reviews and user feedback, generating a summary of the information.

[0052] Step 5:

[0053] The server structures the analysis results and organizes them to include information that users can use as a reference when making purchasing decisions. This information includes price comparison tables, review summaries, and reliability information for each vendor.

[0054] Step 6:

[0055] The terminal receives analysis results sent from the server and presents them to the user in an easy-to-understand interface. Visual elements are added to make it easier for users to check the details of each product.

[0056] Step 7:

[0057] Users make purchase decisions based on the information presented on their devices. When selecting a retailer, they can refer to alternative and related products to make the best product choice.

[0058] Step 8:

[0059] If necessary, the server saves the user's selection results and uses the data for analysis to support future purchasing behavior.

[0060] (Example 1)

[0061] 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."

[0062] Consumers face the challenge of gathering information on products they are considering purchasing and making the best choice, as this involves the time-consuming process of individually researching and comparing product prices, reviews, and other factors. Furthermore, interpreting reviews and extracting reliable information is not easy. There is a growing need for systems that automate this process and support efficient and effective decision-making.

[0063] 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.

[0064] In this invention, the server includes means for receiving product information from a terminal operated by a user, means for collecting product data from a data source based on the product information, and means for organizing the collected data and analyzing it using an artificial intelligence module. This allows users to quickly compare product prices and ratings, and receive suggestions for related and alternative products, enabling them to make more appropriate purchasing decisions.

[0065] A "terminal" is an information processing device used by users to input product information, and includes devices such as personal computers and smartphones.

[0066] "Product information" refers to information that helps identify the product the user is considering purchasing, such as the product name and keywords entered by the user.

[0067] A "server" is a processing device that collects and analyzes data based on product information received from terminals and generates information to support users in making purchasing decisions.

[0068] A "data source" is an information source accessed to obtain product data, and includes APIs and websites of online shopping sites.

[0069] "Product data" refers to information collected from data sources, such as product price, ratings, inventory status, and vendor information.

[0070] An "artificial intelligence module" is a software component used to analyze collected product data and extract and summarize useful information.

[0071] "Analysis results" refer to information such as price comparison tables and store reputations obtained by analyzing product data using an artificial intelligence module.

[0072] A "visual interface" is a means of displaying analysis results in an easily understandable way for the user, and is the UI of software or applications that provide information graphically.

[0073] A "substitute product" is a product that is similar to, but different from, the product that the user is considering.

[0074] "Related products" are related products that are associated with the product a user is considering and that they might also be interested in purchasing.

[0075] A "purchase plan" is a guideline that includes the optimal choices and timing for consumers when purchasing products.

[0076] This invention is an information provision system for consumers to make optimal product selections. Specifically, the user inputs information about products they are considering purchasing using a terminal, and based on that information, a server collects and analyzes relevant product data from a data source. The following hardware and software are used in this process.

[0077] Users use a standard PC or smartphone as a terminal to enter product information. This information is sent to a server via the internet. The server uses shopping site APIs and web scraping techniques to collect data. Libraries such as Python's BeautifulSoup and Scrapy are sometimes used in this process.

[0078] The collected data is organized on a server and analyzed by an artificial intelligence module. During the analysis, natural language processing techniques are used to extract useful information from the text data and summarize prices and ratings. Generative AI models from Google's TENSORFLOW® and OpenAI® are utilized at this stage.

[0079] The analysis results are presented to the user through a visual interface, providing detailed purchasing support information including price comparisons, reputation information, and alternative and related products. This allows users to easily make optimal purchasing decisions.

[0080] For example, if a user is looking for a "high-performance laptop," the system gathers information from multiple online shop data sources and compares prices, ratings, and availability. Based on these results, it presents the user with the most cost-effective option and helps prevent post-purchase problems by selecting a highly-rated store.

[0081] For example, a prompt message could be something like, "Generate a report comparing the prices and reviews of high-performance laptops," which would allow the AI ​​to organize and provide the desired information.

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

[0083] Step 1:

[0084] The user uses their device to enter the name or keywords of the product they are considering purchasing. The entered product information is sent to the server via the internet as text data, such as "high-performance laptop."

[0085] Step 2:

[0086] The server collects relevant product data from data sources based on product information received from users. In this process, the server uses shopping site APIs and web scraping techniques to obtain information such as price, ratings, and stock availability. For example, it might use Python's BeautifulSoup to extract text from a webpage. The input is product information, and the output is raw data on related products.

[0087] Step 3:

[0088] The server organizes the collected product data and prepares it as structured data. It standardizes the data format and classifies the data according to the necessary attributes. At this stage, the raw data is converted to formats such as JSON or CSV, making it ready for analysis. The output is structured product data.

[0089] Step 4:

[0090] The server supplies the organized data to an artificial intelligence module, which then uses a generative AI model to analyze the data. Natural language processing techniques are used to analyze review and rating elements, compare prices, and extract information important to the user. The input is structured product data, and the output is a summarized analysis result.

[0091] Step 5:

[0092] The server generates purchasing support information based on the analysis results. This information includes price comparison tables for each product, seller reputation, and recommendations for alternative and related products. The generated data is prepared for presentation to the user. The output is the purchasing support information presented to the user.

[0093] Step 6:

[0094] The terminal receives purchase support information sent from the server and displays it in a visually easy-to-understand format through the user interface. Frontend technologies such as React and Vue.js are used to provide an environment where users can easily understand the information and select products. The output is visually displayed information.

[0095] This series of steps allows users to efficiently compare and evaluate products and is supported in making appropriate purchasing decisions.

[0096] (Application Example 1)

[0097] 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."

[0098] In recent years, consumers have become overwhelmed by the sheer volume of information available when shopping online, making it difficult to make optimal purchasing decisions. Furthermore, efficiently comparing prices and reviews of products is challenging, leading to anxiety about purchasing decisions. Additionally, the sheer volume of product reviews makes it difficult to read them all, potentially impacting post-purchase satisfaction. These challenges need to be addressed to enable consumers to make purchasing decisions with greater confidence.

[0099] 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.

[0100] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and summarizing price comparison and reputation information, means for extracting and summarizing the content of text data using natural language processing technology, and means for displaying the analysis results in a visually easy-to-understand manner. This enables consumers to efficiently acquire information, comprehensively compare prices and reputations, and make optimal purchasing decisions.

[0101] An "information processing device" is a device that receives input from a user and acquires and processes the necessary data based on that input.

[0102] "Product-related data" refers to all information necessary for purchasing decisions, including product price, reputation, stock availability, and retailer information.

[0103] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate language that humans use on a daily basis.

[0104] "Text data summarization" refers to the process of extracting key topics and points from a large amount of text information and displaying them in a concise format.

[0105] "Displaying analysis results in a visually easy-to-understand manner" means presenting the collected and analyzed data using a graphical user interface or similar method in a way that allows users to intuitively evaluate it.

[0106] "Alternative products" refer to other products in the same category or with similar functions that can satisfy the user's interests and needs.

[0107] An "optimal purchasing plan" is a plan designed to suggest the product, timing, and location of purchase that best matches the user's needs.

[0108] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or answers based on training data.

[0109] To implement this invention, the system is equipped with an information processing device that efficiently processes product information entered by the user. The terminal is responsible for sending the product name and keywords entered by the user to the server. The server receives this information and collects product-related data from multiple sources using API-based data acquisition and web scraping techniques. A high-performance cloud server is used as the hardware, and Python's BeautifulSoup and Requests libraries are utilized as the software.

[0110] The collected data is sent to an AI module on the server, where Scikit-learn and TensorFlow are used to perform price comparisons and reputation analysis of products. Simultaneously, natural language processing techniques are used to summarize product reviews and ratings in an easy-to-understand format. This part utilizes libraries such as NLTK (Natural Language Toolkit).

[0111] The analysis results are fed back to the user's device in a visually easy-to-understand format. This interface is implemented as an application using React Native, allowing users to visually check the information on their smartphones. Users can receive support to make the best product selection.

[0112] For example, when a user searches for a "high-performance laptop," the system compares prices, ratings, and availability from numerous shops and can use prompts such as "Show me the cheapest product sorted by highest review rating" to determine the most cost-effective option. In this way, users can confidently make the best purchase.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] User input of product information

[0116] The user uses a terminal to enter the name or keywords of the product they are considering purchasing. The entered information is sent from the terminal to the server. In this step, the input is the product name or keywords, and the output is the transmission of data to the server.

[0117] Step 2:

[0118] Acquisition of product-related data

[0119] Based on the received product information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data such as product price, reviews, availability, and seller. The input for this step is product information, and the output is the collected product-related data.

[0120] Step 3:

[0121] Data analysis and summarization

[0122] The server sends the collected data to an AI module, which uses Scikit-learn and TensorFlow to perform price comparisons and reputation analysis. Simultaneously, it uses natural language processing techniques to summarize product reviews and convert them into a user-friendly format. The input for this step is product-related data, and the output is the analyzed data and summarized review information.

[0123] Step 4:

[0124] Presenting the optimal option

[0125] Based on the analysis results, the server generates information to support purchase decisions and displays it on the user's terminal. The user views the information through an interface built with React Native and receives visual feedback when selecting products. The input for this step is the analysis results, and the output is the displayed visual feedback.

[0126] Step 5:

[0127] Suggestions for alternative and related products

[0128] Based on the analysis results, the server suggests alternative and related products to the user and provides optimal advice on the best time and place to purchase them. This information is used by the user to create the optimal purchasing plan. The input for this step is the user's interests and conditions, and the output is suggested product information and advice.

[0129] 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.

[0130] This system collects product information obtained through user input from various sources and presents shopping information optimized according to the user's emotional state. In particular, this invention incorporates an emotion engine and analyzes the user's emotions to enable a more personalized user experience.

[0131] The user uses a device to enter the product name and related information of the item they are considering purchasing. The device sends the entered information to the server. Based on the received information, the server accesses selected information sources to collect relevant data, including product price, ratings, and stock availability. This utilizes APIs from various shopping sites.

[0132] The collected data is organized on a server, and an AI module performs price comparisons and summarizes reputation information. Then, an emotion engine analyzes user input, system usage history, or direct user feedback to determine the emotional state. Natural language processing and emotion recognition algorithms are used for this analysis.

[0133] Based on the user's emotions recognized by the emotion engine, the server adjusts how products are presented. For example, a user judged to be stressed might be shown concise and easy-to-understand information, while a user judged to be calm might be provided with more detailed analytical information.

[0134] Furthermore, the server takes emotional states into consideration when suggesting alternative or related products. This process is important for expanding the customer's choices when making a purchase.

[0135] For example, if a user is looking for a "high-performance gaming laptop," the system can customize the information presented based on the user's emotional state. In addition to regular product information, if the emotion engine determines that the user is about to make a stressful decision, it will prioritize displaying products with discounts or special offers.

[0136] In this way, systems equipped with an emotion engine allow users to make purchasing decisions based on information appropriate to their emotional state, resulting in a more satisfying shopping experience.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The user uses the device to enter the name or keywords of the product they are considering purchasing and selects a source of information. At this time, the device also records the user's past purchase history and input history.

[0140] Step 2:

[0141] The terminal sends user input data to the server, and simultaneously sends the user's facial expressions, voice data, etc., to the emotion engine. The emotion engine analyzes this data to determine the user's emotional state.

[0142] Step 3:

[0143] The server accesses multiple shopping site APIs based on the entered product information to collect product-related data, including price, stock availability, ratings, and customer reviews. The collected data is temporarily stored in a database.

[0144] Step 4:

[0145] The server passes product-related data stored in the database to the AI ​​module, which performs price comparisons and summarizes reputation information. The AI ​​module calculates a score for each product and creates a ranking.

[0146] Step 5:

[0147] Based on the user's emotional state analyzed by the emotion engine, the server adjusts how information is presented. For example, if the system detects that the user is in a hurry, it will simply present only the most important information.

[0148] Step 6:

[0149] The server takes the user's emotional state into account and presents alternative or related products as options that match the user's needs. This allows the user to evaluate other options as needed.

[0150] Step 7:

[0151] The terminal receives analysis results and product suggestions sent from the server and displays the information using a customized interface based on the user's emotional state and interests.

[0152] Step 8:

[0153] Users make optimal purchasing choices based on the information displayed on their devices. The selection results and emotional feedback are recorded in a database to improve the user experience in the future.

[0154] (Example 2)

[0155] 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".

[0156] In modern e-commerce, users must deal with a vast amount of product information, which can make purchasing decisions difficult. Furthermore, providing information without considering the user's emotional state can sometimes decrease satisfaction. Therefore, it is necessary to provide optimal information tailored to the user's emotional state to support their purchasing decisions.

[0157] 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.

[0158] In this invention, the server includes means for a device that acquires information about products entered by the user, means for collecting product information from multiple information sources based on the information, means for analyzing the collected information to summarize price comparison and reputation information, and means for determining the user's emotional response and presenting optimized information. This makes it possible to provide product information tailored to the user's emotional state and to support purchasing decisions.

[0159] A "device" is a component of hardware or software designed to perform a specific function.

[0160] "Means" refers to the methods, processes, or equipment used to achieve a particular objective.

[0161] "Information source" refers to the underlying content or platform from which data and information are obtained.

[0162] "Information" refers to useful knowledge obtained as a result of data being processed or interpreted in some way.

[0163] "Collection" refers to the activity or process of systematically gathering specific data or information.

[0164] "Analysis" is the act of examining data and information in detail and making its constituent elements easier to understand.

[0165] "Price comparison" is the process of comparing price information for a product obtained from multiple sources to determine its relative value.

[0166] "Reputation information" refers to data on the social evaluation of a product obtained from user and third-party reviews and ratings.

[0167] "Emotional response" refers to the emotional state or changes of the user, and is an important element in the product purchase process.

[0168] "Optimization" refers to the efficient adjustment or improvement of a system or process in order to achieve the best possible results for a particular purpose.

[0169] This invention is a shopping system that collects relevant data from various sources based on product information entered by the user and provides optimal information in response to the user's emotional response. Embodiments of this invention are described below.

[0170] The user enters information about the product they are considering purchasing into a terminal. This terminal has the function of sending the entered information to a server. The terminal is designed to be a common device such as a regular PC or smartphone, and can utilize dedicated application software.

[0171] The server collects product-related data from multiple sources based on the user's input. Specifically, it uses APIs from shopping platforms to obtain data. For example, APIs from shopping malls and e-commerce sites are one such example, and data such as the price, ratings, and stock status of related products are collected.

[0172] The collected data is analyzed on the server by an AI module. This module eliminates data duplication and performs price comparisons and summarizes reputation information. Subsequently, an emotion engine installed on the server uses natural language processing technology and emotion recognition algorithms to analyze the user's emotional responses and determine the user's emotional state.

[0173] Based on this analysis, the server displays information on the screen that is appropriate for the user's emotional state. If the user is stressed, the information is kept as simple as possible; if they are relaxed, more detailed information is provided. The server also considers the user's emotional state and suggests related or alternative products.

[0174] For example, if a user is searching for a "high-performance gaming laptop" and the server determines that they are in a stressful situation, it can prioritize displaying information about discounted products and those with special offers.

[0175] An example of a prompt to input into a generative AI model would be, "Generate a program that provides shopping information tailored to the user's emotional state." This would allow users to have a more comfortable and satisfying shopping experience.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The user uses a terminal to input information about the product they are considering purchasing. For example, by entering a keyword such as "gaming laptop," information about a specific product category is retrieved. This input data is then formatted and sent to the server. At this point, the output is product information converted into a format that the server can receive.

[0179] Step 2:

[0180] The server receives product information sent from the terminal. Based on this information, the server initiates an operation to access APIs of multiple shopping platforms. Specifically, it sends API requests to retrieve data such as product price, ratings, and inventory status. The input is the product name, and the output is the raw data obtained from the APIs.

[0181] Step 3:

[0182] The server analyzes the acquired raw data. Using an AI module, it compares prices and summarizes reputation information for each product. In this process, it eliminates duplicate data and verifies data consistency. The input is raw data obtained from the API, and the output is organized price comparison information and reputation information.

[0183] Step 4:

[0184] The emotion engine installed on the server analyzes the user's emotional responses. The input consists of the user's input information and past usage history, and natural language processing techniques are used to determine the emotional state (e.g., stress, relaxation). The output is the determination result indicating the user's emotional state.

[0185] Step 5:

[0186] The server optimizes information presentation based on the analysis results of the emotion engine. Specifically, it simplifies information when the user is stressed and provides detailed information to relaxed users. The input is the result of the emotional state assessment, and the output is the optimized information presentation.

[0187] Step 6:

[0188] The server considers the user's emotional state when suggesting relevant or alternative products. For example, if the user is feeling stressed, it prioritizes displaying information about products with discounts or special offers. The input is the optimized information presentation, and the output is the emphasized information presentation and product suggestions.

[0189] (Application Example 2)

[0190] 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".

[0191] Conventional information processing devices present product information without considering the user's emotional state, thus failing to provide an optimal purchasing experience. In particular, when a user is stressed or, conversely, in a positive state, the information presented is often unsuitable for that emotional state. As a result, users may hesitate unnecessarily when making purchasing decisions.

[0192] 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.

[0193] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and performing price comparisons and summarizing reputation information, means for adjusting the information presentation method using an emotion recognition engine that analyzes the user's emotional state, and means for presenting the analysis results to the user and supporting purchasing decision-making. This makes it possible to present information optimally according to the user's emotional state.

[0194] An "information processing device" is a device that includes hardware and software for receiving product information from users and performing data analysis and optimization.

[0195] "Product information" refers to data entered by the user regarding the product they are considering purchasing, and includes the product name, price, and ratings.

[0196] "Information sources" refer to multiple online platforms and databases accessed to obtain product-related data.

[0197] An "emotion recognition engine" is a technology that analyzes user input and feedback and identifies the user's emotional state using natural language processing and emotion recognition algorithms.

[0198] "Analysis results" refer to the summary of price comparisons and reputation information based on the acquired product-related data.

[0199] "Purchase decision-making" is the process by which a user decides whether or not to buy a particular product.

[0200] "Adjusting the presentation method" refers to the operation of changing how information is displayed and structured based on the user's emotional state.

[0201] This system collects product information from numerous sources based on user input and presents information optimized according to the user's emotional state. Implementation requires a network environment for communication between client terminals and a server. The client terminal receives user input and transmits data to the server. Examples of suitable hardware include smartphones and PCs.

[0202] The server retrieves product data from multiple online stores and information platforms. This is typically done via RESTful APIs. AI modules are used to analyze the retrieved data; for example, price comparisons and the extraction of reputation information are performed using libraries such as Scikit-learn and TensorFlow.

[0203] Next, the emotion recognition engine analyzes the user's emotions. This is done based on the user's past usage history and direct feedback. In this process, natural language processing tools such as NLTK and SpaCy are often used.

[0204] If an emotional state is determined, the server will display information to the user that corresponds to that state. This information will be provided in a simple, easy-to-understand format for highly stressed users, and in a detailed, in-depth analysis format for calmer users. This part depends on the front-end design and will be implemented using a JavaScript® framework (e.g., React or Vue.js).

[0205] As a concrete example, let's say a user is looking for a new smartphone. In this case, if the emotion recognition engine detects a state of tension, it can prioritize displaying information about products with discounts or special offers.

[0206] Example of a prompt:

[0207] How can I prioritize displaying relevant products to a user who needs a cool-down, for whatever reason?

[0208] This system allows users to receive information optimized for their emotions, supporting them in making more satisfying purchasing decisions.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The terminal receives user input and sends product information to the server. The input includes the name and category of the product the user is considering purchasing. The information from the terminal is transferred to the server as an HTTP request.

[0212] Step 2:

[0213] Based on the product information received by the server, it retrieves product-related data from multiple sources. The input here is the product information sent in the previous step, and the output is a dataset including product price, ratings, and inventory status. The server accesses each shopping site via a RESTful API and receives responses in JSON format.

[0214] Step 3:

[0215] The server analyzes the acquired data to perform price comparisons and summarize reputation information. The input for this step is the product-related data acquired in the previous step, and the output is concisely summarized information designed for user understanding. A machine learning algorithm using Scikit-learn is executed for data analysis, and the overall rating of each product is output as a numerical value.

[0216] Step 4:

[0217] The user inputs emotional feedback via their device, which is then received by the server. The input consists of text and selected emotional states, and the server uses this data to prepare for emotion recognition.

[0218] Step 5:

[0219] The emotion recognition engine on the server analyzes the user's emotional state. The input here is user feedback and past usage history, and the output is numerical data representing the user's emotions. Natural language processing tools are used to analyze the meaning of the feedback and identify the emotional state.

[0220] Step 6:

[0221] The server generates optimal product information based on the user's emotional state and adjusts the presentation method. The input is the emotion recognition and analysis results, and the output is customized product information displayed on the user's device. Here, the information priority and display format are adjusted.

[0222] Step 7:

[0223] Information from the server is displayed on the user's device to support their purchasing decision. The output consists of product information and recommendations prioritized based on emotions. The user reviews the presented information and decides whether or not to purchase.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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".

[0240] This system is designed to help consumers make optimal purchasing decisions by collecting data from multiple sources based on product information entered by the user and analyzing that data using artificial intelligence.

[0241] During the process, the user first uses their device to enter the name or keywords of the product they are considering purchasing. The device then sends this information to the server. Based on the entered information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data, including product price, ratings, availability, and vendor information.

[0242] Next, the server organizes the collected data and sends it to the AI ​​module. This module performs price comparisons and summarizes reviews and ratings. Here, natural language processing techniques are used to extract the content of the text data and summarize it in a format that is easy for consumers to understand.

[0243] Based on the analysis results, the server generates information to support the user's purchasing decision. Specifically, it generates data including price comparison tables for each product, store reputations, and cashback or point reward information.

[0244] Reflecting these results, the device displays the findings to the user in a visually clear and easy-to-understand manner. Furthermore, it may suggest alternative or related products to broaden the user's options. It can also suggest the optimal time and place to purchase.

[0245] For example, if a user is looking for a "high-performance laptop," this system can retrieve data from various online shops, compare prices, ratings, and availability in detail, and present the user with the most cost-effective option. Furthermore, by selecting reputable stores, it can help prevent problems after purchase.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The user uses their device to enter the name or keywords of the product they are considering purchasing and selects the shopping sites they want to compare. The device then sends the input data to the server.

[0249] Step 2:

[0250] The server accesses multiple selected information sources based on the product information received from the terminal. It collects relevant data such as product prices, inventory, ratings, and reviews by using shopping site APIs or web scraping techniques.

[0251] Step 3:

[0252] The server organizes and formats the collected product-related data. It eliminates data duplication, converts it to a consistent format, and prepares it for transmission to the AI ​​module.

[0253] Step 4:

[0254] The server's AI module analyzes the organized data and generates product rankings based on price. It also uses natural language processing technology to summarize product reviews and user feedback, generating a summary of the information.

[0255] Step 5:

[0256] The server structures the analysis results and organizes them to include information that users can use as a reference when making purchasing decisions. This information includes price comparison tables, review summaries, and reliability information for each vendor.

[0257] Step 6:

[0258] The terminal receives analysis results sent from the server and presents them to the user in an easy-to-understand interface. Visual elements are added to make it easier for users to check the details of each product.

[0259] Step 7:

[0260] Users make purchase decisions based on the information presented on their devices. When selecting a retailer, they can refer to alternative and related products to make the best product choice.

[0261] Step 8:

[0262] If necessary, the server saves the user's selection results and uses the data for analysis to support future purchasing behavior.

[0263] (Example 1)

[0264] 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."

[0265] Consumers face the challenge of gathering information on products they are considering purchasing and making the best choice, as this involves the time-consuming process of individually researching and comparing product prices, reviews, and other factors. Furthermore, interpreting reviews and extracting reliable information is not easy. There is a growing need for systems that automate this process and support efficient and effective decision-making.

[0266] 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.

[0267] In this invention, the server includes means for receiving product information from a terminal operated by a user, means for collecting product data from a data source based on the product information, and means for organizing the collected data and analyzing it using an artificial intelligence module. This allows users to quickly compare product prices and ratings, and receive suggestions for related and alternative products, enabling them to make more appropriate purchasing decisions.

[0268] A "terminal" is an information processing device used by users to input product information, and includes devices such as personal computers and smartphones.

[0269] "Product information" refers to information that helps identify the product the user is considering purchasing, such as the product name and keywords entered by the user.

[0270] A "server" is a processing device that collects and analyzes data based on product information received from terminals and generates information to support users in making purchasing decisions.

[0271] A "data source" is an information source accessed to obtain product data, and includes APIs and websites of online shopping sites.

[0272] "Product data" refers to information collected from data sources, such as product price, ratings, inventory status, and vendor information.

[0273] An "artificial intelligence module" is a software component used to analyze collected product data and extract and summarize useful information.

[0274] "Analysis results" refer to information such as price comparison tables and store reputations obtained by analyzing product data using an artificial intelligence module.

[0275] A "visual interface" is a means of displaying analysis results in an easily understandable way for the user, and is the UI of software or applications that provide information graphically.

[0276] A "substitute product" is a product that is similar to, but different from, the product that the user is considering.

[0277] "Related products" are related products that are associated with the product a user is considering and that they might also be interested in purchasing.

[0278] A "purchase plan" is a guideline that includes the optimal choices and timing for consumers when purchasing products.

[0279] This invention is an information provision system for consumers to make optimal product selections. Specifically, the user inputs information about products they are considering purchasing using a terminal, and based on that information, a server collects and analyzes relevant product data from a data source. The following hardware and software are used in this process.

[0280] The user uses a general personal computer or smartphone as a terminal and inputs product information here. This information is sent to the server via the Internet. The server uses the API of the shopping site and web scraping technology for data collection. Here, libraries such as Python's BeautifulSoup and Scrapy may be utilized.

[0281] The collected data is sorted on the server and analyzed by an artificial intelligence module. In the process of analysis, natural language processing technology is used to extract useful information from the text data and summarize prices and evaluations. At this time, Google's TensorFlow and OpenAI's generative AI models are utilized.

[0282] The analysis results are presented to the user through a visual interface, and detailed purchase support information including price comparison of products, reputation information, alternative products and related products is provided. As a result, the user can easily make an optimal purchase decision.

[0283] As a specific example, when the user is looking for a "high-performance laptop", the system collects information from data sources of multiple online stores and compares prices, evaluations, and inventory status. Based on this result, the system presents the user with the most cost-effective option and can prevent post-purchase troubles by choosing a store with a high evaluation.

[0284] As an example of a prompt sentence, by instructing the AI in the form of "Please generate a report for comparing the prices and evaluations of high-performance laptops", it is possible to organize and provide the desired information.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user uses their device to enter the name or keywords of the product they are considering purchasing. The entered product information is sent to the server via the internet as text data, such as "high-performance laptop."

[0288] Step 2:

[0289] The server collects relevant product data from data sources based on product information received from users. In this process, the server uses shopping site APIs and web scraping techniques to obtain information such as price, ratings, and stock availability. For example, it might use Python's BeautifulSoup to extract text from a webpage. The input is product information, and the output is raw data on related products.

[0290] Step 3:

[0291] The server organizes the collected product data and prepares it as structured data. It standardizes the data format and classifies the data according to the necessary attributes. At this stage, the raw data is converted to formats such as JSON or CSV, making it ready for analysis. The output is structured product data.

[0292] Step 4:

[0293] The server supplies the organized data to an artificial intelligence module, which then uses a generative AI model to analyze the data. Natural language processing techniques are used to analyze review and rating elements, compare prices, and extract information important to the user. The input is structured product data, and the output is a summarized analysis result.

[0294] Step 5:

[0295] The server generates purchasing support information based on the analysis results. This information includes price comparison tables for each product, seller reputation, and recommendations for alternative and related products. The generated data is prepared for presentation to the user. The output is the purchasing support information presented to the user.

[0296] Step 6:

[0297] The terminal receives purchase support information sent from the server and displays it in a visually easy-to-understand format through the user interface. Frontend technologies such as React and Vue.js are used to provide an environment where users can easily understand the information and select products. The output is visually displayed information.

[0298] This series of steps allows users to efficiently compare and evaluate products and is supported in making appropriate purchasing decisions.

[0299] (Application Example 1)

[0300] 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."

[0301] In recent years, consumers have become overwhelmed by the sheer volume of information available when shopping online, making it difficult to make optimal purchasing decisions. Furthermore, efficiently comparing prices and reviews of products is challenging, leading to anxiety about purchasing decisions. Additionally, the sheer volume of product reviews makes it difficult to read them all, potentially impacting post-purchase satisfaction. These challenges need to be addressed to enable consumers to make purchasing decisions with greater confidence.

[0302] 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.

[0303] In this invention, the server includes means for receiving product information input by a user through an information processing device, means for obtaining product-related data from a plurality of information sources based on the product information, means for analyzing the obtained data to perform price comparison and summary of reputation information, means for extracting and summarizing the content of text data using natural language processing technology, and means for visually displaying the analysis results in an easy-to-understand manner. As a result, consumers can efficiently obtain information, comprehensively compare prices and reputations, and make an optimal purchasing decision.

[0304] An "information processing device" is a device that receives input from a user and obtains and processes necessary data based on it.

[0305] "Product-related data" refers to all information necessary for making a purchasing decision, including the price, reputation, inventory status, store information, etc. of a product.

[0306] "Natural language processing technology" is a technology for analyzing, understanding, and generating the language that humans use in daily life by a computer.

[0307] "Summarization of text data" refers to the process of extracting the main topics and points from a large amount of text information and presenting them in a short form.

[0308] "Visually displaying the analysis results in an easy-to-understand manner" means presenting the collected and analyzed data in a form that can be intuitively evaluated by the user using a graphical user interface or the like.

[0309] "Alternative product" refers to another product with the same category or similar functions that can satisfy the interests and needs of the user.

[0310] "Optimal purchasing plan" is a plan for proposing products, purchase timing, and purchase locations that best meet the conditions of the user.

[0311] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or answers based on training data.

[0312] To implement this invention, the system is equipped with an information processing device that efficiently processes product information entered by the user. The terminal is responsible for sending the product name and keywords entered by the user to the server. The server receives this information and collects product-related data from multiple sources using API-based data acquisition and web scraping techniques. A high-performance cloud server is used as the hardware, and Python's BeautifulSoup and Requests libraries are utilized as the software.

[0313] The collected data is sent to an AI module on the server, where Scikit-learn and TensorFlow are used to perform price comparisons and reputation analysis of products. Simultaneously, natural language processing techniques are used to summarize product reviews and ratings in an easy-to-understand format. This part utilizes libraries such as NLTK (Natural Language Toolkit).

[0314] The analysis results are fed back to the user's device in a visually easy-to-understand format. This interface is implemented as an application using React Native, allowing users to visually check the information on their smartphones. Users can receive support to make the best product selection.

[0315] For example, when a user searches for a "high-performance laptop," the system compares prices, ratings, and availability from numerous shops and can use prompts such as "Show me the cheapest product sorted by highest review rating" to determine the most cost-effective option. In this way, users can confidently make the best purchase.

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

[0317] Step 1:

[0318] User input of product information

[0319] The user uses a terminal to enter the name or keywords of the product they are considering purchasing. The entered information is sent from the terminal to the server. In this step, the input is the product name or keywords, and the output is the transmission of data to the server.

[0320] Step 2:

[0321] Acquisition of product-related data

[0322] Based on the received product information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data such as product price, reviews, availability, and seller. The input for this step is product information, and the output is the collected product-related data.

[0323] Step 3:

[0324] Data analysis and summarization

[0325] The server sends the collected data to an AI module, which uses Scikit-learn and TensorFlow to perform price comparisons and reputation analysis. Simultaneously, it uses natural language processing techniques to summarize product reviews and convert them into a user-friendly format. The input for this step is product-related data, and the output is the analyzed data and summarized review information.

[0326] Step 4:

[0327] Presenting the optimal option

[0328] Based on the analysis results, the server generates information to support purchase decisions and displays it on the user's terminal. The user views the information through an interface built with React Native and receives visual feedback when selecting products. The input for this step is the analysis results, and the output is the displayed visual feedback.

[0329] Step 5:

[0330] Suggestions for alternative and related products

[0331] Based on the analysis results, the server suggests alternative and related products to the user and provides optimal advice on the best time and place to purchase them. This information is used by the user to create the optimal purchasing plan. The input for this step is the user's interests and conditions, and the output is suggested product information and advice.

[0332] 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.

[0333] This system collects product information obtained through user input from various sources and presents shopping information optimized according to the user's emotional state. In particular, this invention incorporates an emotion engine and analyzes the user's emotions to enable a more personalized user experience.

[0334] The user uses a device to enter the product name and related information of the item they are considering purchasing. The device sends the entered information to the server. Based on the received information, the server accesses selected information sources to collect relevant data, including product price, ratings, and stock availability. This utilizes APIs from various shopping sites.

[0335] The collected data is organized on a server, and an AI module performs price comparisons and summarizes reputation information. Then, an emotion engine analyzes user input, system usage history, or direct user feedback to determine the emotional state. Natural language processing and emotion recognition algorithms are used for this analysis.

[0336] Based on the user's emotions recognized by the emotion engine, the server adjusts how products are presented. For example, a user judged to be stressed might be shown concise and easy-to-understand information, while a user judged to be calm might be provided with more detailed analytical information.

[0337] Furthermore, the server takes emotional states into consideration when suggesting alternative or related products. This process is important for expanding the customer's choices when making a purchase.

[0338] For example, if a user is looking for a "high-performance gaming laptop," the system can customize the information presented based on the user's emotional state. In addition to regular product information, if the emotion engine determines that the user is about to make a stressful decision, it will prioritize displaying products with discounts or special offers.

[0339] In this way, systems equipped with an emotion engine allow users to make purchasing decisions based on information appropriate to their emotional state, resulting in a more satisfying shopping experience.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The user uses the device to enter the name or keywords of the product they are considering purchasing and selects a source of information. At this time, the device also records the user's past purchase history and input history.

[0343] Step 2:

[0344] The terminal sends user input data to the server, and simultaneously sends the user's facial expressions, voice data, etc., to the emotion engine. The emotion engine analyzes this data to determine the user's emotional state.

[0345] Step 3:

[0346] The server accesses multiple shopping site APIs based on the entered product information to collect product-related data, including price, stock availability, ratings, and customer reviews. The collected data is temporarily stored in a database.

[0347] Step 4:

[0348] The server passes product-related data stored in the database to the AI ​​module, which performs price comparisons and summarizes reputation information. The AI ​​module calculates a score for each product and creates a ranking.

[0349] Step 5:

[0350] Based on the user's emotional state analyzed by the emotion engine, the server adjusts how information is presented. For example, if the system detects that the user is in a hurry, it will simply present only the most important information.

[0351] Step 6:

[0352] The server takes the user's emotional state into account and presents alternative or related products as options that match the user's needs. This allows the user to evaluate other options as needed.

[0353] Step 7:

[0354] The terminal receives analysis results and product suggestions sent from the server and displays the information using a customized interface based on the user's emotional state and interests.

[0355] Step 8:

[0356] Users make optimal purchasing choices based on the information displayed on their devices. The selection results and emotional feedback are recorded in a database to improve the user experience in the future.

[0357] (Example 2)

[0358] 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".

[0359] In modern e-commerce, users must deal with a vast amount of product information, which can make purchasing decisions difficult. Furthermore, providing information without considering the user's emotional state can sometimes decrease satisfaction. Therefore, it is necessary to provide optimal information tailored to the user's emotional state to support their purchasing decisions.

[0360] 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.

[0361] In this invention, the server includes means for a device that acquires information about products entered by the user, means for collecting product information from multiple information sources based on the information, means for analyzing the collected information to summarize price comparison and reputation information, and means for determining the user's emotional response and presenting optimized information. This makes it possible to provide product information tailored to the user's emotional state and to support purchasing decisions.

[0362] A "device" is a component of hardware or software designed to perform a specific function.

[0363] "Means" refers to the methods, processes, or equipment used to achieve a particular objective.

[0364] "Information source" refers to the underlying content or platform from which data and information are obtained.

[0365] "Information" refers to useful knowledge obtained as a result of data being processed or interpreted in some way.

[0366] "Collection" refers to the activity or process of systematically gathering specific data or information.

[0367] "Analysis" is the act of examining data and information in detail and making its constituent elements easier to understand.

[0368] "Price comparison" is the process of comparing price information for a product obtained from multiple sources to determine its relative value.

[0369] "Reputation information" refers to data on the social evaluation of a product obtained from user and third-party reviews and ratings.

[0370] "Emotional response" refers to the emotional state or changes of the user, and is an important element in the product purchase process.

[0371] "Optimization" refers to the efficient adjustment or improvement of a system or process in order to achieve the best possible results for a particular purpose.

[0372] This invention is a shopping system that collects relevant data from various sources based on product information entered by the user and provides optimal information in response to the user's emotional response. Embodiments of this invention are described below.

[0373] The user enters information about the product they are considering purchasing into a terminal. This terminal has the function of sending the entered information to a server. The terminal is designed to be a common device such as a regular PC or smartphone, and can utilize dedicated application software.

[0374] The server collects product-related data from multiple sources based on the user's input. Specifically, it uses APIs from shopping platforms to obtain data. For example, APIs from shopping malls and e-commerce sites are one such example, and data such as the price, ratings, and stock status of related products are collected.

[0375] The collected data is analyzed on the server by an AI module. This module eliminates data duplication and performs price comparisons and summarizes reputation information. Subsequently, an emotion engine installed on the server uses natural language processing technology and emotion recognition algorithms to analyze the user's emotional responses and determine the user's emotional state.

[0376] Based on this analysis, the server displays information on the screen that is appropriate for the user's emotional state. If the user is stressed, the information is kept as simple as possible; if they are relaxed, more detailed information is provided. The server also considers the user's emotional state and suggests related or alternative products.

[0377] For example, if a user is searching for a "high-performance gaming laptop" and the server determines that they are in a stressful situation, it can prioritize displaying information about discounted products and those with special offers.

[0378] An example of a prompt to input into a generative AI model would be, "Generate a program that provides shopping information tailored to the user's emotional state." This would allow users to have a more comfortable and satisfying shopping experience.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The user uses a terminal to input information about the product they are considering purchasing. For example, by entering a keyword such as "gaming laptop," information about a specific product category is retrieved. This input data is then formatted and sent to the server. At this point, the output is product information converted into a format that the server can receive.

[0382] Step 2:

[0383] The server receives product information sent from the terminal. Based on this information, the server initiates an operation to access APIs of multiple shopping platforms. Specifically, it sends API requests to retrieve data such as product price, ratings, and inventory status. The input is the product name, and the output is the raw data obtained from the APIs.

[0384] Step 3:

[0385] The server analyzes the acquired raw data. Using an AI module, it compares prices and summarizes reputation information for each product. In this process, it eliminates duplicate data and verifies data consistency. The input is raw data obtained from the API, and the output is organized price comparison information and reputation information.

[0386] Step 4:

[0387] The emotion engine installed on the server analyzes the user's emotional responses. The input consists of the user's input information and past usage history, and natural language processing techniques are used to determine the emotional state (e.g., stress, relaxation). The output is the determination result indicating the user's emotional state.

[0388] Step 5:

[0389] The server optimizes information presentation based on the analysis results of the emotion engine. Specifically, it simplifies information when the user is stressed and provides detailed information to relaxed users. The input is the result of the emotional state assessment, and the output is the optimized information presentation.

[0390] Step 6:

[0391] The server considers the user's emotional state when suggesting relevant or alternative products. For example, if the user is feeling stressed, it prioritizes displaying information about products with discounts or special offers. The input is the optimized information presentation, and the output is the emphasized information presentation and product suggestions.

[0392] (Application Example 2)

[0393] 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 as the "terminal".

[0394] Conventional information processing devices present product information without considering the user's emotional state, thus failing to provide an optimal purchasing experience. In particular, when a user is stressed or, conversely, in a positive state, the information presented is often unsuitable for that emotional state. As a result, users may hesitate unnecessarily when making purchasing decisions.

[0395] 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.

[0396] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and performing price comparisons and summarizing reputation information, means for adjusting the information presentation method using an emotion recognition engine that analyzes the user's emotional state, and means for presenting the analysis results to the user and supporting purchasing decision-making. This makes it possible to present information optimally according to the user's emotional state.

[0397] An "information processing device" is a device that includes hardware and software for receiving product information from users and performing data analysis and optimization.

[0398] "Product information" refers to data entered by the user regarding the product they are considering purchasing, and includes the product name, price, and ratings.

[0399] "Information sources" refer to multiple online platforms and databases accessed to obtain product-related data.

[0400] An "emotion recognition engine" is a technology that analyzes user input and feedback and identifies the user's emotional state using natural language processing and emotion recognition algorithms.

[0401] "Analysis results" refer to the summary of price comparisons and reputation information based on the acquired product-related data.

[0402] "Purchase decision-making" is the process by which a user decides whether or not to buy a particular product.

[0403] "Adjusting the presentation method" refers to the operation of changing how information is displayed and structured based on the user's emotional state.

[0404] This system collects product information from numerous sources based on user input and presents information optimized according to the user's emotional state. Implementation requires a network environment for communication between client terminals and a server. The client terminal receives user input and transmits data to the server. Examples of suitable hardware include smartphones and PCs.

[0405] The server retrieves product data from multiple online stores and information platforms. This is typically done via RESTful APIs. AI modules are used to analyze the retrieved data; for example, price comparisons and the extraction of reputation information are performed using libraries such as Scikit-learn and TensorFlow.

[0406] Next, the emotion recognition engine analyzes the user's emotions. This is done based on the user's past usage history and direct feedback. In this process, natural language processing tools such as NLTK and SpaCy are often used.

[0407] If an emotional state is determined, the server will display information to the user that corresponds to that state. This information will be presented in a simple, easy-to-understand format for highly stressed users, and in a detailed, in-depth analysis format for calmer users. This part depends on the frontend design and will be implemented using a JavaScript framework (e.g., React or Vue.js).

[0408] As a concrete example, let's say a user is looking for a new smartphone. In this case, if the emotion recognition engine detects a state of tension, it can prioritize displaying information about products with discounts or special offers.

[0409] Example of a prompt:

[0410] How can I prioritize displaying relevant products to a user who needs a cool-down, for whatever reason?

[0411] This system allows users to receive information optimized for their emotions, supporting them in making more satisfying purchasing decisions.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The terminal receives user input and sends product information to the server. The input includes the name and category of the product the user is considering purchasing. The information from the terminal is transferred to the server as an HTTP request.

[0415] Step 2:

[0416] Based on the product information received by the server, it retrieves product-related data from multiple sources. The input here is the product information sent in the previous step, and the output is a dataset including product price, ratings, and inventory status. The server accesses each shopping site via a RESTful API and receives responses in JSON format.

[0417] Step 3:

[0418] The server analyzes the acquired data to perform price comparisons and summarize reputation information. The input for this step is the product-related data acquired in the previous step, and the output is concisely summarized information designed for user understanding. A machine learning algorithm using Scikit-learn is executed for data analysis, and the overall rating of each product is output as a numerical value.

[0419] Step 4:

[0420] The user inputs emotional feedback via their device, which is then received by the server. The input consists of text and selected emotional states, and the server uses this data to prepare for emotion recognition.

[0421] Step 5:

[0422] The emotion recognition engine on the server analyzes the user's emotional state. The input here is user feedback and past usage history, and the output is numerical data representing the user's emotions. Natural language processing tools are used to analyze the meaning of the feedback and identify the emotional state.

[0423] Step 6:

[0424] The server generates optimal product information based on the user's emotional state and adjusts the presentation method. The input is the emotion recognition and analysis results, and the output is customized product information displayed on the user's device. Here, the information priority and display format are adjusted.

[0425] Step 7:

[0426] Information from the server is displayed on the user's device to support their purchasing decision. The output consists of product information and recommendations prioritized based on emotions. The user reviews the presented information and decides whether or not to purchase.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This system is designed to help consumers make optimal purchasing decisions by collecting data from multiple sources based on product information entered by the user and analyzing that data using artificial intelligence.

[0444] During the process, the user first uses their device to enter the name or keywords of the product they are considering purchasing. The device then sends this information to the server. Based on the entered information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data, including product price, ratings, availability, and vendor information.

[0445] Next, the server organizes the collected data and sends it to the AI ​​module. This module performs price comparisons and summarizes reviews and ratings. Here, natural language processing techniques are used to extract the content of the text data and summarize it in a format that is easy for consumers to understand.

[0446] Based on the analysis results, the server generates information to support the user's purchasing decision. Specifically, it generates data including price comparison tables for each product, store reputations, and cashback or point reward information.

[0447] Reflecting these results, the device displays the findings to the user in a visually clear and easy-to-understand manner. Furthermore, it may suggest alternative or related products to broaden the user's options. It can also suggest the optimal time and place to purchase.

[0448] For example, if a user is looking for a "high-performance laptop," this system can retrieve data from various online shops, compare prices, ratings, and availability in detail, and present the user with the most cost-effective option. Furthermore, by selecting reputable stores, it can help prevent problems after purchase.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The user uses their device to enter the name or keywords of the product they are considering purchasing and selects the shopping sites they want to compare. The device then sends the input data to the server.

[0452] Step 2:

[0453] The server accesses multiple selected information sources based on the product information received from the terminal. It collects relevant data such as product prices, inventory, ratings, and reviews by using shopping site APIs or web scraping techniques.

[0454] Step 3:

[0455] The server organizes and formats the collected product-related data. It eliminates data duplication, converts it to a consistent format, and prepares it for transmission to the AI ​​module.

[0456] Step 4:

[0457] The server's AI module analyzes the organized data and generates product rankings based on price. It also uses natural language processing technology to summarize product reviews and user feedback, generating a summary of the information.

[0458] Step 5:

[0459] The server structures the analysis results and organizes them to include information that users can use as a reference when making purchasing decisions. This information includes price comparison tables, review summaries, and reliability information for each vendor.

[0460] Step 6:

[0461] The terminal receives analysis results sent from the server and presents them to the user in an easy-to-understand interface. Visual elements are added to make it easier for users to check the details of each product.

[0462] Step 7:

[0463] Users make purchase decisions based on the information presented on their devices. When selecting a retailer, they can refer to alternative and related products to make the best product choice.

[0464] Step 8:

[0465] If necessary, the server saves the user's selection results and uses the data for analysis to support future purchasing behavior.

[0466] (Example 1)

[0467] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] Consumers face the challenge of gathering information on products they are considering purchasing and making the best choice, as this involves the time-consuming process of individually researching and comparing product prices, reviews, and other factors. Furthermore, interpreting reviews and extracting reliable information is not easy. There is a growing need for systems that automate this process and support efficient and effective decision-making.

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

[0470] In this invention, the server includes means for receiving product information from a terminal operated by a user, means for collecting product data from a data source based on the product information, and means for organizing the collected data and analyzing it using an artificial intelligence module. This allows users to quickly compare product prices and ratings, and receive suggestions for related and alternative products, enabling them to make more appropriate purchasing decisions.

[0471] A "terminal" is an information processing device used by users to input product information, and includes devices such as personal computers and smartphones.

[0472] "Product information" refers to information that helps identify the product the user is considering purchasing, such as the product name and keywords entered by the user.

[0473] A "server" is a processing device that collects and analyzes data based on product information received from terminals and generates information to support users in making purchasing decisions.

[0474] A "data source" is an information source accessed to obtain product data, and includes APIs and websites of online shopping sites.

[0475] "Product data" refers to information collected from data sources, such as product price, ratings, inventory status, and vendor information.

[0476] An "artificial intelligence module" is a software component used to analyze collected product data and extract and summarize useful information.

[0477] "Analysis results" refer to information such as price comparison tables and store reputations obtained by analyzing product data using an artificial intelligence module.

[0478] A "visual interface" is a means of displaying analysis results in an easily understandable way for the user, and is the UI of software or applications that provide information graphically.

[0479] A "substitute product" is a product that is similar to, but different from, the product that the user is considering.

[0480] "Related products" are related products that are associated with the product a user is considering and that they might also be interested in purchasing.

[0481] A "purchase plan" is a guideline that includes the optimal choices and timing for consumers when purchasing products.

[0482] This invention is an information provision system for consumers to make optimal product selections. Specifically, the user inputs information about products they are considering purchasing using a terminal, and based on that information, a server collects and analyzes relevant product data from a data source. The following hardware and software are used in this process.

[0483] Users use a standard PC or smartphone as a terminal to enter product information. This information is sent to a server via the internet. The server uses shopping site APIs and web scraping techniques to collect data. Libraries such as Python's BeautifulSoup and Scrapy are sometimes used in this process.

[0484] The collected data is organized on a server and analyzed by an artificial intelligence module. During the analysis, natural language processing techniques are used to extract useful information from the text data and summarize prices and evaluations. Google's TensorFlow and OpenAI's generative AI models are utilized for this process.

[0485] The analysis results are presented to the user through a visual interface, providing detailed purchasing support information including price comparisons, reputation information, and alternative and related products. This allows users to easily make optimal purchasing decisions.

[0486] For example, if a user is looking for a "high-performance laptop," the system gathers information from multiple online shop data sources and compares prices, ratings, and availability. Based on these results, it presents the user with the most cost-effective option and helps prevent post-purchase problems by selecting a highly-rated store.

[0487] For example, a prompt message could be something like, "Generate a report comparing the prices and reviews of high-performance laptops," which would allow the AI ​​to organize and provide the desired information.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The user uses their device to enter the name or keywords of the product they are considering purchasing. The entered product information is sent to the server via the internet as text data, such as "high-performance laptop."

[0491] Step 2:

[0492] The server collects relevant product data from data sources based on product information received from users. In this process, the server uses shopping site APIs and web scraping techniques to obtain information such as price, ratings, and stock availability. For example, it might use Python's BeautifulSoup to extract text from a webpage. The input is product information, and the output is raw data on related products.

[0493] Step 3:

[0494] The server organizes the collected product data and prepares it as structured data. It standardizes the data format and classifies the data according to the necessary attributes. At this stage, the raw data is converted to formats such as JSON or CSV, making it ready for analysis. The output is structured product data.

[0495] Step 4:

[0496] The server supplies the organized data to an artificial intelligence module, which then uses a generative AI model to analyze the data. Natural language processing techniques are used to analyze review and rating elements, compare prices, and extract information important to the user. The input is structured product data, and the output is a summarized analysis result.

[0497] Step 5:

[0498] The server generates purchasing support information based on the analysis results. This information includes price comparison tables for each product, seller reputation, and recommendations for alternative and related products. The generated data is prepared for presentation to the user. The output is the purchasing support information presented to the user.

[0499] Step 6:

[0500] The terminal receives purchase support information sent from the server and displays it in a visually easy-to-understand format through the user interface. Frontend technologies such as React and Vue.js are used to provide an environment where users can easily understand the information and select products. The output is visually displayed information.

[0501] This series of steps allows users to efficiently compare and evaluate products and is supported in making appropriate purchasing decisions.

[0502] (Application Example 1)

[0503] 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."

[0504] In recent years, consumers have become overwhelmed by the sheer volume of information available when shopping online, making it difficult to make optimal purchasing decisions. Furthermore, efficiently comparing prices and reviews of products is challenging, leading to anxiety about purchasing decisions. Additionally, the sheer volume of product reviews makes it difficult to read them all, potentially impacting post-purchase satisfaction. These challenges need to be addressed to enable consumers to make purchasing decisions with greater confidence.

[0505] 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.

[0506] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and summarizing price comparison and reputation information, means for extracting and summarizing the content of text data using natural language processing technology, and means for displaying the analysis results in a visually easy-to-understand manner. This enables consumers to efficiently acquire information, comprehensively compare prices and reputations, and make optimal purchasing decisions.

[0507] An "information processing device" is a device that receives input from a user and acquires and processes the necessary data based on that input.

[0508] "Product-related data" refers to all information necessary for purchasing decisions, including product price, reputation, stock availability, and retailer information.

[0509] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate language that humans use on a daily basis.

[0510] "Text data summarization" refers to the process of extracting key topics and points from a large amount of text information and displaying them in a concise format.

[0511] "Displaying analysis results in a visually easy-to-understand manner" means presenting the collected and analyzed data using a graphical user interface or similar method in a way that allows users to intuitively evaluate it.

[0512] "Alternative products" refer to other products in the same category or with similar functions that can satisfy the user's interests and needs.

[0513] An "optimal purchasing plan" is a plan designed to suggest the product, timing, and location of purchase that best matches the user's needs.

[0514] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or answers based on training data.

[0515] To implement this invention, the system is equipped with an information processing device that efficiently processes product information entered by the user. The terminal is responsible for sending the product name and keywords entered by the user to the server. The server receives this information and collects product-related data from multiple sources using API-based data acquisition and web scraping techniques. A high-performance cloud server is used as the hardware, and Python's BeautifulSoup and Requests libraries are utilized as the software.

[0516] The collected data is sent to an AI module on the server, where Scikit-learn and TensorFlow are used to perform price comparisons and reputation analysis of products. Simultaneously, natural language processing techniques are used to summarize product reviews and ratings in an easy-to-understand format. This part utilizes libraries such as NLTK (Natural Language Toolkit).

[0517] The analysis results are fed back to the user's device in a visually easy-to-understand format. This interface is implemented as an application using React Native, allowing users to visually check the information on their smartphones. Users can receive support to make the best product selection.

[0518] For example, when a user searches for a "high-performance laptop," the system compares prices, ratings, and availability from numerous shops and can use prompts such as "Show me the cheapest product sorted by highest review rating" to determine the most cost-effective option. In this way, users can confidently make the best purchase.

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] User input of product information

[0522] The user uses a terminal to enter the name or keywords of the product they are considering purchasing. The entered information is sent from the terminal to the server. In this step, the input is the product name or keywords, and the output is the transmission of data to the server.

[0523] Step 2:

[0524] Acquisition of product-related data

[0525] Based on the received product information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data such as product price, reviews, availability, and seller. The input for this step is product information, and the output is the collected product-related data.

[0526] Step 3:

[0527] Data analysis and summarization

[0528] The server sends the collected data to an AI module, which uses Scikit-learn and TensorFlow to perform price comparisons and reputation analysis. Simultaneously, it uses natural language processing techniques to summarize product reviews and convert them into a user-friendly format. The input for this step is product-related data, and the output is the analyzed data and summarized review information.

[0529] Step 4:

[0530] Presenting the optimal option

[0531] Based on the analysis results, the server generates information to support purchase decisions and displays it on the user's terminal. The user views the information through an interface built with React Native and receives visual feedback when selecting products. The input for this step is the analysis results, and the output is the displayed visual feedback.

[0532] Step 5:

[0533] Suggestions for alternative and related products

[0534] Based on the analysis results, the server suggests alternative and related products to the user and provides optimal advice on the best time and place to purchase them. This information is used by the user to create the optimal purchasing plan. The input for this step is the user's interests and conditions, and the output is suggested product information and advice.

[0535] 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.

[0536] This system collects product information obtained through user input from various sources and presents shopping information optimized according to the user's emotional state. In particular, this invention incorporates an emotion engine and analyzes the user's emotions to enable a more personalized user experience.

[0537] The user uses a device to enter the product name and related information of the item they are considering purchasing. The device sends the entered information to the server. Based on the received information, the server accesses selected information sources to collect relevant data, including product price, ratings, and stock availability. This utilizes APIs from various shopping sites.

[0538] The collected data is organized on a server, and an AI module performs price comparisons and summarizes reputation information. Then, an emotion engine analyzes user input, system usage history, or direct user feedback to determine the emotional state. Natural language processing and emotion recognition algorithms are used for this analysis.

[0539] Based on the user's emotions recognized by the emotion engine, the server adjusts how products are presented. For example, a user judged to be stressed might be shown concise and easy-to-understand information, while a user judged to be calm might be provided with more detailed analytical information.

[0540] Furthermore, the server takes emotional states into consideration when suggesting alternative or related products. This process is important for expanding the customer's choices when making a purchase.

[0541] For example, if a user is looking for a "high-performance gaming laptop," the system can customize the information presented based on the user's emotional state. In addition to regular product information, if the emotion engine determines that the user is about to make a stressful decision, it will prioritize displaying products with discounts or special offers.

[0542] In this way, systems equipped with an emotion engine allow users to make purchasing decisions based on information appropriate to their emotional state, resulting in a more satisfying shopping experience.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The user uses the device to enter the name or keywords of the product they are considering purchasing and selects a source of information. At this time, the device also records the user's past purchase history and input history.

[0546] Step 2:

[0547] The terminal sends user input data to the server, and simultaneously sends the user's facial expressions, voice data, etc., to the emotion engine. The emotion engine analyzes this data to determine the user's emotional state.

[0548] Step 3:

[0549] The server accesses multiple shopping site APIs based on the entered product information to collect product-related data, including price, stock availability, ratings, and customer reviews. The collected data is temporarily stored in a database.

[0550] Step 4:

[0551] The server passes product-related data stored in the database to the AI ​​module, which performs price comparisons and summarizes reputation information. The AI ​​module calculates a score for each product and creates a ranking.

[0552] Step 5:

[0553] Based on the user's emotional state analyzed by the emotion engine, the server adjusts how information is presented. For example, if the system detects that the user is in a hurry, it will simply present only the most important information.

[0554] Step 6:

[0555] The server takes the user's emotional state into account and presents alternative or related products as options that match the user's needs. This allows the user to evaluate other options as needed.

[0556] Step 7:

[0557] The terminal receives analysis results and product suggestions sent from the server and displays the information using a customized interface based on the user's emotional state and interests.

[0558] Step 8:

[0559] Users make optimal purchasing choices based on the information displayed on their devices. The selection results and emotional feedback are recorded in a database to improve the user experience in the future.

[0560] (Example 2)

[0561] 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."

[0562] In modern e-commerce, users must deal with a vast amount of product information, which can make purchasing decisions difficult. Furthermore, providing information without considering the user's emotional state can sometimes decrease satisfaction. Therefore, it is necessary to provide optimal information tailored to the user's emotional state to support their purchasing decisions.

[0563] 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.

[0564] In this invention, the server includes means for a device that acquires information about products entered by the user, means for collecting product information from multiple information sources based on the information, means for analyzing the collected information to summarize price comparison and reputation information, and means for determining the user's emotional response and presenting optimized information. This makes it possible to provide product information tailored to the user's emotional state and to support purchasing decisions.

[0565] A "device" is a component of hardware or software designed to perform a specific function.

[0566] "Means" refers to the methods, processes, or equipment used to achieve a particular objective.

[0567] "Information source" refers to the underlying content or platform from which data and information are obtained.

[0568] "Information" refers to useful knowledge obtained as a result of data being processed or interpreted in some way.

[0569] "Collection" refers to the activity or process of systematically gathering specific data or information.

[0570] "Analysis" is the act of examining data and information in detail and making its constituent elements easier to understand.

[0571] "Price comparison" is the process of comparing price information for a product obtained from multiple sources to determine its relative value.

[0572] "Reputation information" refers to data on the social evaluation of a product obtained from user and third-party reviews and ratings.

[0573] "Emotional response" refers to the emotional state or changes of the user, and is an important element in the product purchase process.

[0574] "Optimization" refers to the efficient adjustment or improvement of a system or process in order to achieve the best possible results for a particular purpose.

[0575] This invention is a shopping system that collects relevant data from various sources based on product information entered by the user and provides optimal information in response to the user's emotional response. Embodiments of this invention are described below.

[0576] The user enters information about the product they are considering purchasing into a terminal. This terminal has the function of sending the entered information to a server. The terminal is designed to be a common device such as a regular PC or smartphone, and can utilize dedicated application software.

[0577] The server collects product-related data from multiple sources based on the user's input. Specifically, it uses APIs from shopping platforms to obtain data. For example, APIs from shopping malls and e-commerce sites are one such example, and data such as the price, ratings, and stock status of related products are collected.

[0578] The collected data is analyzed on the server by an AI module. This module eliminates data duplication and performs price comparisons and summarizes reputation information. Subsequently, an emotion engine installed on the server uses natural language processing technology and emotion recognition algorithms to analyze the user's emotional responses and determine the user's emotional state.

[0579] Based on this analysis, the server displays information on the screen that is appropriate for the user's emotional state. If the user is stressed, the information is kept as simple as possible; if they are relaxed, more detailed information is provided. The server also considers the user's emotional state and suggests related or alternative products.

[0580] For example, if a user is searching for a "high-performance gaming laptop" and the server determines that they are in a stressful situation, it can prioritize displaying information about discounted products and those with special offers.

[0581] An example of a prompt to input into a generative AI model would be, "Generate a program that provides shopping information tailored to the user's emotional state." This would allow users to have a more comfortable and satisfying shopping experience.

[0582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0583] Step 1:

[0584] The user uses a terminal to input information about the product they are considering purchasing. For example, by entering a keyword such as "gaming laptop," information about a specific product category is retrieved. This input data is then formatted and sent to the server. At this point, the output is product information converted into a format that the server can receive.

[0585] Step 2:

[0586] The server receives product information sent from the terminal. Based on this information, the server initiates an operation to access APIs of multiple shopping platforms. Specifically, it sends API requests to retrieve data such as product price, ratings, and inventory status. The input is the product name, and the output is the raw data obtained from the APIs.

[0587] Step 3:

[0588] The server analyzes the acquired raw data. Using an AI module, it compares prices and summarizes reputation information for each product. In this process, it eliminates duplicate data and verifies data consistency. The input is raw data obtained from the API, and the output is organized price comparison information and reputation information.

[0589] Step 4:

[0590] The emotion engine installed on the server analyzes the user's emotional responses. The input consists of the user's input information and past usage history, and natural language processing techniques are used to determine the emotional state (e.g., stress, relaxation). The output is the determination result indicating the user's emotional state.

[0591] Step 5:

[0592] The server optimizes information presentation based on the analysis results of the emotion engine. Specifically, it simplifies information when the user is stressed and provides detailed information to relaxed users. The input is the result of the emotional state assessment, and the output is the optimized information presentation.

[0593] Step 6:

[0594] The server considers the user's emotional state when suggesting relevant or alternative products. For example, if the user is feeling stressed, it prioritizes displaying information about products with discounts or special offers. The input is the optimized information presentation, and the output is the emphasized information presentation and product suggestions.

[0595] (Application Example 2)

[0596] 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."

[0597] Conventional information processing devices present product information without considering the user's emotional state, thus failing to provide an optimal purchasing experience. In particular, when a user is stressed or, conversely, in a positive state, the information presented is often unsuitable for that emotional state. As a result, users may hesitate unnecessarily when making purchasing decisions.

[0598] 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.

[0599] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and performing price comparisons and summarizing reputation information, means for adjusting the information presentation method using an emotion recognition engine that analyzes the user's emotional state, and means for presenting the analysis results to the user and supporting purchasing decision-making. This makes it possible to present information optimally according to the user's emotional state.

[0600] An "information processing device" is a device that includes hardware and software for receiving product information from users and performing data analysis and optimization.

[0601] "Product information" refers to data entered by the user regarding the product they are considering purchasing, and includes the product name, price, and ratings.

[0602] "Information sources" refer to multiple online platforms and databases accessed to obtain product-related data.

[0603] An "emotion recognition engine" is a technology that analyzes user input and feedback and identifies the user's emotional state using natural language processing and emotion recognition algorithms.

[0604] "Analysis results" refer to the summary of price comparisons and reputation information based on the acquired product-related data.

[0605] "Purchase decision-making" is the process by which a user decides whether or not to buy a particular product.

[0606] "Adjusting the presentation method" refers to the operation of changing how information is displayed and structured based on the user's emotional state.

[0607] This system collects product information from numerous sources based on user input and presents information optimized according to the user's emotional state. Implementation requires a network environment for communication between client terminals and a server. The client terminal receives user input and transmits data to the server. Examples of suitable hardware include smartphones and PCs.

[0608] The server retrieves product data from multiple online stores and information platforms. This is typically done via RESTful APIs. AI modules are used to analyze the retrieved data; for example, price comparisons and the extraction of reputation information are performed using libraries such as Scikit-learn and TensorFlow.

[0609] Next, the emotion recognition engine analyzes the user's emotions. This is done based on the user's past usage history and direct feedback. In this process, natural language processing tools such as NLTK and SpaCy are often used.

[0610] If an emotional state is determined, the server will display information to the user that corresponds to that state. This information will be presented in a simple, easy-to-understand format for highly stressed users, and in a detailed, in-depth analysis format for calmer users. This part depends on the frontend design and will be implemented using a JavaScript framework (e.g., React or Vue.js).

[0611] As a concrete example, let's say a user is looking for a new smartphone. In this case, if the emotion recognition engine detects a state of tension, it can prioritize displaying information about products with discounts or special offers.

[0612] Example of a prompt:

[0613] How can I prioritize displaying relevant products to a user who needs a cool-down, for whatever reason?

[0614] This system allows users to receive information optimized for their emotions, supporting them in making more satisfying purchasing decisions.

[0615] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0616] Step 1:

[0617] The terminal receives user input and sends product information to the server. The input includes the name and category of the product the user is considering purchasing. The information from the terminal is transferred to the server as an HTTP request.

[0618] Step 2:

[0619] Based on the product information received by the server, it retrieves product-related data from multiple sources. The input here is the product information sent in the previous step, and the output is a dataset including product price, ratings, and inventory status. The server accesses each shopping site via a RESTful API and receives responses in JSON format.

[0620] Step 3:

[0621] The server analyzes the acquired data to perform price comparisons and summarize reputation information. The input for this step is the product-related data acquired in the previous step, and the output is concisely summarized information designed for user understanding. A machine learning algorithm using Scikit-learn is executed for data analysis, and the overall rating of each product is output as a numerical value.

[0622] Step 4:

[0623] The user inputs emotional feedback via their device, which is then received by the server. The input consists of text and selected emotional states, and the server uses this data to prepare for emotion recognition.

[0624] Step 5:

[0625] The emotion recognition engine on the server analyzes the user's emotional state. The input here is user feedback and past usage history, and the output is numerical data representing the user's emotions. Natural language processing tools are used to analyze the meaning of the feedback and identify the emotional state.

[0626] Step 6:

[0627] The server generates optimal product information based on the user's emotional state and adjusts the presentation method. The input is the emotion recognition and analysis results, and the output is customized product information displayed on the user's device. Here, the information priority and display format are adjusted.

[0628] Step 7:

[0629] Information from the server is displayed on the user's device to support their purchasing decision. The output consists of product information and recommendations prioritized based on emotions. The user reviews the presented information and decides whether or not to purchase.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Fourth Embodiment]

[0634] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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".

[0647] This system is designed to help consumers make optimal purchasing decisions by collecting data from multiple sources based on product information entered by the user and analyzing that data using artificial intelligence.

[0648] During the process, the user first uses their device to enter the name or keywords of the product they are considering purchasing. The device then sends this information to the server. Based on the entered information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data, including product price, ratings, availability, and vendor information.

[0649] Next, the server organizes the collected data and sends it to the AI ​​module. This module performs price comparisons and summarizes reviews and ratings. Here, natural language processing techniques are used to extract the content of the text data and summarize it in a format that is easy for consumers to understand.

[0650] Based on the analysis results, the server generates information to support the user's purchasing decision. Specifically, it generates data including price comparison tables for each product, store reputations, and cashback or point reward information.

[0651] Reflecting these results, the device displays the findings to the user in a visually clear and easy-to-understand manner. Furthermore, it may suggest alternative or related products to broaden the user's options. It can also suggest the optimal time and place to purchase.

[0652] For example, if a user is looking for a "high-performance laptop," this system can retrieve data from various online shops, compare prices, ratings, and availability in detail, and present the user with the most cost-effective option. Furthermore, by selecting reputable stores, it can help prevent problems after purchase.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] The user uses their device to enter the name or keywords of the product they are considering purchasing and selects the shopping sites they want to compare. The device then sends the input data to the server.

[0656] Step 2:

[0657] The server accesses multiple selected information sources based on the product information received from the terminal. It collects relevant data such as product prices, inventory, ratings, and reviews by using shopping site APIs or web scraping techniques.

[0658] Step 3:

[0659] The server organizes and formats the collected product-related data. It eliminates data duplication, converts it to a consistent format, and prepares it for transmission to the AI ​​module.

[0660] Step 4:

[0661] The server's AI module analyzes the organized data and generates product rankings based on price. It also uses natural language processing technology to summarize product reviews and user feedback, generating a summary of the information.

[0662] Step 5:

[0663] The server structures the analysis results and organizes them to include information that users can use as a reference when making purchasing decisions. This information includes price comparison tables, review summaries, and reliability information for each vendor.

[0664] Step 6:

[0665] The terminal receives analysis results sent from the server and presents them to the user in an easy-to-understand interface. Visual elements are added to make it easier for users to check the details of each product.

[0666] Step 7:

[0667] Users make purchase decisions based on the information presented on their devices. When selecting a retailer, they can refer to alternative and related products to make the best product choice.

[0668] Step 8:

[0669] If necessary, the server saves the user's selection results and uses the data for analysis to support future purchasing behavior.

[0670] (Example 1)

[0671] 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".

[0672] Consumers face the challenge of gathering information on products they are considering purchasing and making the best choice, as this involves the time-consuming process of individually researching and comparing product prices, reviews, and other factors. Furthermore, interpreting reviews and extracting reliable information is not easy. There is a growing need for systems that automate this process and support efficient and effective decision-making.

[0673] 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.

[0674] In this invention, the server includes means for receiving product information from a terminal operated by a user, means for collecting product data from a data source based on the product information, and means for organizing the collected data and analyzing it using an artificial intelligence module. This allows users to quickly compare product prices and ratings, and receive suggestions for related and alternative products, enabling them to make more appropriate purchasing decisions.

[0675] A "terminal" is an information processing device used by users to input product information, and includes devices such as personal computers and smartphones.

[0676] "Product information" refers to information that helps identify the product the user is considering purchasing, such as the product name and keywords entered by the user.

[0677] A "server" is a processing device that collects and analyzes data based on product information received from terminals and generates information to support users in making purchasing decisions.

[0678] A "data source" is an information source accessed to obtain product data, and includes APIs and websites of online shopping sites.

[0679] "Product data" refers to information collected from data sources, such as product price, ratings, inventory status, and vendor information.

[0680] An "artificial intelligence module" is a software component used to analyze collected product data and extract and summarize useful information.

[0681] "Analysis results" refer to information such as price comparison tables and store reputations obtained by analyzing product data using an artificial intelligence module.

[0682] A "visual interface" is a means of displaying analysis results in an easily understandable way for the user, and is the UI of software or applications that provide information graphically.

[0683] A "substitute product" is a product that is similar to, but different from, the product that the user is considering.

[0684] "Related products" are related products that are associated with the product a user is considering and that they might also be interested in purchasing.

[0685] A "purchase plan" is a guideline that includes the optimal choices and timing for consumers when purchasing products.

[0686] This invention is an information provision system for consumers to make optimal product selections. Specifically, the user inputs information about products they are considering purchasing using a terminal, and based on that information, a server collects and analyzes relevant product data from a data source. The following hardware and software are used in this process.

[0687] Users use a standard PC or smartphone as a terminal to enter product information. This information is sent to a server via the internet. The server uses shopping site APIs and web scraping techniques to collect data. Libraries such as Python's BeautifulSoup and Scrapy are sometimes used in this process.

[0688] The collected data is organized on a server and analyzed by an artificial intelligence module. During the analysis, natural language processing techniques are used to extract useful information from the text data and summarize prices and evaluations. Google's TensorFlow and OpenAI's generative AI models are utilized for this process.

[0689] The analysis results are presented to the user through a visual interface, providing detailed purchasing support information including price comparisons, reputation information, and alternative and related products. This allows users to easily make optimal purchasing decisions.

[0690] For example, if a user is looking for a "high-performance laptop," the system gathers information from multiple online shop data sources and compares prices, ratings, and availability. Based on these results, it presents the user with the most cost-effective option and helps prevent post-purchase problems by selecting a highly-rated store.

[0691] For example, a prompt message could be something like, "Generate a report comparing the prices and reviews of high-performance laptops," which would allow the AI ​​to organize and provide the desired information.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The user uses their device to enter the name or keywords of the product they are considering purchasing. The entered product information is sent to the server via the internet as text data, such as "high-performance laptop."

[0695] Step 2:

[0696] The server collects relevant product data from data sources based on product information received from users. In this process, the server uses shopping site APIs and web scraping techniques to obtain information such as price, ratings, and stock availability. For example, it might use Python's BeautifulSoup to extract text from a webpage. The input is product information, and the output is raw data on related products.

[0697] Step 3:

[0698] The server organizes the collected product data and prepares it as structured data. It standardizes the data format and classifies the data according to the necessary attributes. At this stage, the raw data is converted to formats such as JSON or CSV, making it ready for analysis. The output is structured product data.

[0699] Step 4:

[0700] The server supplies the organized data to an artificial intelligence module, which then uses a generative AI model to analyze the data. Natural language processing techniques are used to analyze review and rating elements, compare prices, and extract information important to the user. The input is structured product data, and the output is a summarized analysis result.

[0701] Step 5:

[0702] The server generates purchasing support information based on the analysis results. This information includes price comparison tables for each product, seller reputation, and recommendations for alternative and related products. The generated data is prepared for presentation to the user. The output is the purchasing support information presented to the user.

[0703] Step 6:

[0704] The terminal receives purchase support information sent from the server and displays it in a visually easy-to-understand format through the user interface. Frontend technologies such as React and Vue.js are used to provide an environment where users can easily understand the information and select products. The output is visually displayed information.

[0705] This series of steps allows users to efficiently compare and evaluate products and is supported in making appropriate purchasing decisions.

[0706] (Application Example 1)

[0707] 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".

[0708] In recent years, consumers have become overwhelmed by the sheer volume of information available when shopping online, making it difficult to make optimal purchasing decisions. Furthermore, efficiently comparing prices and reviews of products is challenging, leading to anxiety about purchasing decisions. Additionally, the sheer volume of product reviews makes it difficult to read them all, potentially impacting post-purchase satisfaction. These challenges need to be addressed to enable consumers to make purchasing decisions with greater confidence.

[0709] 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.

[0710] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and summarizing price comparison and reputation information, means for extracting and summarizing the content of text data using natural language processing technology, and means for displaying the analysis results in a visually easy-to-understand manner. This enables consumers to efficiently acquire information, comprehensively compare prices and reputations, and make optimal purchasing decisions.

[0711] An "information processing device" is a device that receives input from a user and acquires and processes the necessary data based on that input.

[0712] "Product-related data" refers to all information necessary for purchasing decisions, including product price, reputation, stock availability, and retailer information.

[0713] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate language that humans use on a daily basis.

[0714] "Text data summarization" refers to the process of extracting key topics and points from a large amount of text information and displaying them in a concise format.

[0715] "Displaying analysis results in a visually easy-to-understand manner" means presenting the collected and analyzed data using a graphical user interface or similar method in a way that allows users to intuitively evaluate it.

[0716] "Alternative products" refer to other products in the same category or with similar functions that can satisfy the user's interests and needs.

[0717] An "optimal purchasing plan" is a plan designed to suggest the product, timing, and location of purchase that best matches the user's needs.

[0718] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or answers based on training data.

[0719] To implement this invention, the system is equipped with an information processing device that efficiently processes product information entered by the user. The terminal is responsible for sending the product name and keywords entered by the user to the server. The server receives this information and collects product-related data from multiple sources using API-based data acquisition and web scraping techniques. A high-performance cloud server is used as the hardware, and Python's BeautifulSoup and Requests libraries are utilized as the software.

[0720] The collected data is sent to an AI module on the server, where Scikit-learn and TensorFlow are used to perform price comparisons and reputation analysis of products. Simultaneously, natural language processing techniques are used to summarize product reviews and ratings in an easy-to-understand format. This part utilizes libraries such as NLTK (Natural Language Toolkit).

[0721] The analysis results are fed back to the user's device in a visually easy-to-understand format. This interface is implemented as an application using React Native, allowing users to visually check the information on their smartphones. Users can receive support to make the best product selection.

[0722] For example, when a user searches for a "high-performance laptop," the system compares prices, ratings, and availability from numerous shops and can use prompts such as "Show me the cheapest product sorted by highest review rating" to determine the most cost-effective option. In this way, users can confidently make the best purchase.

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] User input of product information

[0726] The user uses a terminal to enter the name or keywords of the product they are considering purchasing. The entered information is sent from the terminal to the server. In this step, the input is the product name or keywords, and the output is the transmission of data to the server.

[0727] Step 2:

[0728] Acquisition of product-related data

[0729] Based on the received product information, the server uses APIs and web scraping techniques from selected shopping sites to collect relevant data such as product price, reviews, availability, and seller. The input for this step is product information, and the output is the collected product-related data.

[0730] Step 3:

[0731] Data analysis and summarization

[0732] The server sends the collected data to an AI module, which uses Scikit-learn and TensorFlow to perform price comparisons and reputation analysis. Simultaneously, it uses natural language processing techniques to summarize product reviews and convert them into a user-friendly format. The input for this step is product-related data, and the output is the analyzed data and summarized review information.

[0733] Step 4:

[0734] Presenting the optimal option

[0735] Based on the analysis results, the server generates information to support purchase decisions and displays it on the user's terminal. The user views the information through an interface built with React Native and receives visual feedback when selecting products. The input for this step is the analysis results, and the output is the displayed visual feedback.

[0736] Step 5:

[0737] Suggestions for alternative and related products

[0738] Based on the analysis results, the server suggests alternative and related products to the user and provides optimal advice on the best time and place to purchase them. This information is used by the user to create the optimal purchasing plan. The input for this step is the user's interests and conditions, and the output is suggested product information and advice.

[0739] 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.

[0740] This system collects product information obtained through user input from various sources and presents shopping information optimized according to the user's emotional state. In particular, this invention incorporates an emotion engine and analyzes the user's emotions to enable a more personalized user experience.

[0741] The user uses a device to enter the product name and related information of the item they are considering purchasing. The device sends the entered information to the server. Based on the received information, the server accesses selected information sources to collect relevant data, including product price, ratings, and stock availability. This utilizes APIs from various shopping sites.

[0742] The collected data is organized on a server, and an AI module performs price comparisons and summarizes reputation information. Then, an emotion engine analyzes user input, system usage history, or direct user feedback to determine the emotional state. Natural language processing and emotion recognition algorithms are used for this analysis.

[0743] Based on the user's emotions recognized by the emotion engine, the server adjusts how products are presented. For example, a user judged to be stressed might be shown concise and easy-to-understand information, while a user judged to be calm might be provided with more detailed analytical information.

[0744] Furthermore, the server takes emotional states into consideration when suggesting alternative or related products. This process is important for expanding the customer's choices when making a purchase.

[0745] For example, if a user is looking for a "high-performance gaming laptop," the system can customize the information presented based on the user's emotional state. In addition to regular product information, if the emotion engine determines that the user is about to make a stressful decision, it will prioritize displaying products with discounts or special offers.

[0746] In this way, systems equipped with an emotion engine allow users to make purchasing decisions based on information appropriate to their emotional state, resulting in a more satisfying shopping experience.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] The user uses the device to enter the name or keywords of the product they are considering purchasing and selects a source of information. At this time, the device also records the user's past purchase history and input history.

[0750] Step 2:

[0751] The terminal sends user input data to the server, and simultaneously sends the user's facial expressions, voice data, etc., to the emotion engine. The emotion engine analyzes this data to determine the user's emotional state.

[0752] Step 3:

[0753] The server accesses multiple shopping site APIs based on the entered product information to collect product-related data, including price, stock availability, ratings, and customer reviews. The collected data is temporarily stored in a database.

[0754] Step 4:

[0755] The server passes product-related data stored in the database to the AI ​​module, which performs price comparisons and summarizes reputation information. The AI ​​module calculates a score for each product and creates a ranking.

[0756] Step 5:

[0757] Based on the user's emotional state analyzed by the emotion engine, the server adjusts how information is presented. For example, if the system detects that the user is in a hurry, it will simply present only the most important information.

[0758] Step 6:

[0759] The server takes the user's emotional state into account and presents alternative or related products as options that match the user's needs. This allows the user to evaluate other options as needed.

[0760] Step 7:

[0761] The terminal receives analysis results and product suggestions sent from the server and displays the information using a customized interface based on the user's emotional state and interests.

[0762] Step 8:

[0763] Users make optimal purchasing choices based on the information displayed on their devices. The selection results and emotional feedback are recorded in a database to improve the user experience in the future.

[0764] (Example 2)

[0765] 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".

[0766] In modern e-commerce, users must deal with a vast amount of product information, which can make purchasing decisions difficult. Furthermore, providing information without considering the user's emotional state can sometimes decrease satisfaction. Therefore, it is necessary to provide optimal information tailored to the user's emotional state to support their purchasing decisions.

[0767] 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.

[0768] In this invention, the server includes means for a device that acquires information about products entered by the user, means for collecting product information from multiple information sources based on the information, means for analyzing the collected information to summarize price comparison and reputation information, and means for determining the user's emotional response and presenting optimized information. This makes it possible to provide product information tailored to the user's emotional state and to support purchasing decisions.

[0769] A "device" is a component of hardware or software designed to perform a specific function.

[0770] "Means" refers to the methods, processes, or equipment used to achieve a particular objective.

[0771] "Information source" refers to the underlying content or platform from which data and information are obtained.

[0772] "Information" refers to useful knowledge obtained as a result of data being processed or interpreted in some way.

[0773] "Collection" refers to the activity or process of systematically gathering specific data or information.

[0774] "Analysis" is the act of examining data and information in detail and making its constituent elements easier to understand.

[0775] "Price comparison" is the process of comparing price information for a product obtained from multiple sources to determine its relative value.

[0776] "Reputation information" refers to data on the social evaluation of a product obtained from user and third-party reviews and ratings.

[0777] "Emotional response" refers to the emotional state or changes of the user, and is an important element in the product purchase process.

[0778] "Optimization" refers to the efficient adjustment or improvement of a system or process in order to achieve the best possible results for a particular purpose.

[0779] This invention is a shopping system that collects relevant data from various sources based on product information entered by the user and provides optimal information in response to the user's emotional response. Embodiments of this invention are described below.

[0780] The user enters information about the product they are considering purchasing into a terminal. This terminal has the function of sending the entered information to a server. The terminal is designed to be a common device such as a regular PC or smartphone, and can utilize dedicated application software.

[0781] The server collects product-related data from multiple sources based on the user's input. Specifically, it uses APIs from shopping platforms to obtain data. For example, APIs from shopping malls and e-commerce sites are one such example, and data such as the price, ratings, and stock status of related products are collected.

[0782] The collected data is analyzed on the server by an AI module. This module eliminates data duplication and performs price comparisons and summarizes reputation information. Subsequently, an emotion engine installed on the server uses natural language processing technology and emotion recognition algorithms to analyze the user's emotional responses and determine the user's emotional state.

[0783] Based on this analysis, the server displays information on the screen that is appropriate for the user's emotional state. If the user is stressed, the information is kept as simple as possible; if they are relaxed, more detailed information is provided. The server also considers the user's emotional state and suggests related or alternative products.

[0784] For example, if a user is searching for a "high-performance gaming laptop" and the server determines that they are in a stressful situation, it can prioritize displaying information about discounted products and those with special offers.

[0785] An example of a prompt to input into a generative AI model would be, "Generate a program that provides shopping information tailored to the user's emotional state." This would allow users to have a more comfortable and satisfying shopping experience.

[0786] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0787] Step 1:

[0788] The user uses a terminal to input information about the product they are considering purchasing. For example, by entering a keyword such as "gaming laptop," information about a specific product category is retrieved. This input data is then formatted and sent to the server. At this point, the output is product information converted into a format that the server can receive.

[0789] Step 2:

[0790] The server receives product information sent from the terminal. Based on this information, the server initiates an operation to access APIs of multiple shopping platforms. Specifically, it sends API requests to retrieve data such as product price, ratings, and inventory status. The input is the product name, and the output is the raw data obtained from the APIs.

[0791] Step 3:

[0792] The server analyzes the acquired raw data. Using an AI module, it compares prices and summarizes reputation information for each product. In this process, it eliminates duplicate data and verifies data consistency. The input is raw data obtained from the API, and the output is organized price comparison information and reputation information.

[0793] Step 4:

[0794] The emotion engine installed on the server analyzes the user's emotional responses. The input consists of the user's input information and past usage history, and natural language processing techniques are used to determine the emotional state (e.g., stress, relaxation). The output is the determination result indicating the user's emotional state.

[0795] Step 5:

[0796] The server optimizes information presentation based on the analysis results of the emotion engine. Specifically, it simplifies information when the user is stressed and provides detailed information to relaxed users. The input is the result of the emotional state assessment, and the output is the optimized information presentation.

[0797] Step 6:

[0798] The server considers the user's emotional state when suggesting relevant or alternative products. For example, if the user is feeling stressed, it prioritizes displaying information about products with discounts or special offers. The input is the optimized information presentation, and the output is the emphasized information presentation and product suggestions.

[0799] (Application Example 2)

[0800] 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".

[0801] Conventional information processing devices present product information without considering the user's emotional state, thus failing to provide an optimal purchasing experience. In particular, when a user is stressed or, conversely, in a positive state, the information presented is often unsuitable for that emotional state. As a result, users may hesitate unnecessarily when making purchasing decisions.

[0802] 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.

[0803] In this invention, the server includes means for receiving product information entered by a user via an information processing device, means for acquiring product-related data from multiple information sources based on the product information, means for analyzing the acquired data and performing price comparisons and summarizing reputation information, means for adjusting the information presentation method using an emotion recognition engine that analyzes the user's emotional state, and means for presenting the analysis results to the user and supporting purchasing decision-making. This makes it possible to present information optimally according to the user's emotional state.

[0804] An "information processing device" is a device that includes hardware and software for receiving product information from users and performing data analysis and optimization.

[0805] "Product information" refers to data entered by the user regarding the product they are considering purchasing, and includes the product name, price, and ratings.

[0806] "Information sources" refer to multiple online platforms and databases accessed to obtain product-related data.

[0807] An "emotion recognition engine" is a technology that analyzes user input and feedback and identifies the user's emotional state using natural language processing and emotion recognition algorithms.

[0808] "Analysis results" refer to the summary of price comparisons and reputation information based on the acquired product-related data.

[0809] "Purchase decision-making" is the process by which a user decides whether or not to buy a particular product.

[0810] "Adjusting the presentation method" refers to the operation of changing how information is displayed and structured based on the user's emotional state.

[0811] This system collects product information from numerous sources based on user input and presents information optimized according to the user's emotional state. Implementation requires a network environment for communication between client terminals and a server. The client terminal receives user input and transmits data to the server. Examples of suitable hardware include smartphones and PCs.

[0812] The server retrieves product data from multiple online stores and information platforms. This is typically done via RESTful APIs. AI modules are used to analyze the retrieved data; for example, price comparisons and the extraction of reputation information are performed using libraries such as Scikit-learn and TensorFlow.

[0813] Next, the emotion recognition engine analyzes the user's emotions. This is done based on the user's past usage history and direct feedback. In this process, natural language processing tools such as NLTK and SpaCy are often used.

[0814] If an emotional state is determined, the server will display information to the user that corresponds to that state. This information will be presented in a simple, easy-to-understand format for highly stressed users, and in a detailed, in-depth analysis format for calmer users. This part depends on the frontend design and will be implemented using a JavaScript framework (e.g., React or Vue.js).

[0815] As a concrete example, let's say a user is looking for a new smartphone. In this case, if the emotion recognition engine detects a state of tension, it can prioritize displaying information about products with discounts or special offers.

[0816] Example of a prompt:

[0817] How can I prioritize displaying relevant products to a user who needs a cool-down, for whatever reason?

[0818] This system allows users to receive information optimized for their emotions, supporting them in making more satisfying purchasing decisions.

[0819] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0820] Step 1:

[0821] The terminal receives user input and sends product information to the server. The input includes the name and category of the product the user is considering purchasing. The information from the terminal is transferred to the server as an HTTP request.

[0822] Step 2:

[0823] Based on the product information received by the server, it retrieves product-related data from multiple sources. The input here is the product information sent in the previous step, and the output is a dataset including product price, ratings, and inventory status. The server accesses each shopping site via a RESTful API and receives responses in JSON format.

[0824] Step 3:

[0825] The server analyzes the acquired data to perform price comparisons and summarize reputation information. The input for this step is the product-related data acquired in the previous step, and the output is concisely summarized information designed for user understanding. A machine learning algorithm using Scikit-learn is executed for data analysis, and the overall rating of each product is output as a numerical value.

[0826] Step 4:

[0827] The user inputs emotional feedback via their device, which is then received by the server. The input consists of text and selected emotional states, and the server uses this data to prepare for emotion recognition.

[0828] Step 5:

[0829] The emotion recognition engine on the server analyzes the user's emotional state. The input here is user feedback and past usage history, and the output is numerical data representing the user's emotions. Natural language processing tools are used to analyze the meaning of the feedback and identify the emotional state.

[0830] Step 6:

[0831] The server generates optimal product information based on the user's emotional state and adjusts the presentation method. The input is the emotion recognition and analysis results, and the output is customized product information displayed on the user's device. Here, the information priority and display format are adjusted.

[0832] Step 7:

[0833] Information from the server is displayed on the user's device to support their purchasing decision. The output consists of product information and recommendations prioritized based on emotions. The user reviews the presented information and decides whether or not to purchase.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] The information processing device provides a means for receiving product information entered by the user,

[0858] A means for obtaining product-related data from multiple information sources based on the aforementioned product information,

[0859] A means for analyzing acquired data and performing price comparisons and summarizing reputation information,

[0860] A means of presenting analysis results to users and supporting their purchasing decisions,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which proposes alternative or related products based on the analysis results.

[0864] (Claim 3)

[0865] The system according to claim 1, which presents an optimal purchase plan based on information sources or additional conditions specified by the user.

[0866] "Example 1"

[0867] (Claim 1)

[0868] A means of receiving product information from a terminal operated by the user,

[0869] A means for collecting product data from a data source based on the aforementioned product information,

[0870] A method for organizing the collected data and analyzing it using an artificial intelligence module,

[0871] Based on the analysis results, a means to support purchasing decisions by providing a visual interface,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which proposes alternative or related products based on the analysis results.

[0875] (Claim 3)

[0876] The system according to claim 1, which presents an optimal purchasing plan based on specified data sources or conditions.

[0877] "Application Example 1"

[0878] (Claim 1)

[0879] The information processing device provides a means for receiving product information entered by the user,

[0880] A means for obtaining product-related data from multiple information sources based on the aforementioned product information,

[0881] A means for analyzing acquired data and performing price comparisons and summarizing reputation information,

[0882] A means of presenting analysis results to users and supporting their purchasing decisions,

[0883] A means of extracting and summarizing the content of text data using natural language processing technology,

[0884] A means of displaying analysis results in an easy-to-understand visual format,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, which suggests alternative or related products and indicates the optimal time and place of purchase.

[0888] (Claim 3)

[0889] The system according to claim 1, which uses a generated AI model to present an optimal purchase plan based on information sources or additional conditions specified by the user.

[0890] "Example 2 of combining an emotion engine"

[0891] (Claim 1)

[0892] A means for a device that acquires product information entered by a user,

[0893] A means for collecting product information from multiple sources based on the aforementioned information,

[0894] A means of analyzing the collected information to summarize price comparison and reputation information,

[0895] A means of judging the emotional response of users and presenting optimized information,

[0896] ...

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, which recommends alternative or related products based on the emotional response of the user.

[0900] (Claim 3)

[0901] The system according to claim 1, which presents a purchase plan that takes into account the user's emotions.

[0902] "Application example 2 when combining with an emotional engine"

[0903] (Claim 1)

[0904] The information processing device provides a means for receiving product information entered by the user,

[0905] A means for obtaining product-related data from multiple information sources based on the aforementioned product information,

[0906] A means for analyzing acquired data and performing price comparisons and summarizing reputation information,

[0907] A means for adjusting the information presentation method using an emotion recognition engine that analyzes the emotional state of the user,

[0908] A means of presenting analysis results to users and supporting their purchasing decisions,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which, based on the analysis results, takes into account the user's emotional state and proposes alternative or related products.

[0912] (Claim 3)

[0913] The system according to claim 1, which presents an optimal purchase plan based on information sources or additional conditions specified by the user, and in accordance with the user's emotional state. [Explanation of Symbols]

[0914] 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. The information processing device provides a means for receiving product information entered by the user, A means for obtaining product-related data from multiple information sources based on the aforementioned product information, A means for analyzing acquired data and performing price comparisons and summarizing reputation information, A means of presenting analysis results to users and supporting their purchasing decisions, A means of extracting and summarizing the content of text data using natural language processing technology, A means of displaying analysis results in an easy-to-understand visual format, A system that includes this.

2. The system according to claim 1, which suggests alternative or related products and indicates the optimal time and place of purchase.

3. The system according to claim 1, which uses a generated AI model to present an optimal purchase plan based on information sources or additional conditions specified by the user.

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