System

Abstract

To provide a system capable of reducing a burden on a user and efficiently selecting an optimum commodity in a short time.SOLUTION: Means for extracting related products from a database based on a search query received from a terminal, means for collating the extracted related products with a past history of a user and preference data of a similar user to generate an optimal product list, means for transmitting the generated optimal product list to the terminal, means for collecting word-of-mouth information on products from a plurality of information sources, means for analyzing the collected word-of-mouth information to generate a summary, means for transmitting the generated summary to the terminal, means for acquiring product prices from a plurality of purchase sites, the system includes a means for comparing and a means for specifying the optimum purchase site based on the price information and transmitting it to the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Online shopping presents a problem in that users spend a lot of time selecting products, checking reviews, and comparing prices. The sheer volume and variety of information makes it difficult to efficiently obtain useful information, and choosing the optimal product requires a great deal of effort. This problem is particularly serious for users who are not good at checking reviews or who prioritize time and performance, and it prevents them from making satisfying purchasing decisions. [Means for solving the problem]

[0005] The present invention reduces the burden on users and enables them to efficiently select optimal products in a short amount of time by using a system that includes: means for extracting related products from a database based on a search query received from a terminal; means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users; means for transmitting the generated optimal product list to the terminal; means for collecting word-of-mouth information about products from multiple information sources; means for analyzing the collected word-of-mouth information and generating a summary; means for transmitting the summarized word-of-mouth information to the terminal; means for acquiring and comparing product prices from multiple purchasing sites; and means for identifying the optimal purchasing site based on the price information and transmitting the identified site to the terminal. This system uses e-commerce sites, social networking sites, and video sharing platforms as means for collecting word-of-mouth information, and further takes into account information on point redemptions and coupon applications in the price comparison means, thereby providing the user with the most optimal purchasing experience.

[0006] A "terminal" is a device that a user operates to access the system and input and receive information, and specifically refers to a smartphone, tablet, PC, etc.

[0007] A "server" refers to a computer system that processes data in response to user requests and provides product information, word-of-mouth information, price information, and the like.

[0008] A "search query" refers to information that a user enters to describe the conditions (price range, brand, category, etc.) for a product they are considering purchasing.

[0009] "Database" refers to a stored collection of related product information that can be accessed by a server based on a search query.

[0010] "Related products" refer to products that are extracted from a database based on a user's search query and that match the conditions.

[0011] "History" refers to a record of a user's past purchases and search activities.

[0012] "Matching" refers to comparing the extracted related products with the user's past history and the preference data of similar users.

[0013] A "product list" refers to a list of multiple recommended products that are organized and presented to a user.

[0014] "Word-of-mouth information" refers to user evaluations and opinions about a specific product collected from multiple sources (e-commerce sites, social media, video sharing platforms, etc.).

[0015] "Sources" refer to media such as e-commerce sites, social media, and video sharing platforms from which word-of-mouth information is collected.

[0016] "Natural language processing" refers to the technology used to analyze collected word-of-mouth information and summarize it in a form that is easy for humans to understand.

[0017] "Summary" refers to information that condenses the collected reviews, particularly the evaluation scores, positive and negative opinions, and specific advantages and disadvantages.

[0018] "Purchase Site" refers to an online store where users can purchase products.

[0019] "Price Information" refers to the selling price of a particular product on each purchasing site.

[0020] "Comparison" refers to evaluating the price of each product, point rewards, coupon application, etc. based on price information obtained from multiple purchasing sites, and identifying the best place to purchase.

[0021] "Point redemption" refers to reward points earned when purchasing a product, which are given to the user as credits that can be used for future purchases.

[0022] A "coupon" is a special offer that can be used to receive a discount on a product purchase. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] This invention is an AI system that allows users to efficiently shop online, searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[0045] Product search and recommendations

[0046] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[0047] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[0048] By receiving this product list, users are presented with products that best meet their needs, enabling them to make efficient product selections.

[0049] Collecting and summarizing reviews

[0050] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[0051] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[0052] This allows users to efficiently understand the vast amount of word-of-mouth information and quickly grasp the advantages and disadvantages of a product.

[0053] Price comparison and recommendations for recommended e-commerce sites

[0054] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[0055] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[0056] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[0057] Specific examples

[0058] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[0059] The server extracts models such as iPhone (registered trademark) SE and iPhone 11 that meet the criteria from the database, and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users.

[0060] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price."

[0061] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[0062] In this way, users can quickly search for products, check reviews, and find the best place to buy them. This system will greatly contribute to users' efficient and satisfying online shopping.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[0066] Step 2:

[0067] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[0068] Step 3:

[0069] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[0070] Step 4:

[0071] The server transmits the generated optimal product list to the user terminal.

[0072] Step 5:

[0073] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[0074] Step 6:

[0075] The user selects a product they are interested in on the screen and requests that reviews of that product be collected. The request is sent to the server.

[0076] Step 7:

[0077] The server receives a review collection request from the user and collects review information about the target product from multiple sources (e.g., e-commerce sites, social media, video sharing platforms, etc.).

[0078] Step 8:

[0079] The server analyzes the collected reviews using natural language processing technology, extracting positive and negative opinions, specific advantages and disadvantages, and generating summaries.

[0080] Step 9:

[0081] The server transmits the generated summary information of the word-of-mouth to the user terminal.

[0082] Step 10:

[0083] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[0084] Step 11:

[0085] A user requests price information and where to purchase a product they like. The request is sent to the server.

[0086] Step 12:

[0087] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[0088] Step 13:

[0089] The server compares the price information it has acquired and identifies the best place to purchase, taking into consideration point redemption and coupon application information.

[0090] Step 14:

[0091] The server transmits information about the optimal purchasing source identified to the user terminal.

[0092] Step 15:

[0093] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] In today's online shopping environment, users must access a vast amount of product information, reviews, and price information, making it difficult to select the best product. Furthermore, finding the lowest price across multiple purchasing sites requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently search for products, summarize reviews, and compare prices, allowing users to quickly find the best product and where to purchase it.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by matching the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting product review information from multiple information sources, means for analyzing the collected review information and generating a summary, means for transmitting the summarized review information to the terminal, means for obtaining and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified product to the terminal, means for performing natural language processing using a generative AI model to summarize the collected review information, and means for obtaining price information from purchasing sites based on a price comparison request received from the terminal and comparing the price information, including point redemption and coupon application information. This enables users to quickly search for products, check reviews, and find the optimal purchasing site.

[0099] A "terminal" is a device that a user operates and inputs information, and includes a personal computer, a smartphone, a tablet, and the like.

[0100] A "server" is a computer system that processes data, accesses databases, and collects information, and communicates with terminals via a network.

[0101] A "search query" is a condition or keyword that a user inputs through a terminal, and includes input information for a product search.

[0102] A "database" is a system for efficiently storing and managing product information and user information, and includes relational databases and NoSQL databases.

[0103] "Related products" are products that match or are related to the product a user is looking for via a search query.

[0104] "Past history" refers to a record of the user's previous purchases and browsing, and is information used to understand the user's preferences.

[0105] "Similar user preference data" refers to data of other users who have similar preferences and purchasing patterns, and is used to generate an optimal product list for a specific user.

[0106] A "product list" is a list of optimal products generated based on the user's search criteria and past history data.

[0107] "Review information" refers to ratings and opinions provided by product purchasers or users, and is primarily collected from e-commerce sites, social networking services, and video-sharing platforms.

[0108] "Analysis" is the act of processing collected data to extract useful information, and includes, among other things, natural language processing and data mining techniques.

[0109] A "summary" is information that extracts the main opinions and evaluations from a vast amount of review information and summarizes them concisely.

[0110] A "purchasing site" is a website that allows users to purchase products online, and includes multiple platforms.

[0111] "Price Information" is a detailed description of the price offered at each purchasing site for a particular product.

[0112] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and uses that knowledge to generate or analyze new data.

[0113] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0114] "Point redemption" is a benefit that allows you to use points earned at the time of purchase under certain conditions at a later date.

[0115] A "coupon" is a code or ticket that provides a discount or benefit that can be applied when purchasing a product.

[0116] An "e-commerce site" is a website for buying and selling goods online.

[0117] A "social networking service" is a web service that allows users to interact with each other, share information, and post reviews.

[0118] A "video sharing platform" is a web service that allows users to post videos and allow other users to view them.

[0119] This invention is an AI system that helps users shop online efficiently by searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[0120] Product search and recommendations

[0121] The user opens a product search screen using a device and enters a search query, such as "price range: 30,000 to 50,000 yen, brand: Apple, category: smartphone."

[0122] The device sends the search query entered by the user in JSON format to the server. The server receives this query and uses an SQL query to extract products that meet the criteria from a database such as MySQL (registered trademark). A query such as "SELECT FROM products WHERE price BETWEEN (registered trademark) 30000 AND 50000 AND brand='Apple' AND category='smartphone'" is used.

[0123] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate an optimal product list. The generated product list is sent to the terminal in JSON format.

[0124] The terminal parses the received product list and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[0125] Collecting and summarizing reviews

[0126] If a user wants more information about a specific product, they can request a review of that product, for example, by clicking the Details button for the "iPhone 11."

[0127] The device sends this request in JSON format to the server, which then uses Python scripts to scrape reviews from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium.

[0128] The server analyzes the collected review information using natural language processing technology (e.g., BERT or GPT-3 (registered trademark)) and generates summaries. It extracts key opinions and ratings and summarizes them as "good battery life," "great camera performance," and "expensive price."

[0129] The device then displays the summary information to the user, with each summary point displayed as a bullet point and an associated star rating.

[0130] Price comparison and recommendations for recommended e-commerce sites

[0131] When a user is considering purchasing a product, they request a price comparison, for example, by clicking the "Buy iPhone 11" button.

[0132] The terminal sends this request in JSON format to the server, which then uses APIs to collect price information from multiple purchasing sites. For example, it uses the API of an e-commerce site to obtain price information.

[0133] The server compares and analyzes the acquired price information, taking into account point redemption and coupon application information, and uses the Python Pandas library to format the data and identify the best place to purchase.

[0134] Finally, the device displays the optimal purchasing information received from the server to the user, providing specific price information and purchasing benefits, such as "Rakuten is the cheapest, and another site offers 10% points back."

[0135] Specific examples

[0136] For example, if a user searches for "price range: ¥30,000-¥50,000, brand: Apple, category: smartphone," the server extracts products that match the criteria, such as the iPhone SE or iPhone 11, from the MySQL database and displays them on the device. When a user is interested in the iPhone 11 and sends a request to check reviews, the server collects review information using Python scripts and scraping tools, and uses a natural language processing model to generate summaries such as "good battery life" and "great camera performance." Finally, when a user requests a price comparison for the iPhone 11, the server retrieves price information from the APIs of multiple purchasing sites, analyzes it using PANDAS, and identifies the best place to purchase, allowing the user to purchase the product conveniently and quickly.

[0137] This allows users to quickly search for products, check reviews, and find the best place to buy, making this system a great contribution to making online shopping more efficient and satisfying for users.

[0138] Prompt Sentence Examples

[0139] As a concrete example of how this system can be implemented, consider the following prompt:

[0140] "Search for Apple smartphones in the price range of 30,000 to 50,000 yen."

[0141] "Collect iPhone 11 reviews."

[0142] "Compare iPhone 11 prices across multiple sites."

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] The user opens a product search screen on their device and enters a search query. For example, they enter conditions such as "Price range: 30,000 to 50,000 yen, Brand: Apple, Category: Smartphone." This becomes the input data.

[0146] Step 2:

[0147] The device sends the search query entered by the user to the server in JSON format. The data received by the server contains the search conditions.

[0148] Step 3:

[0149] The server analyzes this query and extracts products that match the conditions from a database such as MySQL using an SQL query. Specifically, it executes a query such as "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='Smartphone'". The related products retrieved from the database are output.

[0150] Step 4:

[0151] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate the optimal product list. As a result of the comparison, the optimal product list for the user is output in JSON format.

[0152] Step 5:

[0153] The server sends the generated optimal product list to the terminal as an API response. The data received by the terminal is the matched product list.

[0154] Step 6:

[0155] The terminal analyzes the product list received from the server and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[0156] Step 7:

[0157] If a user wants more information about a specific product, they can request a review of that product, for example by clicking the "iPhone 11" details button. This becomes new input data.

[0158] Step 8:

[0159] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[0160] Step 9:

[0161] The server uses Python scripts to collect review information from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium, and outputs the collected review information.

[0162] Step 10:

[0163] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates a summary, which extracts key opinions and ratings.

[0164] Step 11:

[0165] The server sends the generated review summary in JSON format to the device, and the data received by the device is the summarized review information.

[0166] Step 12:

[0167] The terminal displays the summary information received from the server to the user, with each summary point displayed as a bullet point and an associated star rating.

[0168] Step 13:

[0169] When a user is considering purchasing a product, they request a price comparison. For example, they click the "Buy iPhone 11" button. This becomes new input data.

[0170] Step 14:

[0171] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[0172] Step 15:

[0173] The server uses an API to collect price information from multiple purchasing sites. For example, it obtains price information using the API of an e-commerce site. The collected price information is output.

[0174] Step 16:

[0175] The server compares and analyzes the collected price information, taking into account point redemption and coupon application information. It uses the Python Pandas library to format the data and identify the optimal purchasing location. The data is then output as optimal purchasing location information.

[0176] Step 17:

[0177] The server sends the optimal purchasing information to the device in JSON format. The data received by the device is the optimal purchasing information.

[0178] Step 18:

[0179] The device displays the optimal purchasing information received from the server in an easy-to-read format for the user. For example, it shows specific price information and purchasing benefits, such as "Rakuten is the cheapest, and other sites offer 10% points back."

[0180] This allows users to quickly search for products, check reviews, and find the best place to buy.

[0181] (Application example 1)

[0182] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0183] When shopping online, users must spend a lot of time and effort selecting the best product from a vast amount of information. Furthermore, checking reviews and comparing prices is typically done manually, which is inefficient. The lack of automated processes for summarizing useful reviews and identifying optimal purchasing sites limits the user experience. Furthermore, while smartphone applications would improve user convenience, there are few such systems available. There is a need for an efficient and integrated system to solve these issues.

[0184] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0185] In this invention, the server

[0186] means for extracting related products from a database based on a search query received from the terminal;

[0187] A means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users;

[0188] means for transmitting the generated optimal product list to a terminal;

[0189] A means for collecting evaluation information about products from multiple sources;

[0190] means for analyzing the collected evaluation information and generating a summary;

[0191] means for transmitting the summarized evaluation information to a terminal;

[0192] A means to obtain and compare product prices from multiple purchasing sites,

[0193] means for identifying an optimal purchasing site based on the price information and transmitting the information to the terminal;

[0194] means functioning as an application installed on a smartphone;

[0195] A means to recommend the best products online based on users' purchasing queries,

[0196] natural language processing means for acquiring and analyzing the collected evaluation information from multiple information sources;

[0197] A means to identify the best place to purchase, taking into account price information and point redemption and coupon application information;

[0198] Includes:

[0199] This allows users to efficiently search for products, check review summaries, compare prices, and instantly find the best purchase, significantly improving the convenience and user experience of online shopping.

[0200] definition statement

[0201] A "terminal" is a device that a user directly operates, such as to input a search query or receive a product list.

[0202] A "search query" is a request that includes keywords and conditions that a user enters when searching for a product.

[0203] A "database" is a collection of information that stores product information, user history, and the like.

[0204] "Related Products" refers to products extracted from a database based on a user's search query.

[0205] "User's past history" refers to data relating to purchases and searches that the user has previously made.

[0206] "Preference data" is information relating to a user's preferences and tendencies.

[0207] The "generated optimal product list" is a product list generated by the server based on the search query and the user's history and preference data.

[0208] "Evaluation information" refers to feedback information such as word-of-mouth and reviews about a product.

[0209] "Analysis" is the process of using collected information to extract useful data.

[0210] A "summary" is a concise summary of the collected evaluation information.

[0211] A "purchasing site" is a website that sells products online.

[0212] "Price information" refers to the sales price offered by each purchasing site for a particular product.

[0213] An "application installed on a smartphone" is a program that runs on a smartphone and realizes the functions of the present invention.

[0214] "Natural language processing means" is a technology that analyzes language data, extracts meaning, and processes information.

[0215] "Point redemption" is a system in which a certain number of points are returned for each purchase.

[0216] "Coupon application" refers to the use of a code or voucher to receive a discount or special offer when purchasing a product.

[0217] MODE FOR CARRYING OUT THE INVENTION

[0218] The present invention provides a system for making online shopping more efficient. A user accesses the system using a terminal and inputs a search query. Specific embodiments of the system are described below.

[0219] Product search and recommendations

[0220] When a user enters a search query from a terminal, the query is sent to a server. The server extracts related products from a database based on the received search query. This database contains not only product information but also the user's past purchase history and preference data. The server uses this data to generate a product list that is optimal for the user. The generated product list is sent to the terminal, where the user can refer to it.

[0221] Collecting and summarizing reviews

[0222] When a user is interested in a particular product, they send a request to the server to check the product's reviews. The server collects reviews from multiple sources, including e-commerce sites, social networking services, and video sharing platforms. The collected reviews are analyzed and summarized using natural language processing technology. The summarized reviews are then sent to the user's device, where they can be viewed.

[0223] Price comparison and shopping site recommendations

[0224] When a user is considering purchasing a product, it is important to know the best place to purchase it, including price information and special offer information (point redemption and coupon application information). When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information. Based on this information, the server identifies the best place to purchase and sends that information to the terminal. The user can purchase the product under the best conditions.

[0225] Smartphone application

[0226] This system can also be implemented as a smartphone application. This application recommends optimal products online based on a purchasing query entered by the user. Furthermore, it obtains collected evaluation information from multiple sources and summarizes it using natural language processing technology (e.g., Gensim). It also includes a function to identify the optimal purchasing destination by taking into account price information and special offer information.

[0227] Specific examples

[0228] For example, consider the case where a user enters the query "smartphone" to perform a search. The server extracts smartphones that match the search criteria from the database and generates an optimal product list by taking into account past purchase history and preferences. When a user becomes interested in a particular smartphone and sends a request to check its reviews, the server collects reviews from e-commerce sites and social networking services and summarizes them using natural language processing technology. Finally, the server recommends the optimal purchasing site by taking into account price comparisons and special offers, allowing the user to purchase the product at the best price.

[0229] Prompt Sentence Examples

[0230] When a user searches for "iPhone 12 price comparison":

[0231] 1. Get product list from API

[0232] 2. Collecting ratings from multiple sources using libraries such as BeautifulSoup

[0233] 3. Summarize the evaluation information using the summarize function of Gensim

[0234] 4. Obtain and compare price information from multiple purchasing sites

[0235] This allows users to efficiently search for products, check summary reviews, compare prices, and instantly find the best place to buy, significantly improving the convenience of online shopping.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Program processing steps

[0238] Step 1:

[0239] The user enters a search query from their device. The search query includes conditions such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." This becomes a request to the server.

[0240] Step 2:

[0241] The server extracts related products from the database based on the received search query. The search query is used as input (conditions) and a database search is performed based on this. The output is a list of related products.

[0242] Step 3:

[0243] The server compares the extracted related products with the user's past history and similar users' preference data. The server uses the user history data and preference data as inputs and integrates them to output an optimal product list.

[0244] Step 4:

[0245] The server sends the generated optimal product list to the user's terminal. The terminal displays the received product list. The input is the optimal product list, and the output is the list displayed on the terminal.

[0246] Step 5:

[0247] When a user wants to check the details of a particular product, the user sends a request for reviews of that product. The terminal sends the request to the server.

[0248] Step 6:

[0249] The server collects reviews of the product from multiple sources. The input is the request and the source URL, and the output is the collected reviews. Here, we use a library such as BeautifulSoup to extract text from the web page.

[0250] Step 7:

[0251] The server analyzes the collected reviews using natural language processing technology and generates summaries. Specifically, it uses the summarize function from the Gensim library. The input is the text data of the reviews, and the output is the summarized text.

[0252] Step 8:

[0253] The server sends the summarized review information to the user's terminal, and the terminal displays the summarized information. The input is the summarized review information, and the output is the information displayed on the terminal.

[0254] Step 9:

[0255] The user submits a request for a price comparison of a product. The device sends the request to the server.

[0256] Step 10:

[0257] The server retrieves and compares product prices from multiple purchasing sites. The input is the request and the purchasing site URL, and the output is the price information retrieved from each site.

[0258] Step 11:

[0259] The server identifies the optimal purchasing location by adding information on rewards and coupons to the acquired price information. The input is price information and additional information, and the output is the optimal purchasing location.

[0260] Step 12:

[0261] The server sends the optimal purchasing information to the user's terminal. The terminal displays the received information. The input is the optimal purchasing information, and the output is the information displayed on the terminal.

[0262] Specific examples of each step

[0263] Step 1 example: A user types "iPhone 12 price comparison" into their smartphone and presses the search button.

[0264] Step 3 example: The server analyzes the devices the user has purchased in the past and the features preferred by similar users to create an optimal product list.

[0265] Step 6 example: The server collects reviews of the iPhone 12 from each source and combines all the text data into one.

[0266] Example of Step 8: The device displays a summary such as "good battery life," "great camera," and "high price."

[0267] Example of Step 12: Information such as "Site A has the lowest price" and "Site B offers point redemption" is displayed on the user's device.

[0268] In this way, by explaining in detail the specific processing and data flow at each step, we create a system that allows users to efficiently search for products, obtain the information they need, and make optimal decisions.

[0269] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0270] This invention is an AI system that helps users efficiently shop online by searching for related products, summarizing reviews, and comparing prices in a short amount of time. It also combines an emotion engine that recognizes the user's emotional state to provide a more personalized shopping experience. The following are specific examples of how this system can be implemented.

[0271] Product search and recommendations

[0272] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[0273] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[0274] The generated product list is adjusted by an emotion engine based on the user's emotional state. The emotion engine analyzes emotions from the user's search queries and past behavioral data, and can prioritize products with a relaxing effect to reduce stress, for example.

[0275] Collecting and summarizing reviews

[0276] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[0277] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[0278] The summary information of the generated reviews is also adjusted based on the user's emotional state. For example, if the user is in a positive emotional state, the summary is displayed as is. However, if the user is in a negative emotional state, the summary is adjusted to display more positive opinions to alleviate anxiety.

[0279] Price comparison and recommendations for recommended e-commerce sites

[0280] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[0281] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[0282] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[0283] Specific examples

[0284] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[0285] The server extracts models such as the iPhone SE or iPhone 11 that match the user's criteria from the database and generates an optimal product list based on the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data. For example, if the user is feeling stressed, the simple and easy-to-use iPhone SE will be displayed first.

[0286] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate their anxiety.

[0287] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[0288] In this way, users can efficiently search for products, check reviews, and find the best places to buy. The system also takes into account the user's emotional state, providing a more personalized shopping experience.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[0292] Step 2:

[0293] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[0294] Step 3:

[0295] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[0296] Step 4:

[0297] The server sends the generated optimal product list to the terminal.

[0298] Step 5:

[0299] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[0300] Step 6:

[0301] A user selects a product they are interested in and requests that reviews of that product be collected. The request is sent to the server.

[0302] Step 7:

[0303] The server receives a request from a user to collect reviews and collects reviews of the target product from multiple information sources, such as e-commerce sites, social media, and video sharing platforms.

[0304] Step 8:

[0305] The server analyzes the collected word-of-mouth information using natural language processing technology, extracts positive and negative opinions, specific advantages and disadvantages, etc., and generates a summary.

[0306] Step 9:

[0307] The server transmits the generated summary information of the reviews to the terminal.

[0308] Step 10:

[0309] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[0310] Step 11:

[0311] A user decides to purchase based on the reviews and requests information about the product's price and where to purchase it. The request is sent to the server.

[0312] Step 12:

[0313] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[0314] Step 13:

[0315] The server compares the price information it has acquired and also takes into account information on point redemptions and coupon applications to identify the best place to purchase.

[0316] Step 14:

[0317] The server transmits information about the optimal purchasing source identified to the terminal.

[0318] Step 15:

[0319] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[0320] Step 16:

[0321] The emotion engine analyzes users' search queries and reviews to recognize their emotional state, and adjusts the displayed product listings and reviews accordingly.

[0322] Step 17:

[0323] The server transmits the product list and word-of-mouth information adjusted by the emotion engine to the user terminal.

[0324] Example 2

[0325] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] When shopping online, it is difficult for users to efficiently search for products and find related products in a short time. Furthermore, it is also difficult to aggregate word-of-mouth information from multiple sources and present it in an easy-to-understand format, or to compare price information from multiple purchasing sites to find the best place to buy. In particular, there is a problem in that information is not provided personalized according to the user's emotional state, which hinders the user experience.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for extracting related products from a database based on a search query received from a terminal; means for generating an optimal product list by matching the extracted related products with the user's past history and similar user preference data; means for adjusting the generated optimal product list based on the user's emotional state; means for transmitting the adjusted optimal product list to the terminal; means for collecting product review information from multiple information sources; means for analyzing the collected review information and generating a summary; means for adjusting the summarized review information based on the user's emotional state; means for transmitting the adjusted review information to the terminal; means for acquiring and comparing product prices from multiple purchasing sites; and means for identifying the optimal purchasing site based on the price information and transmitting the price information to the terminal. This enables users to efficiently search for products, review review information in an easy-to-understand format, and compare prices to find the optimal purchasing source. Furthermore, by using an emotion engine, personalized information tailored to the user's emotional state can be provided.

[0328] A "terminal" is an electronic device that allows a user to input a search query or receive product information.

[0329] A "search query" is a string of characters including keywords and conditions that a user enters to search for a product.

[0330] A "server" is a central system that receives and processes user-entered search queries.

[0331] A "database" is an information management system that stores related product information and user history.

[0332] "Related products" are products extracted based on a user's search query.

[0333] "User's past history" is a record of the user's previous purchases and searches.

[0334] "Similar user preference data" is behavior data of other users who have similar preferences to the user.

[0335] The "optimal product list" is a product list generated based on the user's search query, past history, and preference data of similar users.

[0336] An "emotion engine" is software that analyzes the user's emotional state and adjusts information based on the results.

[0337] "Word-of-mouth information" refers to the opinions and ratings of other users about a particular product.

[0338] A "source" is a website or service from which review information is obtained.

[0339] "Natural language processing technology" is a technology for analyzing collected word-of-mouth information and generating summaries.

[0340] "Purchase Site" means a website or platform where products can be purchased online.

[0341] "Price information" is data regarding product prices obtained from a purchasing site.

[0342] "Point redemption" is a system in which points are earned as a reward for purchases.

[0343] "Coupon applied" means that a coupon that can be used as a discount is applied to the purchase price of the product.

[0344] The "optimal purchasing site" is the purchasing site that is judged to be the most advantageous for the user after a comprehensive comparison of price information, point redemption, and coupon application information.

[0345] This invention relates to an AI system that supports users in efficiently shopping online. Specifically, it is a system that quickly searches for related products, summarizes reviews, and compares prices, and by combining it with an emotion engine that recognizes the user's emotional state, it provides a more personalized shopping experience.

[0346] Hardware and Software Configuration

[0347] The server is the core of this system and performs the following main processes:

[0348] Search query analysis

[0349] Extracting related products from the database

[0350] Matching with user history and similar users' preference data

[0351] Adjusting product listings using an emotion engine

[0352] Collecting, analyzing, and summarizing reviews

[0353] Get and compare prices

[0354] Identifying the best purchasing site

[0355] The terminal is used by the user to input search queries and display product lists, reviews, and price comparison results sent from the server. The terminal can be a personal computer, smartphone, tablet, etc.

[0356] The emotion engine analyzes emotions from users' search queries and past behavioral data, and adjusts the information displayed based on the user's emotional state.

[0357] The database is used to store data such as product information, user purchase history, and word-of-mouth information.

[0358] Natural language processing technology is a technology for analyzing collected word-of-mouth information and generating summaries in a form that is easy for users to understand.

[0359] Example of a system

[0360] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." The server extracts models such as the iPhone SE and iPhone 11 that match the criteria from the database, and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data, and if the user is feeling stressed, for example, it will prioritize displaying the simple and easy-to-use iPhone SE.

[0361] A user specifically expresses interest in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social networking services, and video sharing platforms, and generates summaries such as "good battery life," "great camera performance," and "expensive price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate anxiety.

[0362] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from multiple purchasing sites, identifies the best purchase site, and provides it to the user. For example, it may present information such as "site A has the lowest price" or "site B offers 10% points back." Based on this information, the user can choose the most suitable purchase site and purchase the product.

[0363] Prompt Sentence Examples

[0364] An example of a prompt sentence to be input to the generative AI model is shown below.

[0365] "A user is shopping online and is looking for a smartphone. The price range is between 30,000 and 50,000 yen, and the brand is Apple. The user's past purchase history shows that they have owned smartphones. Generate a program that implements a system that recommends the best smartphone for the user, generates a summary of reviews, and suggests the best place to purchase."

[0366] This system allows users to efficiently search for products, check reviews in an easy-to-understand format, and compare prices to find the best deals. It also utilizes an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1:

[0369] A user enters a search query on a device. The user opens a shopping application and enters "Smartphone Price range: 30,000 to 50,000 yen Brand: Apple" into the search bar. When entering the query, the user specifies conditions such as price range, brand, category, etc. The device sends this entered search query to the server.

[0370] Step 2:

[0371] The server receives the search query and analyzes it. During analysis, the submitted search query is tokenized and important keywords and conditions are extracted. For example, information such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple" are extracted.

[0372] Step 3:

[0373] The server searches the database based on the analyzed search query and extracts related products. From the product data in the database, models such as "iPhone SE" and "iPhone 11" that match the criteria are extracted. The extracted results are temporarily saved on the server as a product list.

[0374] Step 4:

[0375] The server compares the user's past purchase history with the preference data of similar users to generate an optimal product list. By referencing the past purchase history and preference data of similar users, the most suitable products for the user are added to the list. For example, if a user has purchased many Apple products in the past, Apple products will be recommended first.

[0376] Step 5:

[0377] The emotion engine analyzes the user's emotional state and adjusts the generated optimal product list. The emotion engine analyzes the user's emotional state from the user's search query and past behavioral data, and adjusts the list to prioritize products with a relaxing effect if the user is feeling stressed, for example. The adjusted product list is saved on the server.

[0378] Step 6:

[0379] The server sends the adjusted optimal product list to the device. The device receives this list and displays it to the user. The user then checks the list of suggested products, such as "iPhone SE" and "iPhone 11," on the device.

[0380] Step 7:

[0381] A user is interested in a product and wants to check its reviews. The user requests reviews for a specific product (e.g., iPhone 11) on their device. The device then sends this request to the server.

[0382] Step 8:

[0383] The server collects word-of-mouth information about the product from multiple sources (e.g., e-commerce sites, social networking services, video sharing platforms, etc.), such as reviews on e-commerce sites, posts on social media, and comments on review videos on video sites.

[0384] Step 9:

[0385] The server analyzes the collected reviews using natural language processing technology and generates summaries, such as "battery life is good" and "camera performance is excellent." These summaries are temporarily stored in a database.

[0386] Step 10:

[0387] The emotion engine analyzes the user's emotional state and adjusts the generated summary accordingly. For example, if the user is unsure about a purchase, the summary will be adjusted to display more positive reviews. The adjusted review summary is then stored on the server.

[0388] Step 11:

[0389] The server sends the adjusted review summary to the device. The device receives the summary and displays it to the user. The user sees summaries such as "battery life is good," "camera performance is excellent," and "price is high."

[0390] Step 12:

[0391] A user is considering a purchase and wants to check price information. The user makes a price comparison request on their device. The device sends this request to the server.

[0392] Step 13:

[0393] The server obtains price information from multiple purchasing sites, such as Amazon, Rakuten, and other online retailers.

[0394] Step 14:

[0395] The server takes into account point redemption and coupon application information in addition to the price information it obtains, and performs a comprehensive comparison. For example, it compiles information such as "Rakuten is the cheapest" and "Amazon offers 10% point redemption."

[0396] Step 15:

[0397] The server identifies the best place to purchase and sends information about the best purchasing site to the terminal. The terminal receives this information and displays it to the user. The user selects the best place to purchase based on information such as "site A has the lowest price" or "site B offers 10% points back," and then purchases the product.

[0398] (Application example 2)

[0399] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0400] Current online shopping systems require users to search for products, check reviews, and compare prices, which is a time-consuming and tedious process. Furthermore, while users' emotional state can affect their shopping experience, personalization is not implemented to address this. As a result, users find it difficult to achieve a satisfactory shopping experience.

[0401] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0402] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting word-of-mouth information about products from multiple information sources, means for analyzing the collected word-of-mouth information and generating a summary, means for transmitting the summarized word-of-mouth information to the terminal, means for acquiring and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified word-of-mouth information to the terminal, and means for recognizing the user's emotional state and adjusting the display content of the generated product list and word-of-mouth information based on the user's emotional state, thereby enabling users to efficiently search, compare, and consider products and enjoy an optimal shopping experience personalized based on the user's emotional state.

[0403] A "terminal" is an information processing device that allows a user to communicate with a server via the Internet.

[0404] A "search query" is a keyword or phrase that a user enters to search for specific information.

[0405] "Related products" are products extracted from a database based on a search query.

[0406] A "database" is an information management system for storing product information and user history information.

[0407] "User's past history" is data related to products that the user has searched for or purchased in the past.

[0408] "Similar user preference data" is data analyzed based on the preferences and purchasing behavior of other users.

[0409] The "optimal product list" is a list of products generated based on the user's queries, past history, and preference data of similar users.

[0410] "Information sources" are sources of information such as e-commerce sites, social media, and video sharing platforms.

[0411] "Word-of-mouth information" is information that contains evaluations and opinions about a product.

[0412] The "summary" is a concise summary of the collected word-of-mouth information.

[0413] A "purchasing site" is an online shopping site where products are sold.

[0414] "Price information" refers to product prices and discount information at each purchasing site.

[0415] An "emotional state" indicates a temporary emotional state of a user.

[0416] "Personalization" means tailoring content to a user's individual preferences and emotional state.

[0417] A system embodying this invention includes a user terminal, a server, and an information source. The user terminal is an information processing device such as a smartphone or tablet on which an online shopping application is installed. The server includes a database and an analysis engine and is responsible for various data processing. The information source may be an e-commerce site, a social networking site, a video sharing platform, or the like.

[0418] 1. System Program Overview

[0419] The server performs the following operations:

[0420] It receives a search query sent from a user's device and extracts related products from a database.

[0421] The extracted related products are compared with the user's past history and the preference data of similar users to generate an optimal product list.

[0422] The generated optimum product list is sent to the user's terminal.

[0423] Word-of-mouth information about a product is collected from multiple information sources, and the collected word-of-mouth information is analyzed to generate a summary.

[0424] The summarized word-of-mouth information is sent to the user's terminal.

[0425] To obtain product prices from multiple purchasing sites and compare the obtained price information.

[0426] The optimal purchasing site is identified based on the price information and sent to the user's terminal.

[0427] The emotional state of the user is recognized, and the generated product list and the display content of the word-of-mouth information are adjusted based on the emotional state.

[0428] 2. Natural language explanation of program processing

[0429] The server receives a search query sent from the user's device and searches a database to extract relevant product information. The database contains detailed product information, price information, and customer reviews. The server compares the extracted products with the user's past purchase history and similar users' preference data to generate an optimal product list. This allows users to quickly find products that match their preferences.

[0430] Next, the server collects review information from multiple sources, including e-commerce sites, social media, and video sharing platforms. The collected review information is analyzed using natural language processing technology to generate a summary. This summary information is then processed into a form that is easy for users to understand and sent to their devices.

[0431] The server then obtains price information from multiple purchasing sites and performs data calculations to compare prices. The compared price information takes into account point redemption and coupon application information to identify the optimal purchasing site. This information is also sent to the user's device.

[0432] To recognize the user's emotional state, the server uses an emotion engine to analyze emotions based on the user's search query and past behavioral data, and prioritizes displaying products that will help the user relax or that will pique the user's interest.

[0433] Specific hardware and software used includes database management systems (e.g., MySQL), natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis engines (e.g., the transformers library), and web scraping tools for gathering pricing information (e.g., Beautiful Soup).

[0434] 3. Examples of specific examples and prompts

[0435] For example, if a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple," the server will extract models such as the iPhone SE or iPhone 11 that match the criteria from the database. The server will generate an optimal product list taking into account the user's past purchasing history and the preferences of similar users, and the emotion engine will analyze the user's emotions.

[0436] Example prompt sentence:

[0437] Analyze your emotional state towards the following products:

[0438] Product name: iPhone 11

[0439] User reviews: ['Good battery life', 'Excellent camera performance', 'High price']

[0440] Determine whether the user's emotional state is positive or negative.

[0441] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[0442] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0443] Step 1:

[0444] A user inputs a search query from a terminal. The terminal receives the query and sends it to the server. The search query includes conditions such as product category, price range, and brand.

[0445] Input: Search query including product category, price range, and brand.

[0446] Output: The search query is sent to the server.

[0447] Step 2:

[0448] The server searches a database containing detailed information about each product based on the received search query, and extracts related products that match the query.

[0449] Input: Search query.

[0450] Data processing: Matching product data in the database with queries.

[0451] Output: A list of related products.

[0452] Step 3:

[0453] The server compares the extracted related products with the user's past history and similar users' preference data to generate an optimal product list, and determines the priority of the products based on the preference data and history data.

[0454] Input: List of related products, user's past history, preference data of similar users.

[0455] Data processing: Prioritizing related product lists.

[0456] Output: A list of optimal products.

[0457] Step 4:

[0458] The server sends the generated optimal product list to the user's terminal, which displays the list on its screen.

[0459] Input: Optimal product listing.

[0460] Output: The best product list displayed on the device.

[0461] Step 5:

[0462] When a user becomes interested in a particular product, the user sends a request from the terminal to the server to check word-of-mouth information about the product.

[0463] Input: User review request.

[0464] Output: The review confirmation request is sent to the server.

[0465] Step 6:

[0466] The server collects word-of-mouth information from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) and analyzes the collected information using natural language processing technology to generate summaries.

[0467] Input: Review request.

[0468] Data processing: Collecting reviews from sources and generating summaries.

[0469] Output: A summary of the reviews.

[0470] Step 7:

[0471] The server transmits the summarized word-of-mouth information to the user's terminal, which displays the received summarized word-of-mouth information to the user.

[0472] Input: Abridged review information.

[0473] Output: Summary review information displayed on terminal.

[0474] Step 8:

[0475] When a user requests price information, the server retrieves product prices from multiple purchasing sites.

[0476] Input: User request for pricing information.

[0477] Output: The price information request is sent to the server.

[0478] Step 9:

[0479] The server compares the acquired price information and identifies the best place to purchase, taking into consideration point redemption and coupon application.

[0480] Input: Price information from each purchasing site.

[0481] Data processing: Comparing and analyzing price information.

[0482] Output: Best place to buy information.

[0483] Step 10:

[0484] The server transmits the identified optimal supplier information to the user's terminal, which displays this information to the user.

[0485] Input: Best Buy Information.

[0486] Output: Best place to buy information displayed on device.

[0487] Step 11:

[0488] The server uses an emotion engine to analyze search queries and past behavioral data to recognize the user's emotional state.

[0489] Input: User search queries, past behavior data.

[0490] Data Computing: Sentiment Analysis.

[0491] Output: The user's emotional state.

[0492] Step 12:

[0493] The server adjusts the displayed product list and review information based on the user's emotional state: if the user is feeling stressed, it prioritizes the display of products with a relaxing effect and positive reviews.

[0494] Input: User's emotional state, optimal product listings, summarized customer reviews.

[0495] Data processing: Adjusting display content based on emotional state.

[0496] Output: Product listings and reviews tailored based on emotional state.

[0497] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[0498] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0499] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0500] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0501] [Second embodiment]

[0502] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0503] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0504] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0505] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0506] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0507] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0508] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0509] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0510] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0511] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0512] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0513] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0514] This invention is an AI system that allows users to efficiently shop online, searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[0515] Product search and recommendations

[0516] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[0517] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[0518] By receiving this product list, users are presented with products that best meet their needs, enabling them to make efficient product selections.

[0519] Collecting and summarizing reviews

[0520] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[0521] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[0522] This allows users to efficiently understand the vast amount of word-of-mouth information and quickly grasp the advantages and disadvantages of a product.

[0523] Price comparison and recommendations for recommended e-commerce sites

[0524] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[0525] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[0526] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[0527] Specific examples

[0528] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[0529] The server extracts models such as iPhone SE and iPhone 11 that meet the criteria from the database and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users.

[0530] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price."

[0531] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[0532] In this way, users can quickly search for products, check reviews, and find the best place to buy them. This system will greatly contribute to users' efficient and satisfying online shopping.

[0533] The processing flow will be explained below.

[0534] Step 1:

[0535] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[0536] Step 2:

[0537] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[0538] Step 3:

[0539] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[0540] Step 4:

[0541] The server transmits the generated optimal product list to the user terminal.

[0542] Step 5:

[0543] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[0544] Step 6:

[0545] The user selects a product they are interested in on the screen and requests that reviews of that product be collected. The request is sent to the server.

[0546] Step 7:

[0547] The server receives a review collection request from the user and collects review information about the target product from multiple sources (e.g., e-commerce sites, social media, video sharing platforms, etc.).

[0548] Step 8:

[0549] The server analyzes the collected reviews using natural language processing technology, extracting positive and negative opinions, specific advantages and disadvantages, and generating summaries.

[0550] Step 9:

[0551] The server transmits the generated summary information of the word-of-mouth to the user terminal.

[0552] Step 10:

[0553] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[0554] Step 11:

[0555] A user requests price information and where to purchase a product they like. The request is sent to the server.

[0556] Step 12:

[0557] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[0558] Step 13:

[0559] The server compares the price information it has acquired and identifies the best place to purchase, taking into consideration point redemption and coupon application information.

[0560] Step 14:

[0561] The server transmits information about the optimal purchasing source identified to the user terminal.

[0562] Step 15:

[0563] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[0564] Example 1

[0565] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0566] In today's online shopping environment, users must access a vast amount of product information, reviews, and price information, making it difficult to select the best product. Furthermore, finding the lowest price across multiple purchasing sites requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently search for products, summarize reviews, and compare prices, allowing users to quickly find the best product and where to purchase it.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0568] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by matching the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting product review information from multiple information sources, means for analyzing the collected review information and generating a summary, means for transmitting the summarized review information to the terminal, means for obtaining and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified product to the terminal, means for performing natural language processing using a generative AI model to summarize the collected review information, and means for obtaining price information from purchasing sites based on a price comparison request received from the terminal and comparing the price information, including point redemption and coupon application information. This enables users to quickly search for products, check reviews, and find the optimal purchasing site.

[0569] A "terminal" is a device that a user operates and inputs information, and includes a personal computer, a smartphone, a tablet, and the like.

[0570] A "server" is a computer system that processes data, accesses databases, and collects information, and communicates with terminals via a network.

[0571] A "search query" is a condition or keyword that a user inputs through a terminal, and includes input information for a product search.

[0572] A "database" is a system for efficiently storing and managing product information and user information, and includes relational databases and NoSQL databases.

[0573] "Related products" are products that match or are related to the product a user is looking for via a search query.

[0574] "Past history" refers to a record of the user's previous purchases and browsing, and is information used to understand the user's preferences.

[0575] "Similar user preference data" refers to data of other users who have similar preferences and purchasing patterns, and is used to generate an optimal product list for a specific user.

[0576] A "product list" is a list of optimal products generated based on the user's search criteria and past history data.

[0577] "Review information" refers to ratings and opinions provided by product purchasers or users, and is primarily collected from e-commerce sites, social networking services, and video-sharing platforms.

[0578] "Analysis" is the act of processing collected data to extract useful information, and includes, among other things, natural language processing and data mining techniques.

[0579] A "summary" is information that extracts the main opinions and evaluations from a vast amount of review information and summarizes them concisely.

[0580] A "purchasing site" is a website that allows users to purchase products online, and includes multiple platforms.

[0581] "Price Information" is a detailed description of the price offered at each purchasing site for a particular product.

[0582] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and uses that knowledge to generate or analyze new data.

[0583] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0584] "Point redemption" is a benefit that allows you to use points earned at the time of purchase under certain conditions at a later date.

[0585] A "coupon" is a code or ticket that provides a discount or benefit that can be applied when purchasing a product.

[0586] An "e-commerce site" is a website for buying and selling goods online.

[0587] A "social networking service" is a web service that allows users to interact with each other, share information, and post reviews.

[0588] A "video sharing platform" is a web service that allows users to post videos and allow other users to view them.

[0589] This invention is an AI system that helps users shop online efficiently by searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[0590] Product search and recommendations

[0591] The user opens a product search screen using a device and enters a search query, such as "price range: 30,000 to 50,000 yen, brand: Apple, category: smartphone."

[0592] The device sends the search query entered by the user in JSON format to the server. The server receives this query and uses an SQL query to extract products that meet the criteria from a database such as MySQL. A query like "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='smartphone'" is used.

[0593] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate an optimal product list. The generated product list is sent to the terminal in JSON format.

[0594] The terminal parses the received product list and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[0595] Collecting and summarizing reviews

[0596] If a user wants more information about a specific product, they can request a review of that product, for example, by clicking the Details button for the "iPhone 11."

[0597] The device sends this request in JSON format to the server, which then uses Python scripts to scrape reviews from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium.

[0598] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates summaries. It extracts key opinions and ratings and summarizes them as "good battery life," "great camera performance," and "expensive price."

[0599] The device then displays the summary information to the user, with each summary point displayed as a bullet point and an associated star rating.

[0600] Price comparison and recommendations for recommended e-commerce sites

[0601] When a user is considering purchasing a product, they request a price comparison, for example, by clicking the "Buy iPhone 11" button.

[0602] The terminal sends this request in JSON format to the server, which then uses APIs to collect price information from multiple purchasing sites. For example, it uses the API of an e-commerce site to obtain price information.

[0603] The server compares and analyzes the acquired price information, taking into account point redemption and coupon application information, and uses the Python Pandas library to format the data and identify the best place to purchase.

[0604] Finally, the device displays the optimal purchasing information received from the server to the user, providing specific price information and purchasing benefits, such as "Rakuten is the cheapest, and another site offers 10% points back."

[0605] Specific examples

[0606] For example, if a user searches for "price range: ¥30,000-¥50,000, brand: Apple, category: smartphone," the server extracts products that match the criteria, such as the iPhone SE or iPhone 11, from the MySQL database and displays them on the device. When a user is interested in the iPhone 11 and sends a request to check reviews, the server collects review information using Python scripts and scraping tools, and uses a natural language processing model to generate summaries such as "good battery life" and "great camera performance." Finally, when a user requests a price comparison for the iPhone 11, the server retrieves price information from the APIs of multiple purchasing sites, analyzes it using PANDAS, and identifies the best place to purchase, allowing the user to purchase the product conveniently and quickly.

[0607] This allows users to quickly search for products, check reviews, and find the best place to buy, making this system a great contribution to making online shopping more efficient and satisfying for users.

[0608] Prompt Sentence Examples

[0609] As a concrete example of how this system can be implemented, consider the following prompt:

[0610] "Search for Apple smartphones in the price range of 30,000 to 50,000 yen."

[0611] "Collect iPhone 11 reviews."

[0612] "Compare iPhone 11 prices across multiple sites."

[0613] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0614] Step 1:

[0615] The user opens a product search screen on their device and enters a search query. For example, they enter conditions such as "Price range: 30,000 to 50,000 yen, Brand: Apple, Category: Smartphone." This becomes the input data.

[0616] Step 2:

[0617] The device sends the search query entered by the user to the server in JSON format. The data received by the server contains the search conditions.

[0618] Step 3:

[0619] The server analyzes this query and extracts products that match the conditions from a database such as MySQL using an SQL query. Specifically, it executes a query such as "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='Smartphone'". The related products retrieved from the database are output.

[0620] Step 4:

[0621] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate the optimal product list. As a result of the comparison, the optimal product list for the user is output in JSON format.

[0622] Step 5:

[0623] The server sends the generated optimal product list to the terminal as an API response. The data received by the terminal is the matched product list.

[0624] Step 6:

[0625] The terminal analyzes the product list received from the server and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[0626] Step 7:

[0627] If a user wants more information about a specific product, they can request a review of that product, for example by clicking the "iPhone 11" details button. This becomes new input data.

[0628] Step 8:

[0629] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[0630] Step 9:

[0631] The server uses Python scripts to collect review information from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium, and outputs the collected review information.

[0632] Step 10:

[0633] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates a summary, which extracts key opinions and ratings.

[0634] Step 11:

[0635] The server sends the generated review summary in JSON format to the device, and the data received by the device is the summarized review information.

[0636] Step 12:

[0637] The terminal displays the summary information received from the server to the user, with each summary point displayed as a bullet point and an associated star rating.

[0638] Step 13:

[0639] When a user is considering purchasing a product, they request a price comparison. For example, they click the "Buy iPhone 11" button. This becomes new input data.

[0640] Step 14:

[0641] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[0642] Step 15:

[0643] The server uses an API to collect price information from multiple purchasing sites. For example, it obtains price information using the API of an e-commerce site. The collected price information is output.

[0644] Step 16:

[0645] The server compares and analyzes the collected price information, taking into account point redemption and coupon application information. It uses the Python Pandas library to format the data and identify the optimal purchasing location. The data is then output as optimal purchasing location information.

[0646] Step 17:

[0647] The server sends the optimal purchasing information to the device in JSON format. The data received by the device is the optimal purchasing information.

[0648] Step 18:

[0649] The device displays the optimal purchasing information received from the server in an easy-to-read format for the user. For example, it shows specific price information and purchasing benefits, such as "Rakuten is the cheapest, and other sites offer 10% points back."

[0650] This allows users to quickly search for products, check reviews, and find the best place to buy.

[0651] (Application example 1)

[0652] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0653] When shopping online, users must spend a lot of time and effort selecting the best product from a vast amount of information. Furthermore, checking reviews and comparing prices is typically done manually, which is inefficient. The lack of automated processes for summarizing useful reviews and identifying optimal purchasing sites limits the user experience. Furthermore, while smartphone applications would improve user convenience, there are few such systems available. There is a need for an efficient and integrated system to solve these issues.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0655] In this invention, the server

[0656] means for extracting related products from a database based on a search query received from the terminal;

[0657] A means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users;

[0658] means for transmitting the generated optimal product list to a terminal;

[0659] A means for collecting evaluation information about products from multiple sources;

[0660] means for analyzing the collected evaluation information and generating a summary;

[0661] means for transmitting the summarized evaluation information to a terminal;

[0662] A means to obtain and compare product prices from multiple purchasing sites,

[0663] means for identifying an optimal purchasing site based on the price information and transmitting the information to the terminal;

[0664] means functioning as an application installed on a smartphone;

[0665] A means to recommend the best products online based on users' purchasing queries,

[0666] natural language processing means for acquiring and analyzing the collected evaluation information from multiple information sources;

[0667] A means to identify the best place to purchase, taking into account price information and point redemption and coupon application information;

[0668] Includes:

[0669] This allows users to efficiently search for products, check review summaries, compare prices, and instantly find the best purchase, significantly improving the convenience and user experience of online shopping.

[0670] definition statement

[0671] A "terminal" is a device that a user directly operates, such as to input a search query or receive a product list.

[0672] A "search query" is a request that includes keywords and conditions that a user enters when searching for a product.

[0673] A "database" is a collection of information that stores product information, user history, and the like.

[0674] "Related Products" refers to products extracted from a database based on a user's search query.

[0675] "User's past history" refers to data relating to purchases and searches that the user has previously made.

[0676] "Preference data" is information relating to a user's preferences and tendencies.

[0677] The "generated optimal product list" is a product list generated by the server based on the search query and the user's history and preference data.

[0678] "Evaluation information" refers to feedback information such as word-of-mouth and reviews about a product.

[0679] "Analysis" is the process of using collected information to extract useful data.

[0680] A "summary" is a concise summary of the collected evaluation information.

[0681] A "purchasing site" is a website that sells products online.

[0682] "Price information" refers to the sales price offered by each purchasing site for a particular product.

[0683] An "application installed on a smartphone" is a program that runs on a smartphone and realizes the functions of the present invention.

[0684] "Natural language processing means" is a technology that analyzes language data, extracts meaning, and processes information.

[0685] "Point redemption" is a system in which a certain number of points are returned for each purchase.

[0686] "Coupon application" refers to the use of a code or voucher to receive a discount or special offer when purchasing a product.

[0687] MODE FOR CARRYING OUT THE INVENTION

[0688] The present invention provides a system for making online shopping more efficient. A user accesses the system using a terminal and inputs a search query. Specific embodiments of the system are described below.

[0689] Product search and recommendations

[0690] When a user enters a search query from a terminal, the query is sent to a server. The server extracts related products from a database based on the received search query. This database contains not only product information but also the user's past purchase history and preference data. The server uses this data to generate a product list that is optimal for the user. The generated product list is sent to the terminal, where the user can refer to it.

[0691] Collecting and summarizing reviews

[0692] When a user is interested in a particular product, they send a request to the server to check the product's reviews. The server collects reviews from multiple sources, including e-commerce sites, social networking services, and video sharing platforms. The collected reviews are analyzed and summarized using natural language processing technology. The summarized reviews are then sent to the user's device, where they can be viewed.

[0693] Price comparison and shopping site recommendations

[0694] When a user is considering purchasing a product, it is important to know the best place to purchase it, including price information and special offer information (point redemption and coupon application information). When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information. Based on this information, the server identifies the best place to purchase and sends that information to the terminal. The user can purchase the product under the best conditions.

[0695] Smartphone application

[0696] This system can also be implemented as a smartphone application. This application recommends optimal products online based on a purchasing query entered by the user. Furthermore, it obtains collected evaluation information from multiple sources and summarizes it using natural language processing technology (e.g., Gensim). It also includes a function to identify the optimal purchasing destination by taking into account price information and special offer information.

[0697] Specific examples

[0698] For example, consider the case where a user enters the query "smartphone" to perform a search. The server extracts smartphones that match the search criteria from the database and generates an optimal product list by taking into account past purchase history and preferences. When a user becomes interested in a particular smartphone and sends a request to check its reviews, the server collects reviews from e-commerce sites and social networking services and summarizes them using natural language processing technology. Finally, the server recommends the optimal purchasing site by taking into account price comparisons and special offers, allowing the user to purchase the product at the best price.

[0699] Prompt Sentence Examples

[0700] When a user searches for "iPhone 12 price comparison":

[0701] 1. Get product list from API

[0702] 2. Collecting ratings from multiple sources using libraries such as BeautifulSoup

[0703] 3. Summarize the evaluation information using the summarize function of Gensim

[0704] 4. Obtain and compare price information from multiple purchasing sites

[0705] This allows users to efficiently search for products, check summary reviews, compare prices, and instantly find the best place to buy, significantly improving the convenience of online shopping.

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Program processing steps

[0708] Step 1:

[0709] The user enters a search query from their device. The search query includes conditions such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." This becomes a request to the server.

[0710] Step 2:

[0711] The server extracts related products from the database based on the received search query. The search query is used as input (conditions) and a database search is performed based on this. The output is a list of related products.

[0712] Step 3:

[0713] The server compares the extracted related products with the user's past history and similar users' preference data. The server uses the user history data and preference data as inputs and integrates them to output an optimal product list.

[0714] Step 4:

[0715] The server sends the generated optimal product list to the user's terminal. The terminal displays the received product list. The input is the optimal product list, and the output is the list displayed on the terminal.

[0716] Step 5:

[0717] When a user wants to check the details of a particular product, the user sends a request for reviews of that product. The terminal sends the request to the server.

[0718] Step 6:

[0719] The server collects reviews of the product from multiple sources. The input is the request and the source URL, and the output is the collected reviews. Here, we use a library such as BeautifulSoup to extract text from the web page.

[0720] Step 7:

[0721] The server analyzes the collected reviews using natural language processing technology and generates summaries. Specifically, it uses the summarize function from the Gensim library. The input is the text data of the reviews, and the output is the summarized text.

[0722] Step 8:

[0723] The server sends the summarized review information to the user's terminal, and the terminal displays the summarized information. The input is the summarized review information, and the output is the information displayed on the terminal.

[0724] Step 9:

[0725] The user submits a request for a price comparison of a product. The device sends the request to the server.

[0726] Step 10:

[0727] The server retrieves and compares product prices from multiple purchasing sites. The input is the request and the purchasing site URL, and the output is the price information retrieved from each site.

[0728] Step 11:

[0729] The server identifies the optimal purchasing location by adding information on rewards and coupons to the acquired price information. The input is price information and additional information, and the output is the optimal purchasing location.

[0730] Step 12:

[0731] The server sends the optimal purchasing information to the user's terminal. The terminal displays the received information. The input is the optimal purchasing information, and the output is the information displayed on the terminal.

[0732] Specific examples of each step

[0733] Step 1 example: A user types "iPhone 12 price comparison" into their smartphone and presses the search button.

[0734] Step 3 example: The server analyzes the devices the user has purchased in the past and the features preferred by similar users to create an optimal product list.

[0735] Step 6 example: The server collects reviews of the iPhone 12 from each source and combines all the text data into one.

[0736] Example of Step 8: The device displays a summary such as "good battery life," "great camera," and "high price."

[0737] Example of Step 12: Information such as "Site A has the lowest price" and "Site B offers point redemption" is displayed on the user's device.

[0738] In this way, by explaining in detail the specific processing and data flow at each step, we create a system that allows users to efficiently search for products, obtain the information they need, and make optimal decisions.

[0739] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0740] This invention is an AI system that helps users efficiently shop online by searching for related products, summarizing reviews, and comparing prices in a short amount of time. It also combines an emotion engine that recognizes the user's emotional state to provide a more personalized shopping experience. The following are specific examples of how this system can be implemented.

[0741] Product search and recommendations

[0742] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[0743] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[0744] The generated product list is adjusted by an emotion engine based on the user's emotional state. The emotion engine analyzes emotions from the user's search queries and past behavioral data, and can prioritize products with a relaxing effect to reduce stress, for example.

[0745] Collecting and summarizing reviews

[0746] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[0747] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[0748] The summary information of the generated reviews is also adjusted based on the user's emotional state. For example, if the user is in a positive emotional state, the summary is displayed as is. However, if the user is in a negative emotional state, the summary is adjusted to display more positive opinions to alleviate anxiety.

[0749] Price comparison and recommendations for recommended e-commerce sites

[0750] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[0751] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[0752] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[0753] Specific examples

[0754] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[0755] The server extracts models such as the iPhone SE or iPhone 11 that match the user's criteria from the database and generates an optimal product list based on the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data. For example, if the user is feeling stressed, the simple and easy-to-use iPhone SE will be displayed first.

[0756] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate their anxiety.

[0757] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[0758] In this way, users can efficiently search for products, check reviews, and find the best places to buy. The system also takes into account the user's emotional state, providing a more personalized shopping experience.

[0759] The processing flow will be explained below.

[0760] Step 1:

[0761] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[0762] Step 2:

[0763] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[0764] Step 3:

[0765] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[0766] Step 4:

[0767] The server sends the generated optimal product list to the terminal.

[0768] Step 5:

[0769] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[0770] Step 6:

[0771] A user selects a product they are interested in and requests that reviews of that product be collected. The request is sent to the server.

[0772] Step 7:

[0773] The server receives a request from a user to collect reviews and collects reviews of the target product from multiple information sources, such as e-commerce sites, social media, and video sharing platforms.

[0774] Step 8:

[0775] The server analyzes the collected word-of-mouth information using natural language processing technology, extracts positive and negative opinions, specific advantages and disadvantages, etc., and generates a summary.

[0776] Step 9:

[0777] The server transmits the generated summary information of the reviews to the terminal.

[0778] Step 10:

[0779] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[0780] Step 11:

[0781] A user decides to purchase based on the reviews and requests information about the product's price and where to purchase it. The request is sent to the server.

[0782] Step 12:

[0783] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[0784] Step 13:

[0785] The server compares the price information it has acquired and also takes into account information on point redemptions and coupon applications to identify the best place to purchase.

[0786] Step 14:

[0787] The server transmits information about the optimal purchasing source identified to the terminal.

[0788] Step 15:

[0789] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[0790] Step 16:

[0791] The emotion engine analyzes users' search queries and reviews to recognize their emotional state, and adjusts the displayed product listings and reviews accordingly.

[0792] Step 17:

[0793] The server transmits the product list and word-of-mouth information adjusted by the emotion engine to the user terminal.

[0794] Example 2

[0795] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0796] When shopping online, it is difficult for users to efficiently search for products and find related products in a short time. Furthermore, it is also difficult to aggregate word-of-mouth information from multiple sources and present it in an easy-to-understand format, or to compare price information from multiple purchasing sites to find the best place to buy. In particular, there is a problem in that information is not provided personalized according to the user's emotional state, which hinders the user experience.

[0797] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for extracting related products from a database based on a search query received from a terminal; means for generating an optimal product list by matching the extracted related products with the user's past history and similar user preference data; means for adjusting the generated optimal product list based on the user's emotional state; means for transmitting the adjusted optimal product list to the terminal; means for collecting product review information from multiple information sources; means for analyzing the collected review information and generating a summary; means for adjusting the summarized review information based on the user's emotional state; means for transmitting the adjusted review information to the terminal; means for acquiring and comparing product prices from multiple purchasing sites; and means for identifying the optimal purchasing site based on the price information and transmitting the price information to the terminal. This enables users to efficiently search for products, review review information in an easy-to-understand format, and compare prices to find the optimal purchasing source. Furthermore, by using an emotion engine, personalized information tailored to the user's emotional state can be provided.

[0798] A "terminal" is an electronic device that allows a user to input a search query or receive product information.

[0799] A "search query" is a string of characters including keywords and conditions that a user enters to search for a product.

[0800] A "server" is a central system that receives and processes user-entered search queries.

[0801] A "database" is an information management system that stores related product information and user history.

[0802] "Related products" are products extracted based on a user's search query.

[0803] "User's past history" is a record of the user's previous purchases and searches.

[0804] "Similar user preference data" is behavior data of other users who have similar preferences to the user.

[0805] The "optimal product list" is a product list generated based on the user's search query, past history, and preference data of similar users.

[0806] An "emotion engine" is software that analyzes the user's emotional state and adjusts information based on the results.

[0807] "Word-of-mouth information" refers to the opinions and ratings of other users about a particular product.

[0808] A "source" is a website or service from which review information is obtained.

[0809] "Natural language processing technology" is a technology for analyzing collected word-of-mouth information and generating summaries.

[0810] "Purchase Site" means a website or platform where products can be purchased online.

[0811] "Price information" is data regarding product prices obtained from a purchasing site.

[0812] "Point redemption" is a system in which points are earned as a reward for purchases.

[0813] "Coupon applied" means that a coupon that can be used as a discount is applied to the purchase price of the product.

[0814] The "optimal purchasing site" is the purchasing site that is judged to be the most advantageous for the user after a comprehensive comparison of price information, point redemption, and coupon application information.

[0815] This invention relates to an AI system that supports users in efficiently shopping online. Specifically, it is a system that quickly searches for related products, summarizes reviews, and compares prices, and by combining it with an emotion engine that recognizes the user's emotional state, it provides a more personalized shopping experience.

[0816] Hardware and Software Configuration

[0817] The server is the core of this system and performs the following main processes:

[0818] Search query analysis

[0819] Extracting related products from the database

[0820] Matching with user history and similar users' preference data

[0821] Adjusting product listings using an emotion engine

[0822] Collecting, analyzing, and summarizing reviews

[0823] Get and compare prices

[0824] Identifying the best purchasing site

[0825] The terminal is used by the user to input search queries and display product lists, reviews, and price comparison results sent from the server. The terminal can be a personal computer, smartphone, tablet, etc.

[0826] The emotion engine analyzes emotions from users' search queries and past behavioral data, and adjusts the information displayed based on the user's emotional state.

[0827] The database is used to store data such as product information, user purchase history, and word-of-mouth information.

[0828] Natural language processing technology is a technology for analyzing collected word-of-mouth information and generating summaries in a form that is easy for users to understand.

[0829] Example of a system

[0830] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." The server extracts models such as the iPhone SE and iPhone 11 that match the criteria from the database, and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data, and if the user is feeling stressed, for example, it will prioritize displaying the simple and easy-to-use iPhone SE.

[0831] A user specifically expresses interest in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social networking services, and video sharing platforms, and generates summaries such as "good battery life," "great camera performance," and "expensive price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate anxiety.

[0832] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from multiple purchasing sites, identifies the best purchase site, and provides it to the user. For example, it may present information such as "site A has the lowest price" or "site B offers 10% points back." Based on this information, the user can choose the most suitable purchase site and purchase the product.

[0833] Prompt Sentence Examples

[0834] An example of a prompt sentence to be input to the generative AI model is shown below.

[0835] "A user is shopping online and is looking for a smartphone. The price range is between 30,000 and 50,000 yen, and the brand is Apple. The user's past purchase history shows that they have owned smartphones. Generate a program that implements a system that recommends the best smartphone for the user, generates a summary of reviews, and suggests the best place to purchase."

[0836] This system allows users to efficiently search for products, check reviews in an easy-to-understand format, and compare prices to find the best deals. It also utilizes an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[0837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0838] Step 1:

[0839] A user enters a search query on a device. The user opens a shopping application and enters "Smartphone Price range: 30,000 to 50,000 yen Brand: Apple" into the search bar. When entering the query, the user specifies conditions such as price range, brand, category, etc. The device sends this entered search query to the server.

[0840] Step 2:

[0841] The server receives the search query and analyzes it. During analysis, the submitted search query is tokenized and important keywords and conditions are extracted. For example, information such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple" are extracted.

[0842] Step 3:

[0843] The server searches the database based on the analyzed search query and extracts related products. From the product data in the database, models such as "iPhone SE" and "iPhone 11" that match the criteria are extracted. The extracted results are temporarily saved on the server as a product list.

[0844] Step 4:

[0845] The server compares the user's past purchase history with the preference data of similar users to generate an optimal product list. By referencing the past purchase history and preference data of similar users, the most suitable products for the user are added to the list. For example, if a user has purchased many Apple products in the past, Apple products will be recommended first.

[0846] Step 5:

[0847] The emotion engine analyzes the user's emotional state and adjusts the generated optimal product list. The emotion engine analyzes the user's emotional state from the user's search query and past behavioral data, and adjusts the list to prioritize products with a relaxing effect if the user is feeling stressed, for example. The adjusted product list is saved on the server.

[0848] Step 6:

[0849] The server sends the adjusted optimal product list to the device. The device receives this list and displays it to the user. The user then checks the list of suggested products, such as "iPhone SE" and "iPhone 11," on the device.

[0850] Step 7:

[0851] A user is interested in a product and wants to check its reviews. The user requests reviews for a specific product (e.g., iPhone 11) on their device. The device then sends this request to the server.

[0852] Step 8:

[0853] The server collects word-of-mouth information about the product from multiple sources (e.g., e-commerce sites, social networking services, video sharing platforms, etc.), such as reviews on e-commerce sites, posts on social media, and comments on review videos on video sites.

[0854] Step 9:

[0855] The server analyzes the collected reviews using natural language processing technology and generates summaries, such as "battery life is good" and "camera performance is excellent." These summaries are temporarily stored in a database.

[0856] Step 10:

[0857] The emotion engine analyzes the user's emotional state and adjusts the generated summary accordingly. For example, if the user is unsure about a purchase, the summary will be adjusted to display more positive reviews. The adjusted review summary is then stored on the server.

[0858] Step 11:

[0859] The server sends the adjusted review summary to the device. The device receives the summary and displays it to the user. The user sees summaries such as "battery life is good," "camera performance is excellent," and "price is high."

[0860] Step 12:

[0861] A user is considering a purchase and wants to check price information. The user makes a price comparison request on their device. The device sends this request to the server.

[0862] Step 13:

[0863] The server obtains price information from multiple purchasing sites, such as Amazon, Rakuten, and other online retailers.

[0864] Step 14:

[0865] The server takes into account point redemption and coupon application information in addition to the price information it obtains, and performs a comprehensive comparison. For example, it compiles information such as "Rakuten is the cheapest" and "Amazon offers 10% point redemption."

[0866] Step 15:

[0867] The server identifies the best place to purchase and sends information about the best purchasing site to the terminal. The terminal receives this information and displays it to the user. The user selects the best place to purchase based on information such as "site A has the lowest price" or "site B offers 10% points back," and then purchases the product.

[0868] (Application example 2)

[0869] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0870] Current online shopping systems require users to search for products, check reviews, and compare prices, which is a time-consuming and tedious process. Furthermore, while users' emotional state can affect their shopping experience, personalization is not implemented to address this. As a result, users find it difficult to achieve a satisfactory shopping experience.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0872] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting word-of-mouth information about products from multiple information sources, means for analyzing the collected word-of-mouth information and generating a summary, means for transmitting the summarized word-of-mouth information to the terminal, means for acquiring and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified word-of-mouth information to the terminal, and means for recognizing the user's emotional state and adjusting the display content of the generated product list and word-of-mouth information based on the user's emotional state, thereby enabling users to efficiently search, compare, and consider products and enjoy an optimal shopping experience personalized based on the user's emotional state.

[0873] A "terminal" is an information processing device that allows a user to communicate with a server via the Internet.

[0874] A "search query" is a keyword or phrase that a user enters to search for specific information.

[0875] "Related products" are products extracted from a database based on a search query.

[0876] A "database" is an information management system for storing product information and user history information.

[0877] "User's past history" is data related to products that the user has searched for or purchased in the past.

[0878] "Similar user preference data" is data analyzed based on the preferences and purchasing behavior of other users.

[0879] The "optimal product list" is a list of products generated based on the user's queries, past history, and preference data of similar users.

[0880] "Information sources" are sources of information such as e-commerce sites, social media, and video sharing platforms.

[0881] "Word-of-mouth information" is information that contains evaluations and opinions about a product.

[0882] The "summary" is a concise summary of the collected word-of-mouth information.

[0883] A "purchasing site" is an online shopping site where products are sold.

[0884] "Price information" refers to product prices and discount information at each purchasing site.

[0885] An "emotional state" indicates a temporary emotional state of a user.

[0886] "Personalization" means tailoring content to a user's individual preferences and emotional state.

[0887] A system embodying this invention includes a user terminal, a server, and an information source. The user terminal is an information processing device such as a smartphone or tablet on which an online shopping application is installed. The server includes a database and an analysis engine and is responsible for various data processing. The information source may be an e-commerce site, a social networking site, a video sharing platform, or the like.

[0888] 1. System Program Overview

[0889] The server performs the following operations:

[0890] It receives a search query sent from a user's device and extracts related products from a database.

[0891] The extracted related products are compared with the user's past history and the preference data of similar users to generate an optimal product list.

[0892] The generated optimum product list is sent to the user's terminal.

[0893] Word-of-mouth information about a product is collected from multiple information sources, and the collected word-of-mouth information is analyzed to generate a summary.

[0894] The summarized word-of-mouth information is sent to the user's terminal.

[0895] To obtain product prices from multiple purchasing sites and compare the obtained price information.

[0896] The optimal purchasing site is identified based on the price information and sent to the user's terminal.

[0897] The emotional state of the user is recognized, and the generated product list and the display content of the word-of-mouth information are adjusted based on the emotional state.

[0898] 2. Natural language explanation of program processing

[0899] The server receives a search query sent from the user's device and searches a database to extract relevant product information. The database contains detailed product information, price information, and customer reviews. The server compares the extracted products with the user's past purchase history and similar users' preference data to generate an optimal product list. This allows users to quickly find products that match their preferences.

[0900] Next, the server collects review information from multiple sources, including e-commerce sites, social media, and video sharing platforms. The collected review information is analyzed using natural language processing technology to generate a summary. This summary information is then processed into a form that is easy for users to understand and sent to their devices.

[0901] The server then obtains price information from multiple purchasing sites and performs data calculations to compare prices. The compared price information takes into account point redemption and coupon application information to identify the optimal purchasing site. This information is also sent to the user's device.

[0902] To recognize the user's emotional state, the server uses an emotion engine to analyze emotions based on the user's search query and past behavioral data, and prioritizes displaying products that will help the user relax or that will pique the user's interest.

[0903] Specific hardware and software used includes database management systems (e.g., MySQL), natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis engines (e.g., the transformers library), and web scraping tools for gathering pricing information (e.g., Beautiful Soup).

[0904] 3. Examples of specific examples and prompts

[0905] For example, if a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple," the server will extract models such as the iPhone SE or iPhone 11 that match the criteria from the database. The server will generate an optimal product list taking into account the user's past purchasing history and the preferences of similar users, and the emotion engine will analyze the user's emotions.

[0906] Example prompt sentence:

[0907] Analyze your emotional state towards the following products:

[0908] Product name: iPhone 11

[0909] User reviews: ['Good battery life', 'Excellent camera performance', 'High price']

[0910] Determine whether the user's emotional state is positive or negative.

[0911] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[0912] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0913] Step 1:

[0914] A user inputs a search query from a terminal. The terminal receives the query and sends it to the server. The search query includes conditions such as product category, price range, and brand.

[0915] Input: Search query including product category, price range, and brand.

[0916] Output: The search query is sent to the server.

[0917] Step 2:

[0918] The server searches a database containing detailed information about each product based on the received search query, and extracts related products that match the query.

[0919] Input: Search query.

[0920] Data processing: Matching product data in the database with queries.

[0921] Output: A list of related products.

[0922] Step 3:

[0923] The server compares the extracted related products with the user's past history and similar users' preference data to generate an optimal product list, and determines the priority of the products based on the preference data and history data.

[0924] Input: List of related products, user's past history, preference data of similar users.

[0925] Data processing: Prioritizing related product lists.

[0926] Output: A list of optimal products.

[0927] Step 4:

[0928] The server sends the generated optimal product list to the user's terminal, which displays the list on its screen.

[0929] Input: Optimal product listing.

[0930] Output: The best product list displayed on the device.

[0931] Step 5:

[0932] When a user becomes interested in a particular product, the user sends a request from the terminal to the server to check word-of-mouth information about the product.

[0933] Input: User review request.

[0934] Output: The review confirmation request is sent to the server.

[0935] Step 6:

[0936] The server collects word-of-mouth information from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) and analyzes the collected information using natural language processing technology to generate summaries.

[0937] Input: Review request.

[0938] Data processing: Collecting reviews from sources and generating summaries.

[0939] Output: A summary of the reviews.

[0940] Step 7:

[0941] The server transmits the summarized word-of-mouth information to the user's terminal, which displays the received summarized word-of-mouth information to the user.

[0942] Input: Abridged review information.

[0943] Output: Summary review information displayed on terminal.

[0944] Step 8:

[0945] When a user requests price information, the server retrieves product prices from multiple purchasing sites.

[0946] Input: User request for pricing information.

[0947] Output: The price information request is sent to the server.

[0948] Step 9:

[0949] The server compares the acquired price information and identifies the best place to purchase, taking into consideration point redemption and coupon application.

[0950] Input: Price information from each purchasing site.

[0951] Data processing: Comparing and analyzing price information.

[0952] Output: Best place to buy information.

[0953] Step 10:

[0954] The server transmits the identified optimal supplier information to the user's terminal, which displays this information to the user.

[0955] Input: Best Buy Information.

[0956] Output: Best place to buy information displayed on device.

[0957] Step 11:

[0958] The server uses an emotion engine to analyze search queries and past behavioral data to recognize the user's emotional state.

[0959] Input: User search queries, past behavior data.

[0960] Data Computing: Sentiment Analysis.

[0961] Output: The user's emotional state.

[0962] Step 12:

[0963] The server adjusts the displayed product list and review information based on the user's emotional state: if the user is feeling stressed, it prioritizes the display of products with a relaxing effect and positive reviews.

[0964] Input: User's emotional state, optimal product listings, summarized customer reviews.

[0965] Data processing: Adjusting display content based on emotional state.

[0966] Output: Product listings and reviews tailored based on emotional state.

[0967] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[0968] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0969] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0970] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0971] [Third embodiment]

[0972] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0973] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0974] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0975] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0976] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0977] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0978] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0979] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0980] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0981] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0982] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0983] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0984] This invention is an AI system that allows users to efficiently shop online, searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[0985] Product search and recommendations

[0986] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[0987] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[0988] By receiving this product list, users are presented with products that best meet their needs, enabling them to make efficient product selections.

[0989] Collecting and summarizing reviews

[0990] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[0991] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[0992] This allows users to efficiently understand the vast amount of word-of-mouth information and quickly grasp the advantages and disadvantages of a product.

[0993] Price comparison and recommendations for recommended e-commerce sites

[0994] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[0995] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[0996] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[0997] Specific examples

[0998] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[0999] The server extracts models such as iPhone SE and iPhone 11 that meet the criteria from the database and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users.

[1000] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price."

[1001] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[1002] In this way, users can quickly search for products, check reviews, and find the best place to buy them. This system will greatly contribute to users' efficient and satisfying online shopping.

[1003] The processing flow will be explained below.

[1004] Step 1:

[1005] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[1006] Step 2:

[1007] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[1008] Step 3:

[1009] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[1010] Step 4:

[1011] The server transmits the generated optimal product list to the user terminal.

[1012] Step 5:

[1013] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[1014] Step 6:

[1015] The user selects a product they are interested in on the screen and requests that reviews of that product be collected. The request is sent to the server.

[1016] Step 7:

[1017] The server receives a review collection request from the user and collects review information about the target product from multiple sources (e.g., e-commerce sites, social media, video sharing platforms, etc.).

[1018] Step 8:

[1019] The server analyzes the collected reviews using natural language processing technology, extracting positive and negative opinions, specific advantages and disadvantages, and generating summaries.

[1020] Step 9:

[1021] The server transmits the generated summary information of the word-of-mouth to the user terminal.

[1022] Step 10:

[1023] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[1024] Step 11:

[1025] A user requests price information and where to purchase a product they like. The request is sent to the server.

[1026] Step 12:

[1027] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[1028] Step 13:

[1029] The server compares the price information it has acquired and identifies the best place to purchase, taking into consideration point redemption and coupon application information.

[1030] Step 14:

[1031] The server transmits information about the optimal purchasing source identified to the user terminal.

[1032] Step 15:

[1033] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[1034] Example 1

[1035] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1036] In today's online shopping environment, users must access a vast amount of product information, reviews, and price information, making it difficult to select the best product. Furthermore, finding the lowest price across multiple purchasing sites requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently search for products, summarize reviews, and compare prices, allowing users to quickly find the best product and where to purchase it.

[1037] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1038] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by matching the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting product review information from multiple information sources, means for analyzing the collected review information and generating a summary, means for transmitting the summarized review information to the terminal, means for obtaining and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified product to the terminal, means for performing natural language processing using a generative AI model to summarize the collected review information, and means for obtaining price information from purchasing sites based on a price comparison request received from the terminal and comparing the price information, including point redemption and coupon application information. This enables users to quickly search for products, check reviews, and find the optimal purchasing site.

[1039] A "terminal" is a device that a user operates and inputs information, and includes a personal computer, a smartphone, a tablet, and the like.

[1040] A "server" is a computer system that processes data, accesses databases, and collects information, and communicates with terminals via a network.

[1041] A "search query" is a condition or keyword that a user inputs through a terminal, and includes input information for a product search.

[1042] A "database" is a system for efficiently storing and managing product information and user information, and includes relational databases and NoSQL databases.

[1043] "Related products" are products that match or are related to the product a user is looking for via a search query.

[1044] "Past history" refers to a record of the user's previous purchases and browsing, and is information used to understand the user's preferences.

[1045] "Similar user preference data" refers to data of other users who have similar preferences and purchasing patterns, and is used to generate an optimal product list for a specific user.

[1046] A "product list" is a list of optimal products generated based on the user's search criteria and past history data.

[1047] "Review information" refers to ratings and opinions provided by product purchasers or users, and is primarily collected from e-commerce sites, social networking services, and video-sharing platforms.

[1048] "Analysis" is the act of processing collected data to extract useful information, and includes, among other things, natural language processing and data mining techniques.

[1049] A "summary" is information that extracts the main opinions and evaluations from a vast amount of review information and summarizes them concisely.

[1050] A "purchasing site" is a website that allows users to purchase products online, and includes multiple platforms.

[1051] "Price Information" is a detailed description of the price offered at each purchasing site for a particular product.

[1052] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and uses that knowledge to generate or analyze new data.

[1053] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1054] "Point redemption" is a benefit that allows you to use points earned at the time of purchase under certain conditions at a later date.

[1055] A "coupon" is a code or ticket that provides a discount or benefit that can be applied when purchasing a product.

[1056] An "e-commerce site" is a website for buying and selling goods online.

[1057] A "social networking service" is a web service that allows users to interact with each other, share information, and post reviews.

[1058] A "video sharing platform" is a web service that allows users to post videos and allow other users to view them.

[1059] This invention is an AI system that helps users shop online efficiently by searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[1060] Product search and recommendations

[1061] The user opens a product search screen using a device and enters a search query, such as "price range: 30,000 to 50,000 yen, brand: Apple, category: smartphone."

[1062] The device sends the search query entered by the user in JSON format to the server. The server receives this query and uses an SQL query to extract products that meet the criteria from a database such as MySQL. A query like "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='smartphone'" is used.

[1063] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate an optimal product list. The generated product list is sent to the terminal in JSON format.

[1064] The terminal parses the received product list and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[1065] Collecting and summarizing reviews

[1066] If a user wants more information about a specific product, they can request a review of that product, for example, by clicking the Details button for the "iPhone 11."

[1067] The device sends this request in JSON format to the server, which then uses Python scripts to scrape reviews from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium.

[1068] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates summaries. It extracts key opinions and ratings and summarizes them as "good battery life," "great camera performance," and "expensive price."

[1069] The device then displays the summary information to the user, with each summary point displayed as a bullet point and an associated star rating.

[1070] Price comparison and recommendations for recommended e-commerce sites

[1071] When a user is considering purchasing a product, they request a price comparison, for example, by clicking the "Buy iPhone 11" button.

[1072] The terminal sends this request in JSON format to the server, which then uses APIs to collect price information from multiple purchasing sites. For example, it uses the API of an e-commerce site to obtain price information.

[1073] The server compares and analyzes the acquired price information, taking into account point redemption and coupon application information, and uses the Python Pandas library to format the data and identify the best place to purchase.

[1074] Finally, the device displays the optimal purchasing information received from the server to the user, providing specific price information and purchasing benefits, such as "Rakuten is the cheapest, and another site offers 10% points back."

[1075] Specific examples

[1076] For example, if a user searches for "price range: ¥30,000-¥50,000, brand: Apple, category: smartphone," the server extracts products that match the criteria, such as the iPhone SE or iPhone 11, from the MySQL database and displays them on the device. When a user is interested in the iPhone 11 and sends a request to check reviews, the server collects review information using Python scripts and scraping tools, and uses a natural language processing model to generate summaries such as "good battery life" and "great camera performance." Finally, when a user requests a price comparison for the iPhone 11, the server retrieves price information from the APIs of multiple purchasing sites, analyzes it using PANDAS, and identifies the best place to purchase, allowing the user to purchase the product conveniently and quickly.

[1077] This allows users to quickly search for products, check reviews, and find the best place to buy, making this system a great contribution to making online shopping more efficient and satisfying for users.

[1078] Prompt Sentence Examples

[1079] As a concrete example of how this system can be implemented, consider the following prompt:

[1080] "Search for Apple smartphones in the price range of 30,000 to 50,000 yen."

[1081] "Collect iPhone 11 reviews."

[1082] "Compare iPhone 11 prices across multiple sites."

[1083] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] The user opens a product search screen on their device and enters a search query. For example, they enter conditions such as "Price range: 30,000 to 50,000 yen, Brand: Apple, Category: Smartphone." This becomes the input data.

[1086] Step 2:

[1087] The device sends the search query entered by the user to the server in JSON format. The data received by the server contains the search conditions.

[1088] Step 3:

[1089] The server analyzes this query and extracts products that match the conditions from a database such as MySQL using an SQL query. Specifically, it executes a query such as "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='Smartphone'". The related products retrieved from the database are output.

[1090] Step 4:

[1091] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate the optimal product list. As a result of the comparison, the optimal product list for the user is output in JSON format.

[1092] Step 5:

[1093] The server sends the generated optimal product list to the terminal as an API response. The data received by the terminal is the matched product list.

[1094] Step 6:

[1095] The terminal analyzes the product list received from the server and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[1096] Step 7:

[1097] If a user wants more information about a specific product, they can request a review of that product, for example by clicking the "iPhone 11" details button. This becomes new input data.

[1098] Step 8:

[1099] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[1100] Step 9:

[1101] The server uses Python scripts to collect review information from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium, and outputs the collected review information.

[1102] Step 10:

[1103] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates a summary, which extracts key opinions and ratings.

[1104] Step 11:

[1105] The server sends the generated review summary in JSON format to the device, and the data received by the device is the summarized review information.

[1106] Step 12:

[1107] The terminal displays the summary information received from the server to the user, with each summary point displayed as a bullet point and an associated star rating.

[1108] Step 13:

[1109] When a user is considering purchasing a product, they request a price comparison. For example, they click the "Buy iPhone 11" button. This becomes new input data.

[1110] Step 14:

[1111] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[1112] Step 15:

[1113] The server uses an API to collect price information from multiple purchasing sites. For example, it obtains price information using the API of an e-commerce site. The collected price information is output.

[1114] Step 16:

[1115] The server compares and analyzes the collected price information, taking into account point redemption and coupon application information. It uses the Python Pandas library to format the data and identify the optimal purchasing location. The data is then output as optimal purchasing location information.

[1116] Step 17:

[1117] The server sends the optimal purchasing information to the device in JSON format. The data received by the device is the optimal purchasing information.

[1118] Step 18:

[1119] The device displays the optimal purchasing information received from the server in an easy-to-read format for the user. For example, it shows specific price information and purchasing benefits, such as "Rakuten is the cheapest, and other sites offer 10% points back."

[1120] This allows users to quickly search for products, check reviews, and find the best place to buy.

[1121] (Application example 1)

[1122] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1123] When shopping online, users must spend a lot of time and effort selecting the best product from a vast amount of information. Furthermore, checking reviews and comparing prices is typically done manually, which is inefficient. The lack of automated processes for summarizing useful reviews and identifying optimal purchasing sites limits the user experience. Furthermore, while smartphone applications would improve user convenience, there are few such systems available. There is a need for an efficient and integrated system to solve these issues.

[1124] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1125] In this invention, the server

[1126] means for extracting related products from a database based on a search query received from the terminal;

[1127] A means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users;

[1128] means for transmitting the generated optimal product list to a terminal;

[1129] A means for collecting evaluation information about products from multiple sources;

[1130] means for analyzing the collected evaluation information and generating a summary;

[1131] means for transmitting the summarized evaluation information to a terminal;

[1132] A means to obtain and compare product prices from multiple purchasing sites,

[1133] means for identifying an optimal purchasing site based on the price information and transmitting the information to the terminal;

[1134] means functioning as an application installed on a smartphone;

[1135] A means to recommend the best products online based on users' purchasing queries,

[1136] natural language processing means for acquiring and analyzing the collected evaluation information from multiple information sources;

[1137] A means to identify the best place to purchase, taking into account price information and point redemption and coupon application information;

[1138] Includes:

[1139] This allows users to efficiently search for products, check review summaries, compare prices, and instantly find the best purchase, significantly improving the convenience and user experience of online shopping.

[1140] definition statement

[1141] A "terminal" is a device that a user directly operates, such as to input a search query or receive a product list.

[1142] A "search query" is a request that includes keywords and conditions that a user enters when searching for a product.

[1143] A "database" is a collection of information that stores product information, user history, and the like.

[1144] "Related Products" refers to products extracted from a database based on a user's search query.

[1145] "User's past history" refers to data relating to purchases and searches that the user has previously made.

[1146] "Preference data" is information relating to a user's preferences and tendencies.

[1147] The "generated optimal product list" is a product list generated by the server based on the search query and the user's history and preference data.

[1148] "Evaluation information" refers to feedback information such as word-of-mouth and reviews about a product.

[1149] "Analysis" is the process of using collected information to extract useful data.

[1150] A "summary" is a concise summary of the collected evaluation information.

[1151] A "purchasing site" is a website that sells products online.

[1152] "Price information" refers to the sales price offered by each purchasing site for a particular product.

[1153] An "application installed on a smartphone" is a program that runs on a smartphone and realizes the functions of the present invention.

[1154] "Natural language processing means" is a technology that analyzes language data, extracts meaning, and processes information.

[1155] "Point redemption" is a system in which a certain number of points are returned for each purchase.

[1156] "Coupon application" refers to the use of a code or voucher to receive a discount or special offer when purchasing a product.

[1157] MODE FOR CARRYING OUT THE INVENTION

[1158] The present invention provides a system for making online shopping more efficient. A user accesses the system using a terminal and inputs a search query. Specific embodiments of the system are described below.

[1159] Product search and recommendations

[1160] When a user enters a search query from a terminal, the query is sent to a server. The server extracts related products from a database based on the received search query. This database contains not only product information but also the user's past purchase history and preference data. The server uses this data to generate a product list that is optimal for the user. The generated product list is sent to the terminal, where the user can refer to it.

[1161] Collecting and summarizing reviews

[1162] When a user is interested in a particular product, they send a request to the server to check the product's reviews. The server collects reviews from multiple sources, including e-commerce sites, social networking services, and video sharing platforms. The collected reviews are analyzed and summarized using natural language processing technology. The summarized reviews are then sent to the user's device, where they can be viewed.

[1163] Price comparison and shopping site recommendations

[1164] When a user is considering purchasing a product, it is important to know the best place to purchase it, including price information and special offer information (point redemption and coupon application information). When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information. Based on this information, the server identifies the best place to purchase and sends that information to the terminal. The user can purchase the product under the best conditions.

[1165] Smartphone application

[1166] This system can also be implemented as a smartphone application. This application recommends optimal products online based on a purchasing query entered by the user. Furthermore, it obtains collected evaluation information from multiple sources and summarizes it using natural language processing technology (e.g., Gensim). It also includes a function to identify the optimal purchasing destination by taking into account price information and special offer information.

[1167] Specific examples

[1168] For example, consider the case where a user enters the query "smartphone" to perform a search. The server extracts smartphones that match the search criteria from the database and generates an optimal product list by taking into account past purchase history and preferences. When a user becomes interested in a particular smartphone and sends a request to check its reviews, the server collects reviews from e-commerce sites and social networking services and summarizes them using natural language processing technology. Finally, the server recommends the optimal purchasing site by taking into account price comparisons and special offers, allowing the user to purchase the product at the best price.

[1169] Prompt Sentence Examples

[1170] When a user searches for "iPhone 12 price comparison":

[1171] 1. Get product list from API

[1172] 2. Collecting ratings from multiple sources using libraries such as BeautifulSoup

[1173] 3. Summarize the evaluation information using the summarize function of Gensim

[1174] 4. Obtain and compare price information from multiple purchasing sites

[1175] This allows users to efficiently search for products, check summary reviews, compare prices, and instantly find the best place to buy, significantly improving the convenience of online shopping.

[1176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1177] Program processing steps

[1178] Step 1:

[1179] The user enters a search query from their device. The search query includes conditions such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." This becomes a request to the server.

[1180] Step 2:

[1181] The server extracts related products from the database based on the received search query. The search query is used as input (conditions) and a database search is performed based on this. The output is a list of related products.

[1182] Step 3:

[1183] The server compares the extracted related products with the user's past history and similar users' preference data. The server uses the user history data and preference data as inputs and integrates them to output an optimal product list.

[1184] Step 4:

[1185] The server sends the generated optimal product list to the user's terminal. The terminal displays the received product list. The input is the optimal product list, and the output is the list displayed on the terminal.

[1186] Step 5:

[1187] When a user wants to check the details of a particular product, the user sends a request for reviews of that product. The terminal sends the request to the server.

[1188] Step 6:

[1189] The server collects reviews of the product from multiple sources. The input is the request and the source URL, and the output is the collected reviews. Here, we use a library such as BeautifulSoup to extract text from the web page.

[1190] Step 7:

[1191] The server analyzes the collected reviews using natural language processing technology and generates summaries. Specifically, it uses the summarize function from the Gensim library. The input is the text data of the reviews, and the output is the summarized text.

[1192] Step 8:

[1193] The server sends the summarized review information to the user's terminal, and the terminal displays the summarized information. The input is the summarized review information, and the output is the information displayed on the terminal.

[1194] Step 9:

[1195] The user submits a request for a price comparison of a product. The device sends the request to the server.

[1196] Step 10:

[1197] The server retrieves and compares product prices from multiple purchasing sites. The input is the request and the purchasing site URL, and the output is the price information retrieved from each site.

[1198] Step 11:

[1199] The server identifies the optimal purchasing location by adding information on rewards and coupons to the acquired price information. The input is price information and additional information, and the output is the optimal purchasing location.

[1200] Step 12:

[1201] The server sends the optimal purchasing information to the user's terminal. The terminal displays the received information. The input is the optimal purchasing information, and the output is the information displayed on the terminal.

[1202] Specific examples of each step

[1203] Step 1 example: A user types "iPhone 12 price comparison" into their smartphone and presses the search button.

[1204] Step 3 example: The server analyzes the devices the user has purchased in the past and the features preferred by similar users to create an optimal product list.

[1205] Step 6 example: The server collects reviews of the iPhone 12 from each source and combines all the text data into one.

[1206] Example of Step 8: The device displays a summary such as "good battery life," "great camera," and "high price."

[1207] Example of Step 12: Information such as "Site A has the lowest price" and "Site B offers point redemption" is displayed on the user's device.

[1208] In this way, by explaining in detail the specific processing and data flow at each step, we create a system that allows users to efficiently search for products, obtain the information they need, and make optimal decisions.

[1209] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1210] This invention is an AI system that helps users efficiently shop online by searching for related products, summarizing reviews, and comparing prices in a short amount of time. It also combines an emotion engine that recognizes the user's emotional state to provide a more personalized shopping experience. The following are specific examples of how this system can be implemented.

[1211] Product search and recommendations

[1212] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[1213] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[1214] The generated product list is adjusted by an emotion engine based on the user's emotional state. The emotion engine analyzes emotions from the user's search queries and past behavioral data, and can prioritize products with a relaxing effect to reduce stress, for example.

[1215] Collecting and summarizing reviews

[1216] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[1217] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[1218] The summary information of the generated reviews is also adjusted based on the user's emotional state. For example, if the user is in a positive emotional state, the summary is displayed as is. However, if the user is in a negative emotional state, the summary is adjusted to display more positive opinions to alleviate anxiety.

[1219] Price comparison and recommendations for recommended e-commerce sites

[1220] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[1221] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[1222] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[1223] Specific examples

[1224] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[1225] The server extracts models such as the iPhone SE or iPhone 11 that match the user's criteria from the database and generates an optimal product list based on the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data. For example, if the user is feeling stressed, the simple and easy-to-use iPhone SE will be displayed first.

[1226] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate their anxiety.

[1227] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[1228] In this way, users can efficiently search for products, check reviews, and find the best places to buy. The system also takes into account the user's emotional state, providing a more personalized shopping experience.

[1229] The processing flow will be explained below.

[1230] Step 1:

[1231] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[1232] Step 2:

[1233] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[1234] Step 3:

[1235] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[1236] Step 4:

[1237] The server sends the generated optimal product list to the terminal.

[1238] Step 5:

[1239] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[1240] Step 6:

[1241] A user selects a product they are interested in and requests that reviews of that product be collected. The request is sent to the server.

[1242] Step 7:

[1243] The server receives a request from a user to collect reviews and collects reviews of the target product from multiple information sources, such as e-commerce sites, social media, and video sharing platforms.

[1244] Step 8:

[1245] The server analyzes the collected word-of-mouth information using natural language processing technology, extracts positive and negative opinions, specific advantages and disadvantages, etc., and generates a summary.

[1246] Step 9:

[1247] The server transmits the generated summary information of the reviews to the terminal.

[1248] Step 10:

[1249] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[1250] Step 11:

[1251] A user decides to purchase based on the reviews and requests information about the product's price and where to purchase it. The request is sent to the server.

[1252] Step 12:

[1253] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[1254] Step 13:

[1255] The server compares the price information it has acquired and also takes into account information on point redemptions and coupon applications to identify the best place to purchase.

[1256] Step 14:

[1257] The server transmits information about the optimal purchasing source identified to the terminal.

[1258] Step 15:

[1259] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[1260] Step 16:

[1261] The emotion engine analyzes users' search queries and reviews to recognize their emotional state, and adjusts the displayed product listings and reviews accordingly.

[1262] Step 17:

[1263] The server transmits the product list and word-of-mouth information adjusted by the emotion engine to the user terminal.

[1264] Example 2

[1265] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1266] When shopping online, it is difficult for users to efficiently search for products and find related products in a short time. Furthermore, it is also difficult to aggregate word-of-mouth information from multiple sources and present it in an easy-to-understand format, or to compare price information from multiple purchasing sites to find the best place to buy. In particular, there is a problem in that information is not provided personalized according to the user's emotional state, which hinders the user experience.

[1267] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for extracting related products from a database based on a search query received from a terminal; means for generating an optimal product list by matching the extracted related products with the user's past history and similar user preference data; means for adjusting the generated optimal product list based on the user's emotional state; means for transmitting the adjusted optimal product list to the terminal; means for collecting product review information from multiple information sources; means for analyzing the collected review information and generating a summary; means for adjusting the summarized review information based on the user's emotional state; means for transmitting the adjusted review information to the terminal; means for acquiring and comparing product prices from multiple purchasing sites; and means for identifying the optimal purchasing site based on the price information and transmitting the price information to the terminal. This enables users to efficiently search for products, review review information in an easy-to-understand format, and compare prices to find the optimal purchasing source. Furthermore, by using an emotion engine, personalized information tailored to the user's emotional state can be provided.

[1268] A "terminal" is an electronic device that allows a user to input a search query or receive product information.

[1269] A "search query" is a string of characters including keywords and conditions that a user enters to search for a product.

[1270] A "server" is a central system that receives and processes user-entered search queries.

[1271] A "database" is an information management system that stores related product information and user history.

[1272] "Related products" are products extracted based on a user's search query.

[1273] "User's past history" is a record of the user's previous purchases and searches.

[1274] "Similar user preference data" is behavior data of other users who have similar preferences to the user.

[1275] The "optimal product list" is a product list generated based on the user's search query, past history, and preference data of similar users.

[1276] An "emotion engine" is software that analyzes the user's emotional state and adjusts information based on the results.

[1277] "Word-of-mouth information" refers to the opinions and ratings of other users about a particular product.

[1278] A "source" is a website or service from which review information is obtained.

[1279] "Natural language processing technology" is a technology for analyzing collected word-of-mouth information and generating summaries.

[1280] "Purchase Site" means a website or platform where products can be purchased online.

[1281] "Price information" is data regarding product prices obtained from a purchasing site.

[1282] "Point redemption" is a system in which points are earned as a reward for purchases.

[1283] "Coupon applied" means that a coupon that can be used as a discount is applied to the purchase price of the product.

[1284] The "optimal purchasing site" is the purchasing site that is judged to be the most advantageous for the user after a comprehensive comparison of price information, point redemption, and coupon application information.

[1285] This invention relates to an AI system that supports users in efficiently shopping online. Specifically, it is a system that quickly searches for related products, summarizes reviews, and compares prices, and by combining it with an emotion engine that recognizes the user's emotional state, it provides a more personalized shopping experience.

[1286] Hardware and Software Configuration

[1287] The server is the core of this system and performs the following main processes:

[1288] Search query analysis

[1289] Extracting related products from the database

[1290] Matching with user history and similar users' preference data

[1291] Adjusting product listings using an emotion engine

[1292] Collecting, analyzing, and summarizing reviews

[1293] Get and compare prices

[1294] Identifying the best purchasing site

[1295] The terminal is used by the user to input search queries and display product lists, reviews, and price comparison results sent from the server. The terminal can be a personal computer, smartphone, tablet, etc.

[1296] The emotion engine analyzes emotions from users' search queries and past behavioral data, and adjusts the information displayed based on the user's emotional state.

[1297] The database is used to store data such as product information, user purchase history, and word-of-mouth information.

[1298] Natural language processing technology is a technology for analyzing collected word-of-mouth information and generating summaries in a form that is easy for users to understand.

[1299] Example of a system

[1300] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." The server extracts models such as the iPhone SE and iPhone 11 that match the criteria from the database, and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data, and if the user is feeling stressed, for example, it will prioritize displaying the simple and easy-to-use iPhone SE.

[1301] A user specifically expresses interest in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social networking services, and video sharing platforms, and generates summaries such as "good battery life," "great camera performance," and "expensive price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate anxiety.

[1302] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from multiple purchasing sites, identifies the best purchase site, and provides it to the user. For example, it may present information such as "site A has the lowest price" or "site B offers 10% points back." Based on this information, the user can choose the most suitable purchase site and purchase the product.

[1303] Prompt Sentence Examples

[1304] An example of a prompt sentence to be input to the generative AI model is shown below.

[1305] "A user is shopping online and is looking for a smartphone. The price range is between 30,000 and 50,000 yen, and the brand is Apple. The user's past purchase history shows that they have owned smartphones. Generate a program that implements a system that recommends the best smartphone for the user, generates a summary of reviews, and suggests the best place to purchase."

[1306] This system allows users to efficiently search for products, check reviews in an easy-to-understand format, and compare prices to find the best deals. It also utilizes an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[1307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1308] Step 1:

[1309] A user enters a search query on a device. The user opens a shopping application and enters "Smartphone Price range: 30,000 to 50,000 yen Brand: Apple" into the search bar. When entering the query, the user specifies conditions such as price range, brand, category, etc. The device sends this entered search query to the server.

[1310] Step 2:

[1311] The server receives the search query and analyzes it. During analysis, the submitted search query is tokenized and important keywords and conditions are extracted. For example, information such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple" are extracted.

[1312] Step 3:

[1313] The server searches the database based on the analyzed search query and extracts related products. From the product data in the database, models such as "iPhone SE" and "iPhone 11" that match the criteria are extracted. The extracted results are temporarily saved on the server as a product list.

[1314] Step 4:

[1315] The server compares the user's past purchase history with the preference data of similar users to generate an optimal product list. By referencing the past purchase history and preference data of similar users, the most suitable products for the user are added to the list. For example, if a user has purchased many Apple products in the past, Apple products will be recommended first.

[1316] Step 5:

[1317] The emotion engine analyzes the user's emotional state and adjusts the generated optimal product list. The emotion engine analyzes the user's emotional state from the user's search query and past behavioral data, and adjusts the list to prioritize products with a relaxing effect if the user is feeling stressed, for example. The adjusted product list is saved on the server.

[1318] Step 6:

[1319] The server sends the adjusted optimal product list to the device. The device receives this list and displays it to the user. The user then checks the list of suggested products, such as "iPhone SE" and "iPhone 11," on the device.

[1320] Step 7:

[1321] A user is interested in a product and wants to check its reviews. The user requests reviews for a specific product (e.g., iPhone 11) on their device. The device then sends this request to the server.

[1322] Step 8:

[1323] The server collects word-of-mouth information about the product from multiple sources (e.g., e-commerce sites, social networking services, video sharing platforms, etc.), such as reviews on e-commerce sites, posts on social media, and comments on review videos on video sites.

[1324] Step 9:

[1325] The server analyzes the collected reviews using natural language processing technology and generates summaries, such as "battery life is good" and "camera performance is excellent." These summaries are temporarily stored in a database.

[1326] Step 10:

[1327] The emotion engine analyzes the user's emotional state and adjusts the generated summary accordingly. For example, if the user is unsure about a purchase, the summary will be adjusted to display more positive reviews. The adjusted review summary is then stored on the server.

[1328] Step 11:

[1329] The server sends the adjusted review summary to the device. The device receives the summary and displays it to the user. The user sees summaries such as "battery life is good," "camera performance is excellent," and "price is high."

[1330] Step 12:

[1331] A user is considering a purchase and wants to check price information. The user makes a price comparison request on their device. The device sends this request to the server.

[1332] Step 13:

[1333] The server obtains price information from multiple purchasing sites, such as Amazon, Rakuten, and other online retailers.

[1334] Step 14:

[1335] The server takes into account point redemption and coupon application information in addition to the price information it obtains, and performs a comprehensive comparison. For example, it compiles information such as "Rakuten is the cheapest" and "Amazon offers 10% point redemption."

[1336] Step 15:

[1337] The server identifies the best place to purchase and sends information about the best purchasing site to the terminal. The terminal receives this information and displays it to the user. The user selects the best place to purchase based on information such as "site A has the lowest price" or "site B offers 10% points back," and then purchases the product.

[1338] (Application example 2)

[1339] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1340] Current online shopping systems require users to search for products, check reviews, and compare prices, which is a time-consuming and tedious process. Furthermore, while users' emotional state can affect their shopping experience, personalization is not implemented to address this. As a result, users find it difficult to achieve a satisfactory shopping experience.

[1341] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1342] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting word-of-mouth information about products from multiple information sources, means for analyzing the collected word-of-mouth information and generating a summary, means for transmitting the summarized word-of-mouth information to the terminal, means for acquiring and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified word-of-mouth information to the terminal, and means for recognizing the user's emotional state and adjusting the display content of the generated product list and word-of-mouth information based on the user's emotional state, thereby enabling users to efficiently search, compare, and consider products and enjoy an optimal shopping experience personalized based on the user's emotional state.

[1343] A "terminal" is an information processing device that allows a user to communicate with a server via the Internet.

[1344] A "search query" is a keyword or phrase that a user enters to search for specific information.

[1345] "Related products" are products extracted from a database based on a search query.

[1346] A "database" is an information management system for storing product information and user history information.

[1347] "User's past history" is data related to products that the user has searched for or purchased in the past.

[1348] "Similar user preference data" is data analyzed based on the preferences and purchasing behavior of other users.

[1349] The "optimal product list" is a list of products generated based on the user's queries, past history, and preference data of similar users.

[1350] "Information sources" are sources of information such as e-commerce sites, social media, and video sharing platforms.

[1351] "Word-of-mouth information" is information that contains evaluations and opinions about a product.

[1352] The "summary" is a concise summary of the collected word-of-mouth information.

[1353] A "purchasing site" is an online shopping site where products are sold.

[1354] "Price information" refers to product prices and discount information at each purchasing site.

[1355] An "emotional state" indicates a temporary emotional state of a user.

[1356] "Personalization" means tailoring content to a user's individual preferences and emotional state.

[1357] A system embodying this invention includes a user terminal, a server, and an information source. The user terminal is an information processing device such as a smartphone or tablet on which an online shopping application is installed. The server includes a database and an analysis engine and is responsible for various data processing. The information source may be an e-commerce site, a social networking site, a video sharing platform, or the like.

[1358] 1. System Program Overview

[1359] The server performs the following operations:

[1360] It receives a search query sent from a user's device and extracts related products from a database.

[1361] The extracted related products are compared with the user's past history and the preference data of similar users to generate an optimal product list.

[1362] The generated optimum product list is sent to the user's terminal.

[1363] Word-of-mouth information about a product is collected from multiple information sources, and the collected word-of-mouth information is analyzed to generate a summary.

[1364] The summarized word-of-mouth information is sent to the user's terminal.

[1365] To obtain product prices from multiple purchasing sites and compare the obtained price information.

[1366] The optimal purchasing site is identified based on the price information and sent to the user's terminal.

[1367] The emotional state of the user is recognized, and the generated product list and the display content of the word-of-mouth information are adjusted based on the emotional state.

[1368] 2. Natural language explanation of program processing

[1369] The server receives a search query sent from the user's device and searches a database to extract relevant product information. The database contains detailed product information, price information, and customer reviews. The server compares the extracted products with the user's past purchase history and similar users' preference data to generate an optimal product list. This allows users to quickly find products that match their preferences.

[1370] Next, the server collects review information from multiple sources, including e-commerce sites, social media, and video sharing platforms. The collected review information is analyzed using natural language processing technology to generate a summary. This summary information is then processed into a form that is easy for users to understand and sent to their devices.

[1371] The server then obtains price information from multiple purchasing sites and performs data calculations to compare prices. The compared price information takes into account point redemption and coupon application information to identify the optimal purchasing site. This information is also sent to the user's device.

[1372] To recognize the user's emotional state, the server uses an emotion engine to analyze emotions based on the user's search query and past behavioral data, and prioritizes displaying products that will help the user relax or that will pique the user's interest.

[1373] Specific hardware and software used includes database management systems (e.g., MySQL), natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis engines (e.g., the transformers library), and web scraping tools for gathering pricing information (e.g., Beautiful Soup).

[1374] 3. Examples of specific examples and prompts

[1375] For example, if a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple," the server will extract models such as the iPhone SE or iPhone 11 that match the criteria from the database. The server will generate an optimal product list taking into account the user's past purchasing history and the preferences of similar users, and the emotion engine will analyze the user's emotions.

[1376] Example prompt sentence:

[1377] Analyze your emotional state towards the following products:

[1378] Product name: iPhone 11

[1379] User reviews: ['Good battery life', 'Excellent camera performance', 'High price']

[1380] Determine whether the user's emotional state is positive or negative.

[1381] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[1382] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1383] Step 1:

[1384] A user inputs a search query from a terminal. The terminal receives the query and sends it to the server. The search query includes conditions such as product category, price range, and brand.

[1385] Input: Search query including product category, price range, and brand.

[1386] Output: The search query is sent to the server.

[1387] Step 2:

[1388] The server searches a database containing detailed information about each product based on the received search query, and extracts related products that match the query.

[1389] Input: Search query.

[1390] Data processing: Matching product data in the database with queries.

[1391] Output: A list of related products.

[1392] Step 3:

[1393] The server compares the extracted related products with the user's past history and similar users' preference data to generate an optimal product list, and determines the priority of the products based on the preference data and history data.

[1394] Input: List of related products, user's past history, preference data of similar users.

[1395] Data processing: Prioritizing related product lists.

[1396] Output: A list of optimal products.

[1397] Step 4:

[1398] The server sends the generated optimal product list to the user's terminal, which displays the list on its screen.

[1399] Input: Optimal product listing.

[1400] Output: The best product list displayed on the device.

[1401] Step 5:

[1402] When a user becomes interested in a particular product, the user sends a request from the terminal to the server to check word-of-mouth information about the product.

[1403] Input: User review request.

[1404] Output: The review confirmation request is sent to the server.

[1405] Step 6:

[1406] The server collects word-of-mouth information from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) and analyzes the collected information using natural language processing technology to generate summaries.

[1407] Input: Review request.

[1408] Data processing: Collecting reviews from sources and generating summaries.

[1409] Output: A summary of the reviews.

[1410] Step 7:

[1411] The server transmits the summarized word-of-mouth information to the user's terminal, which displays the received summarized word-of-mouth information to the user.

[1412] Input: Abridged review information.

[1413] Output: Summary review information displayed on terminal.

[1414] Step 8:

[1415] When a user requests price information, the server retrieves product prices from multiple purchasing sites.

[1416] Input: User request for pricing information.

[1417] Output: The price information request is sent to the server.

[1418] Step 9:

[1419] The server compares the acquired price information and identifies the best place to purchase, taking into consideration point redemption and coupon application.

[1420] Input: Price information from each purchasing site.

[1421] Data processing: Comparing and analyzing price information.

[1422] Output: Best place to buy information.

[1423] Step 10:

[1424] The server transmits the identified optimal supplier information to the user's terminal, which displays this information to the user.

[1425] Input: Best Buy Information.

[1426] Output: Best place to buy information displayed on device.

[1427] Step 11:

[1428] The server uses an emotion engine to analyze search queries and past behavioral data to recognize the user's emotional state.

[1429] Input: User search queries, past behavior data.

[1430] Data Computing: Sentiment Analysis.

[1431] Output: The user's emotional state.

[1432] Step 12:

[1433] The server adjusts the displayed product list and review information based on the user's emotional state: if the user is feeling stressed, it prioritizes the display of products with a relaxing effect and positive reviews.

[1434] Input: User's emotional state, optimal product listings, summarized customer reviews.

[1435] Data processing: Adjusting display content based on emotional state.

[1436] Output: Product listings and reviews tailored based on emotional state.

[1437] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[1438] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1440] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1441] [Fourth embodiment]

[1442] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1443] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1444] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1445] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1446] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1447] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1448] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1449] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1450] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1451] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1452] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1453] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1454] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1455] This invention is an AI system that allows users to efficiently shop online, searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[1456] Product search and recommendations

[1457] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[1458] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[1459] By receiving this product list, users are presented with products that best meet their needs, enabling them to make efficient product selections.

[1460] Collecting and summarizing reviews

[1461] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[1462] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[1463] This allows users to efficiently understand the vast amount of word-of-mouth information and quickly grasp the advantages and disadvantages of a product.

[1464] Price comparison and recommendations for recommended e-commerce sites

[1465] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[1466] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[1467] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[1468] Specific examples

[1469] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[1470] The server extracts models such as iPhone SE and iPhone 11 that meet the criteria from the database and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users.

[1471] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price."

[1472] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[1473] In this way, users can quickly search for products, check reviews, and find the best place to buy them. This system will greatly contribute to users' efficient and satisfying online shopping.

[1474] The processing flow will be explained below.

[1475] Step 1:

[1476] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[1477] Step 2:

[1478] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[1479] Step 3:

[1480] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[1481] Step 4:

[1482] The server transmits the generated optimal product list to the user terminal.

[1483] Step 5:

[1484] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[1485] Step 6:

[1486] The user selects a product they are interested in on the screen and requests that reviews of that product be collected. The request is sent to the server.

[1487] Step 7:

[1488] The server receives a review collection request from the user and collects review information about the target product from multiple sources (e.g., e-commerce sites, social media, video sharing platforms, etc.).

[1489] Step 8:

[1490] The server analyzes the collected reviews using natural language processing technology, extracting positive and negative opinions, specific advantages and disadvantages, and generating summaries.

[1491] Step 9:

[1492] The server transmits the generated summary information of the word-of-mouth to the user terminal.

[1493] Step 10:

[1494] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[1495] Step 11:

[1496] A user requests price information and where to purchase a product they like. The request is sent to the server.

[1497] Step 12:

[1498] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[1499] Step 13:

[1500] The server compares the price information it has acquired and identifies the best place to purchase, taking into consideration point redemption and coupon application information.

[1501] Step 14:

[1502] The server transmits information about the optimal purchasing source identified to the user terminal.

[1503] Step 15:

[1504] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[1505] Example 1

[1506] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1507] In today's online shopping environment, users must access a vast amount of product information, reviews, and price information, making it difficult to select the best product. Furthermore, finding the lowest price across multiple purchasing sites requires a great deal of time and effort. Therefore, there is a need for a system that can efficiently search for products, summarize reviews, and compare prices, allowing users to quickly find the best product and where to purchase it.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1509] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by matching the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting product review information from multiple information sources, means for analyzing the collected review information and generating a summary, means for transmitting the summarized review information to the terminal, means for obtaining and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified product to the terminal, means for performing natural language processing using a generative AI model to summarize the collected review information, and means for obtaining price information from purchasing sites based on a price comparison request received from the terminal and comparing the price information, including point redemption and coupon application information. This enables users to quickly search for products, check reviews, and find the optimal purchasing site.

[1510] A "terminal" is a device that a user operates and inputs information, and includes a personal computer, a smartphone, a tablet, and the like.

[1511] A "server" is a computer system that processes data, accesses databases, and collects information, and communicates with terminals via a network.

[1512] A "search query" is a condition or keyword that a user inputs through a terminal, and includes input information for a product search.

[1513] A "database" is a system for efficiently storing and managing product information and user information, and includes relational databases and NoSQL databases.

[1514] "Related products" are products that match or are related to the product a user is looking for via a search query.

[1515] "Past history" refers to a record of the user's previous purchases and browsing, and is information used to understand the user's preferences.

[1516] "Similar user preference data" refers to data of other users who have similar preferences and purchasing patterns, and is used to generate an optimal product list for a specific user.

[1517] A "product list" is a list of optimal products generated based on the user's search criteria and past history data.

[1518] "Review information" refers to ratings and opinions provided by product purchasers or users, and is primarily collected from e-commerce sites, social networking services, and video-sharing platforms.

[1519] "Analysis" is the act of processing collected data to extract useful information, and includes, among other things, natural language processing and data mining techniques.

[1520] A "summary" is information that extracts the main opinions and evaluations from a vast amount of review information and summarizes them concisely.

[1521] A "purchasing site" is a website that allows users to purchase products online, and includes multiple platforms.

[1522] "Price Information" is a detailed description of the price offered at each purchasing site for a particular product.

[1523] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and uses that knowledge to generate or analyze new data.

[1524] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1525] "Point redemption" is a benefit that allows you to use points earned at the time of purchase under certain conditions at a later date.

[1526] A "coupon" is a code or ticket that provides a discount or benefit that can be applied when purchasing a product.

[1527] An "e-commerce site" is a website for buying and selling goods online.

[1528] A "social networking service" is a web service that allows users to interact with each other, share information, and post reviews.

[1529] A "video sharing platform" is a web service that allows users to post videos and allow other users to view them.

[1530] This invention is an AI system that helps users shop online efficiently by searching for related products, summarizing reviews, and comparing prices in a short amount of time. The following is a specific example of how this system can be implemented.

[1531] Product search and recommendations

[1532] The user opens a product search screen using a device and enters a search query, such as "price range: 30,000 to 50,000 yen, brand: Apple, category: smartphone."

[1533] The device sends the search query entered by the user in JSON format to the server. The server receives this query and uses an SQL query to extract products that meet the criteria from a database such as MySQL. A query like "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='smartphone'" is used.

[1534] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate an optimal product list. The generated product list is sent to the terminal in JSON format.

[1535] The terminal parses the received product list and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[1536] Collecting and summarizing reviews

[1537] If a user wants more information about a specific product, they can request a review of that product, for example, by clicking the Details button for the "iPhone 11."

[1538] The device sends this request in JSON format to the server, which then uses Python scripts to scrape reviews from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium.

[1539] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates summaries. It extracts key opinions and ratings and summarizes them as "good battery life," "great camera performance," and "expensive price."

[1540] The device then displays the summary information to the user, with each summary point displayed as a bullet point and an associated star rating.

[1541] Price comparison and recommendations for recommended e-commerce sites

[1542] When a user is considering purchasing a product, they request a price comparison, for example, by clicking the "Buy iPhone 11" button.

[1543] The terminal sends this request in JSON format to the server, which then uses APIs to collect price information from multiple purchasing sites. For example, it uses the API of an e-commerce site to obtain price information.

[1544] The server compares and analyzes the acquired price information, taking into account point redemption and coupon application information, and uses the Python Pandas library to format the data and identify the best place to purchase.

[1545] Finally, the device displays the optimal purchasing information received from the server to the user, providing specific price information and purchasing benefits, such as "Rakuten is the cheapest, and another site offers 10% points back."

[1546] Specific examples

[1547] For example, if a user searches for "price range: ¥30,000-¥50,000, brand: Apple, category: smartphone," the server extracts products that match the criteria, such as the iPhone SE or iPhone 11, from the MySQL database and displays them on the device. When a user is interested in the iPhone 11 and sends a request to check reviews, the server collects review information using Python scripts and scraping tools, and uses a natural language processing model to generate summaries such as "good battery life" and "great camera performance." Finally, when a user requests a price comparison for the iPhone 11, the server retrieves price information from the APIs of multiple purchasing sites, analyzes it using PANDAS, and identifies the best place to purchase, allowing the user to purchase the product conveniently and quickly.

[1548] This allows users to quickly search for products, check reviews, and find the best place to buy, making this system a great contribution to making online shopping more efficient and satisfying for users.

[1549] Prompt Sentence Examples

[1550] As a concrete example of how this system can be implemented, consider the following prompt:

[1551] "Search for Apple smartphones in the price range of 30,000 to 50,000 yen."

[1552] "Collect iPhone 11 reviews."

[1553] "Compare iPhone 11 prices across multiple sites."

[1554] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1555] Step 1:

[1556] The user opens a product search screen on their device and enters a search query. For example, they enter conditions such as "Price range: 30,000 to 50,000 yen, Brand: Apple, Category: Smartphone." This becomes the input data.

[1557] Step 2:

[1558] The device sends the search query entered by the user to the server in JSON format. The data received by the server contains the search conditions.

[1559] Step 3:

[1560] The server analyzes this query and extracts products that match the conditions from a database such as MySQL using an SQL query. Specifically, it executes a query such as "SELECT FROM products WHERE price BETWEEN 30000 AND 50000 AND brand='Apple' AND category='Smartphone'". The related products retrieved from the database are output.

[1561] Step 4:

[1562] The server compares the extracted product list with the user's past purchase history and similar users' preference data (analyzed using Hadoop) to generate the optimal product list. As a result of the comparison, the optimal product list for the user is output in JSON format.

[1563] Step 5:

[1564] The server sends the generated optimal product list to the terminal as an API response. The data received by the terminal is the matched product list.

[1565] Step 6:

[1566] The terminal analyzes the product list received from the server and displays it in a user-friendly format, including an image, name, price, and brief description of each product.

[1567] Step 7:

[1568] If a user wants more information about a specific product, they can request a review of that product, for example by clicking the "iPhone 11" details button. This becomes new input data.

[1569] Step 8:

[1570] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[1571] Step 9:

[1572] The server uses Python scripts to collect review information from e-commerce sites, social networking services, and video sharing platforms using scraping tools such as BeautifulSoup and Selenium, and outputs the collected review information.

[1573] Step 10:

[1574] The server analyzes the collected review information using natural language processing techniques (e.g., BERT or GPT-3) and generates a summary, which extracts key opinions and ratings.

[1575] Step 11:

[1576] The server sends the generated review summary in JSON format to the device, and the data received by the device is the summarized review information.

[1577] Step 12:

[1578] The terminal displays the summary information received from the server to the user, with each summary point displayed as a bullet point and an associated star rating.

[1579] Step 13:

[1580] When a user is considering purchasing a product, they request a price comparison. For example, they click the "Buy iPhone 11" button. This becomes new input data.

[1581] Step 14:

[1582] The device sends this request in JSON format to the server, and the data the server receives is the ID of the specific product.

[1583] Step 15:

[1584] The server uses an API to collect price information from multiple purchasing sites. For example, it obtains price information using the API of an e-commerce site. The collected price information is output.

[1585] Step 16:

[1586] The server compares and analyzes the collected price information, taking into account point redemption and coupon application information. It uses the Python Pandas library to format the data and identify the optimal purchasing location. The data is then output as optimal purchasing location information.

[1587] Step 17:

[1588] The server sends the optimal purchasing information to the device in JSON format. The data received by the device is the optimal purchasing information.

[1589] Step 18:

[1590] The device displays the optimal purchasing information received from the server in an easy-to-read format for the user. For example, it shows specific price information and purchasing benefits, such as "Rakuten is the cheapest, and other sites offer 10% points back."

[1591] This allows users to quickly search for products, check reviews, and find the best place to buy.

[1592] (Application example 1)

[1593] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1594] When shopping online, users must spend a lot of time and effort selecting the best product from a vast amount of information. Furthermore, checking reviews and comparing prices is typically done manually, which is inefficient. The lack of automated processes for summarizing useful reviews and identifying optimal purchasing sites limits the user experience. Furthermore, while smartphone applications would improve user convenience, there are few such systems available. There is a need for an efficient and integrated system to solve these issues.

[1595] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1596] In this invention, the server

[1597] means for extracting related products from a database based on a search query received from the terminal;

[1598] A means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users;

[1599] means for transmitting the generated optimal product list to a terminal;

[1600] A means for collecting evaluation information about products from multiple sources;

[1601] means for analyzing the collected evaluation information and generating a summary;

[1602] means for transmitting the summarized evaluation information to a terminal;

[1603] A means to obtain and compare product prices from multiple purchasing sites,

[1604] means for identifying an optimal purchasing site based on the price information and transmitting the information to the terminal;

[1605] means functioning as an application installed on a smartphone;

[1606] A means to recommend the best products online based on users' purchasing queries,

[1607] natural language processing means for acquiring and analyzing the collected evaluation information from multiple information sources;

[1608] A means to identify the best place to purchase, taking into account price information and point redemption and coupon application information;

[1609] Includes:

[1610] This allows users to efficiently search for products, check review summaries, compare prices, and instantly find the best purchase, significantly improving the convenience and user experience of online shopping.

[1611] definition statement

[1612] A "terminal" is a device that a user directly operates, such as to input a search query or receive a product list.

[1613] A "search query" is a request that includes keywords and conditions that a user enters when searching for a product.

[1614] A "database" is a collection of information that stores product information, user history, and the like.

[1615] "Related Products" refers to products extracted from a database based on a user's search query.

[1616] "User's past history" refers to data relating to purchases and searches that the user has previously made.

[1617] "Preference data" is information relating to a user's preferences and tendencies.

[1618] The "generated optimal product list" is a product list generated by the server based on the search query and the user's history and preference data.

[1619] "Evaluation information" refers to feedback information such as word-of-mouth and reviews about a product.

[1620] "Analysis" is the process of using collected information to extract useful data.

[1621] A "summary" is a concise summary of the collected evaluation information.

[1622] A "purchasing site" is a website that sells products online.

[1623] "Price information" refers to the sales price offered by each purchasing site for a particular product.

[1624] An "application installed on a smartphone" is a program that runs on a smartphone and realizes the functions of the present invention.

[1625] "Natural language processing means" is a technology that analyzes language data, extracts meaning, and processes information.

[1626] "Point redemption" is a system in which a certain number of points are returned for each purchase.

[1627] "Coupon application" refers to the use of a code or voucher to receive a discount or special offer when purchasing a product.

[1628] MODE FOR CARRYING OUT THE INVENTION

[1629] The present invention provides a system for making online shopping more efficient. A user accesses the system using a terminal and inputs a search query. Specific embodiments of the system are described below.

[1630] Product search and recommendations

[1631] When a user enters a search query from a terminal, the query is sent to a server. The server extracts related products from a database based on the received search query. This database contains not only product information but also the user's past purchase history and preference data. The server uses this data to generate a product list that is optimal for the user. The generated product list is sent to the terminal, where the user can refer to it.

[1632] Collecting and summarizing reviews

[1633] When a user is interested in a particular product, they send a request to the server to check the product's reviews. The server collects reviews from multiple sources, including e-commerce sites, social networking services, and video sharing platforms. The collected reviews are analyzed and summarized using natural language processing technology. The summarized reviews are then sent to the user's device, where they can be viewed.

[1634] Price comparison and shopping site recommendations

[1635] When a user is considering purchasing a product, it is important to know the best place to purchase it, including price information and special offer information (point redemption and coupon application information). When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information. Based on this information, the server identifies the best place to purchase and sends that information to the terminal. The user can purchase the product under the best conditions.

[1636] Smartphone application

[1637] This system can also be implemented as a smartphone application. This application recommends optimal products online based on a purchasing query entered by the user. Furthermore, it obtains collected evaluation information from multiple sources and summarizes it using natural language processing technology (e.g., Gensim). It also includes a function to identify the optimal purchasing destination by taking into account price information and special offer information.

[1638] Specific examples

[1639] For example, consider the case where a user enters the query "smartphone" to perform a search. The server extracts smartphones that match the search criteria from the database and generates an optimal product list by taking into account past purchase history and preferences. When a user becomes interested in a particular smartphone and sends a request to check its reviews, the server collects reviews from e-commerce sites and social networking services and summarizes them using natural language processing technology. Finally, the server recommends the optimal purchasing site by taking into account price comparisons and special offers, allowing the user to purchase the product at the best price.

[1640] Prompt Sentence Examples

[1641] When a user searches for "iPhone 12 price comparison":

[1642] 1. Get product list from API

[1643] 2. Collecting ratings from multiple sources using libraries such as BeautifulSoup

[1644] 3. Summarize the evaluation information using the summarize function of Gensim

[1645] 4. Obtain and compare price information from multiple purchasing sites

[1646] This allows users to efficiently search for products, check summary reviews, compare prices, and instantly find the best place to buy, significantly improving the convenience of online shopping.

[1647] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1648] Program processing steps

[1649] Step 1:

[1650] The user enters a search query from their device. The search query includes conditions such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." This becomes a request to the server.

[1651] Step 2:

[1652] The server extracts related products from the database based on the received search query. The search query is used as input (conditions) and a database search is performed based on this. The output is a list of related products.

[1653] Step 3:

[1654] The server compares the extracted related products with the user's past history and similar users' preference data. The server uses the user history data and preference data as inputs and integrates them to output an optimal product list.

[1655] Step 4:

[1656] The server sends the generated optimal product list to the user's terminal. The terminal displays the received product list. The input is the optimal product list, and the output is the list displayed on the terminal.

[1657] Step 5:

[1658] When a user wants to check the details of a particular product, the user sends a request for reviews of that product. The terminal sends the request to the server.

[1659] Step 6:

[1660] The server collects reviews of the product from multiple sources. The input is the request and the source URL, and the output is the collected reviews. Here, we use a library such as BeautifulSoup to extract text from the web page.

[1661] Step 7:

[1662] The server analyzes the collected reviews using natural language processing technology and generates summaries. Specifically, it uses the summarize function from the Gensim library. The input is the text data of the reviews, and the output is the summarized text.

[1663] Step 8:

[1664] The server sends the summarized review information to the user's terminal, and the terminal displays the summarized information. The input is the summarized review information, and the output is the information displayed on the terminal.

[1665] Step 9:

[1666] The user submits a request for a price comparison of a product. The device sends the request to the server.

[1667] Step 10:

[1668] The server retrieves and compares product prices from multiple purchasing sites. The input is the request and the purchasing site URL, and the output is the price information retrieved from each site.

[1669] Step 11:

[1670] The server identifies the optimal purchasing location by adding information on rewards and coupons to the acquired price information. The input is price information and additional information, and the output is the optimal purchasing location.

[1671] Step 12:

[1672] The server sends the optimal purchasing information to the user's terminal. The terminal displays the received information. The input is the optimal purchasing information, and the output is the information displayed on the terminal.

[1673] Specific examples of each step

[1674] Step 1 example: A user types "iPhone 12 price comparison" into their smartphone and presses the search button.

[1675] Step 3 example: The server analyzes the devices the user has purchased in the past and the features preferred by similar users to create an optimal product list.

[1676] Step 6 example: The server collects reviews of the iPhone 12 from each source and combines all the text data into one.

[1677] Example of Step 8: The device displays a summary such as "good battery life," "great camera," and "high price."

[1678] Example of Step 12: Information such as "Site A has the lowest price" and "Site B offers point redemption" is displayed on the user's device.

[1679] In this way, by explaining in detail the specific processing and data flow at each step, we create a system that allows users to efficiently search for products, obtain the information they need, and make optimal decisions.

[1680] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1681] This invention is an AI system that helps users efficiently shop online by searching for related products, summarizing reviews, and comparing prices in a short amount of time. It also combines an emotion engine that recognizes the user's emotional state to provide a more personalized shopping experience. The following are specific examples of how this system can be implemented.

[1682] Product search and recommendations

[1683] When a user searches for a product using a terminal, he or she first inputs a search query, which includes conditions such as price range, brand, and category.

[1684] The server extracts related products that match the search query received from the user from the database. This related product list is then collated with the user's past purchase history and the preference data of similar users to generate a product list that is optimal for the user.

[1685] The generated product list is adjusted by an emotion engine based on the user's emotional state. The emotion engine analyzes emotions from the user's search queries and past behavioral data, and can prioritize products with a relaxing effect to reduce stress, for example.

[1686] Collecting and summarizing reviews

[1687] When a user becomes interested in a specific product, they send a request to the server to check reviews of that product.

[1688] The server receives this request and collects reviews of the product from multiple sources (e-commerce sites, social media, video sharing platforms, etc.) The collected reviews are analyzed using natural language processing technology and summarized in a form that is easy for users to understand.

[1689] The summary information of the generated reviews is also adjusted based on the user's emotional state. For example, if the user is in a positive emotional state, the summary is displayed as is. However, if the user is in a negative emotional state, the summary is adjusted to display more positive opinions to alleviate anxiety.

[1690] Price comparison and recommendations for recommended e-commerce sites

[1691] When users are considering purchasing a product, it is important to know the best place to purchase it, including price information and information on point rewards and coupon applications.

[1692] When a user sends a price comparison request to the server, the server retrieves price information from multiple purchasing sites. The retrieved price information is compared, taking into account point redemption and coupon application information.

[1693] The server uses this information to identify the best place to purchase the product and sends that information to the user's terminal, allowing the user to purchase the product at the best price.

[1694] Specific examples

[1695] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple."

[1696] The server extracts models such as the iPhone SE or iPhone 11 that match the user's criteria from the database and generates an optimal product list based on the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data. For example, if the user is feeling stressed, the simple and easy-to-use iPhone SE will be displayed first.

[1697] A user is specifically interested in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social media, and video sharing platforms, and generates summaries such as "good battery life," "excellent camera performance," and "high price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate their anxiety.

[1698] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from Amazon, Rakuten, Yahoo! Shopping, etc., identifies the best purchase source, and provides it to the user. For example, it may present information such as "Rakuten is the cheapest" or "Amazon offers 10% points rebate."

[1699] In this way, users can efficiently search for products, check reviews, and find the best places to buy. The system also takes into account the user's emotional state, providing a more personalized shopping experience.

[1700] The processing flow will be explained below.

[1701] Step 1:

[1702] A user accesses the product search page using a terminal and enters search criteria such as price range, brand, category, etc. Then, the user presses the search button to send the search query to the server.

[1703] Step 2:

[1704] The server analyzes the search query received from the user and queries the database to extract related products that match the criteria.

[1705] Step 3:

[1706] The server compares the extracted related products with the user's past history and the preference data of similar users, and generates an optimal product list based on this information.

[1707] Step 4:

[1708] The server sends the generated optimal product list to the terminal.

[1709] Step 5:

[1710] The terminal displays the received optimal product list on the screen, allowing the user to check the product list.

[1711] Step 6:

[1712] A user selects a product they are interested in and requests that reviews of that product be collected. The request is sent to the server.

[1713] Step 7:

[1714] The server receives a request from a user to collect reviews and collects reviews of the target product from multiple information sources, such as e-commerce sites, social media, and video sharing platforms.

[1715] Step 8:

[1716] The server analyzes the collected word-of-mouth information using natural language processing technology, extracts positive and negative opinions, specific advantages and disadvantages, etc., and generates a summary.

[1717] Step 9:

[1718] The server transmits the generated summary information of the reviews to the terminal.

[1719] Step 10:

[1720] The terminal displays the received review summary information on the screen, allowing the user to check detailed product reviews.

[1721] Step 11:

[1722] A user decides to purchase based on the reviews and requests information about the product's price and where to purchase it. The request is sent to the server.

[1723] Step 12:

[1724] The server receives a price comparison request from a user and collects price information of the target product from a plurality of purchasing sites.

[1725] Step 13:

[1726] The server compares the price information it has acquired and also takes into account information on point redemptions and coupon applications to identify the best place to purchase.

[1727] Step 14:

[1728] The server transmits information about the optimal purchasing source identified to the terminal.

[1729] Step 15:

[1730] The device then displays the price comparison results and recommended purchasing information on the screen, allowing the user to purchase products at the best possible conditions.

[1731] Step 16:

[1732] The emotion engine analyzes users' search queries and reviews to recognize their emotional state, and adjusts the displayed product listings and reviews accordingly.

[1733] Step 17:

[1734] The server transmits the product list and word-of-mouth information adjusted by the emotion engine to the user terminal.

[1735] Example 2

[1736] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1737] When shopping online, it is difficult for users to efficiently search for products and find related products in a short time. Furthermore, it is also difficult to aggregate word-of-mouth information from multiple sources and present it in an easy-to-understand format, or to compare price information from multiple purchasing sites to find the best place to buy. In particular, there is a problem in that information is not provided personalized according to the user's emotional state, which hinders the user experience.

[1738] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for extracting related products from a database based on a search query received from a terminal; means for generating an optimal product list by matching the extracted related products with the user's past history and similar user preference data; means for adjusting the generated optimal product list based on the user's emotional state; means for transmitting the adjusted optimal product list to the terminal; means for collecting product review information from multiple information sources; means for analyzing the collected review information and generating a summary; means for adjusting the summarized review information based on the user's emotional state; means for transmitting the adjusted review information to the terminal; means for acquiring and comparing product prices from multiple purchasing sites; and means for identifying the optimal purchasing site based on the price information and transmitting the price information to the terminal. This enables users to efficiently search for products, review review information in an easy-to-understand format, and compare prices to find the optimal purchasing source. Furthermore, by using an emotion engine, personalized information tailored to the user's emotional state can be provided.

[1739] A "terminal" is an electronic device that allows a user to input a search query or receive product information.

[1740] A "search query" is a string of characters including keywords and conditions that a user enters to search for a product.

[1741] A "server" is a central system that receives and processes user-entered search queries.

[1742] A "database" is an information management system that stores related product information and user history.

[1743] "Related products" are products extracted based on a user's search query.

[1744] "User's past history" is a record of the user's previous purchases and searches.

[1745] "Similar user preference data" is behavior data of other users who have similar preferences to the user.

[1746] The "optimal product list" is a product list generated based on the user's search query, past history, and preference data of similar users.

[1747] An "emotion engine" is software that analyzes the user's emotional state and adjusts information based on the results.

[1748] "Word-of-mouth information" refers to the opinions and ratings of other users about a particular product.

[1749] A "source" is a website or service from which review information is obtained.

[1750] "Natural language processing technology" is a technology for analyzing collected word-of-mouth information and generating summaries.

[1751] "Purchase Site" means a website or platform where products can be purchased online.

[1752] "Price information" is data regarding product prices obtained from a purchasing site.

[1753] "Point redemption" is a system in which points are earned as a reward for purchases.

[1754] "Coupon applied" means that a coupon that can be used as a discount is applied to the purchase price of the product.

[1755] The "optimal purchasing site" is the purchasing site that is judged to be the most advantageous for the user after a comprehensive comparison of price information, point redemption, and coupon application information.

[1756] This invention relates to an AI system that supports users in efficiently shopping online. Specifically, it is a system that quickly searches for related products, summarizes reviews, and compares prices, and by combining it with an emotion engine that recognizes the user's emotional state, it provides a more personalized shopping experience.

[1757] Hardware and Software Configuration

[1758] The server is the core of this system and performs the following main processes:

[1759] Search query analysis

[1760] Extracting related products from the database

[1761] Matching with user history and similar users' preference data

[1762] Adjusting product listings using an emotion engine

[1763] Collecting, analyzing, and summarizing reviews

[1764] Get and compare prices

[1765] Identifying the best purchasing site

[1766] The terminal is used by the user to input search queries and display product lists, reviews, and price comparison results sent from the server. The terminal can be a personal computer, smartphone, tablet, etc.

[1767] The emotion engine analyzes emotions from users' search queries and past behavioral data, and adjusts the information displayed based on the user's emotional state.

[1768] The database is used to store data such as product information, user purchase history, and word-of-mouth information.

[1769] Natural language processing technology is a technology for analyzing collected word-of-mouth information and generating summaries in a form that is easy for users to understand.

[1770] Example of a system

[1771] For example, suppose a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple." The server extracts models such as the iPhone SE and iPhone 11 that match the criteria from the database, and generates an optimal product list taking into account the user's past purchasing history and the preferences of similar users. The emotion engine analyzes the user's emotions based on this data, and if the user is feeling stressed, for example, it will prioritize displaying the simple and easy-to-use iPhone SE.

[1772] A user specifically expresses interest in the iPhone 11 and requests to check reviews. The server collects reviews of the iPhone 11 from e-commerce sites, social networking services, and video sharing platforms, and generates summaries such as "good battery life," "great camera performance," and "expensive price." The emotion engine analyzes the user's emotional state, and if the user has concerns about the purchase, it displays more positive reviews to alleviate anxiety.

[1773] Finally, when a user requests price information to consider purchasing an iPhone 11, the server collects price information from multiple purchasing sites, identifies the best purchase site, and provides it to the user. For example, it may present information such as "site A has the lowest price" or "site B offers 10% points back." Based on this information, the user can choose the most suitable purchase site and purchase the product.

[1774] Prompt Sentence Examples

[1775] An example of a prompt sentence to be input to the generative AI model is shown below.

[1776] "A user is shopping online and is looking for a smartphone. The price range is between 30,000 and 50,000 yen, and the brand is Apple. The user's past purchase history shows that they have owned smartphones. Generate a program that implements a system that recommends the best smartphone for the user, generates a summary of reviews, and suggests the best place to purchase."

[1777] This system allows users to efficiently search for products, check reviews in an easy-to-understand format, and compare prices to find the best deals. It also utilizes an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[1778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1779] Step 1:

[1780] A user enters a search query on a device. The user opens a shopping application and enters "Smartphone Price range: 30,000 to 50,000 yen Brand: Apple" into the search bar. When entering the query, the user specifies conditions such as price range, brand, category, etc. The device sends this entered search query to the server.

[1781] Step 2:

[1782] The server receives the search query and analyzes it. During analysis, the submitted search query is tokenized and important keywords and conditions are extracted. For example, information such as "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple" are extracted.

[1783] Step 3:

[1784] The server searches the database based on the analyzed search query and extracts related products. From the product data in the database, models such as "iPhone SE" and "iPhone 11" that match the criteria are extracted. The extracted results are temporarily saved on the server as a product list.

[1785] Step 4:

[1786] The server compares the user's past purchase history with the preference data of similar users to generate an optimal product list. By referencing the past purchase history and preference data of similar users, the most suitable products for the user are added to the list. For example, if a user has purchased many Apple products in the past, Apple products will be recommended first.

[1787] Step 5:

[1788] The emotion engine analyzes the user's emotional state and adjusts the generated optimal product list. The emotion engine analyzes the user's emotional state from the user's search query and past behavioral data, and adjusts the list to prioritize products with a relaxing effect if the user is feeling stressed, for example. The adjusted product list is saved on the server.

[1789] Step 6:

[1790] The server sends the adjusted optimal product list to the device. The device receives this list and displays it to the user. The user then checks the list of suggested products, such as "iPhone SE" and "iPhone 11," on the device.

[1791] Step 7:

[1792] A user is interested in a product and wants to check its reviews. The user requests reviews for a specific product (e.g., iPhone 11) on their device. The device then sends this request to the server.

[1793] Step 8:

[1794] The server collects word-of-mouth information about the product from multiple sources (e.g., e-commerce sites, social networking services, video sharing platforms, etc.), such as reviews on e-commerce sites, posts on social media, and comments on review videos on video sites.

[1795] Step 9:

[1796] The server analyzes the collected reviews using natural language processing technology and generates summaries, such as "battery life is good" and "camera performance is excellent." These summaries are temporarily stored in a database.

[1797] Step 10:

[1798] The emotion engine analyzes the user's emotional state and adjusts the generated summary accordingly. For example, if the user is unsure about a purchase, the summary will be adjusted to display more positive reviews. The adjusted review summary is then stored on the server.

[1799] Step 11:

[1800] The server sends the adjusted review summary to the device. The device receives the summary and displays it to the user. The user sees summaries such as "battery life is good," "camera performance is excellent," and "price is high."

[1801] Step 12:

[1802] A user is considering a purchase and wants to check price information. The user makes a price comparison request on their device. The device sends this request to the server.

[1803] Step 13:

[1804] The server obtains price information from multiple purchasing sites, such as Amazon, Rakuten, and other online retailers.

[1805] Step 14:

[1806] The server takes into account point redemption and coupon application information in addition to the price information it obtains, and performs a comprehensive comparison. For example, it compiles information such as "Rakuten is the cheapest" and "Amazon offers 10% point redemption."

[1807] Step 15:

[1808] The server identifies the best place to purchase and sends information about the best purchasing site to the terminal. The terminal receives this information and displays it to the user. The user selects the best place to purchase based on information such as "site A has the lowest price" or "site B offers 10% points back," and then purchases the product.

[1809] (Application example 2)

[1810] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1811] Current online shopping systems require users to search for products, check reviews, and compare prices, which is a time-consuming and tedious process. Furthermore, while users' emotional state can affect their shopping experience, personalization is not implemented to address this. As a result, users find it difficult to achieve a satisfactory shopping experience.

[1812] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1813] In this invention, the server includes means for extracting related products from a database based on a search query received from a terminal, means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users, means for transmitting the generated optimal product list to the terminal, means for collecting word-of-mouth information about products from multiple information sources, means for analyzing the collected word-of-mouth information and generating a summary, means for transmitting the summarized word-of-mouth information to the terminal, means for acquiring and comparing product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the identified word-of-mouth information to the terminal, and means for recognizing the user's emotional state and adjusting the display content of the generated product list and word-of-mouth information based on the user's emotional state, thereby enabling users to efficiently search, compare, and consider products and enjoy an optimal shopping experience personalized based on the user's emotional state.

[1814] A "terminal" is an information processing device that allows a user to communicate with a server via the Internet.

[1815] A "search query" is a keyword or phrase that a user enters to search for specific information.

[1816] "Related products" are products extracted from a database based on a search query.

[1817] A "database" is an information management system for storing product information and user history information.

[1818] "User's past history" is data related to products that the user has searched for or purchased in the past.

[1819] "Similar user preference data" is data analyzed based on the preferences and purchasing behavior of other users.

[1820] The "optimal product list" is a list of products generated based on the user's queries, past history, and preference data of similar users.

[1821] "Information sources" are sources of information such as e-commerce sites, social media, and video sharing platforms.

[1822] "Word-of-mouth information" is information that contains evaluations and opinions about a product.

[1823] The "summary" is a concise summary of the collected word-of-mouth information.

[1824] A "purchasing site" is an online shopping site where products are sold.

[1825] "Price information" refers to product prices and discount information at each purchasing site.

[1826] An "emotional state" indicates a temporary emotional state of a user.

[1827] "Personalization" means tailoring content to a user's individual preferences and emotional state.

[1828] A system embodying this invention includes a user terminal, a server, and an information source. The user terminal is an information processing device such as a smartphone or tablet on which an online shopping application is installed. The server includes a database and an analysis engine and is responsible for various data processing. The information source may be an e-commerce site, a social networking site, a video sharing platform, or the like.

[1829] 1. System Program Overview

[1830] The server performs the following operations:

[1831] It receives a search query sent from a user's device and extracts related products from a database.

[1832] The extracted related products are compared with the user's past history and the preference data of similar users to generate an optimal product list.

[1833] The generated optimum product list is sent to the user's terminal.

[1834] Word-of-mouth information about a product is collected from multiple information sources, and the collected word-of-mouth information is analyzed to generate a summary.

[1835] The summarized word-of-mouth information is sent to the user's terminal.

[1836] To obtain product prices from multiple purchasing sites and compare the obtained price information.

[1837] The optimal purchasing site is identified based on the price information and sent to the user's terminal.

[1838] The emotional state of the user is recognized, and the generated product list and the display content of the word-of-mouth information are adjusted based on the emotional state.

[1839] 2. Natural language explanation of program processing

[1840] The server receives a search query sent from the user's device and searches a database to extract relevant product information. The database contains detailed product information, price information, and customer reviews. The server compares the extracted products with the user's past purchase history and similar users' preference data to generate an optimal product list. This allows users to quickly find products that match their preferences.

[1841] Next, the server collects review information from multiple sources, including e-commerce sites, social media, and video sharing platforms. The collected review information is analyzed using natural language processing technology to generate a summary. This summary information is then processed into a form that is easy for users to understand and sent to their devices.

[1842] The server then obtains price information from multiple purchasing sites and performs data calculations to compare prices. The compared price information takes into account point redemption and coupon application information to identify the optimal purchasing site. This information is also sent to the user's device.

[1843] To recognize the user's emotional state, the server uses an emotion engine to analyze emotions based on the user's search query and past behavioral data, and prioritizes displaying products that will help the user relax or that will pique the user's interest.

[1844] Specific hardware and software used includes database management systems (e.g., MySQL), natural language processing libraries (e.g., NLTK, spaCy), sentiment analysis engines (e.g., the transformers library), and web scraping tools for gathering pricing information (e.g., Beautiful Soup).

[1845] 3. Examples of specific examples and prompts

[1846] For example, if a user searches for "smartphone," "price range: 30,000 to 50,000 yen," and "brand: Apple," the server will extract models such as the iPhone SE or iPhone 11 that match the criteria from the database. The server will generate an optimal product list taking into account the user's past purchasing history and the preferences of similar users, and the emotion engine will analyze the user's emotions.

[1847] Example prompt sentence:

[1848] Analyze your emotional state towards the following products:

[1849] Product name: iPhone 11

[1850] User reviews: ['Good battery life', 'Excellent camera performance', 'High price']

[1851] Determine whether the user's emotional state is positive or negative.

[1852] In this way, the system provides a personalized shopping experience that takes into account the user's emotional state, allowing them to efficiently search, compare, and purchase products.

[1853] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1854] Step 1:

[1855] A user inputs a search query from a terminal. The terminal receives the query and sends it to the server. The search query includes conditions such as product category, price range, and brand.

[1856] Input: Search query including product category, price range, and brand.

[1857] Output: The search query is sent to the server.

[1858] Step 2:

[1859] The server searches a database containing detailed information about each product based on the received search query, and extracts related products that match the query.

[1860] Input: Search query.

[1861] Data processing: Matching product data in the database with queries.

[1862] Output: A list of related products.

[1863] Step 3:

[1864] The server compares the extracted related products with the user's past history and similar users' preference data to generate an optimal product list, and determines the priority of the products based on the preference data and history data.

[1865] Input: List of related products, user's past history, preference data of similar users.

[1866] Data processing: Prioritizing related product lists.

[1867] Output: A list of optimal products.

[1868] Step 4:

[1869] The server sends the generated optimal product list to the user's terminal, which displays the list on its screen.

[1870] Input: Optimal product listing.

[1871] Output: The best product list displayed on the device.

[1872] Step 5:

[1873] When a user becomes interested in a particular product, the user sends a request from the terminal to the server to check word-of-mouth information about the product.

[1874] Input: User review request.

[1875] Output: The review confirmation request is sent to the server.

[1876] Step 6:

[1877] The server collects word-of-mouth information from multiple sources (e-commerce sites, social media, vid...

Claims

1. means for extracting related products from a database based on a search query received from the terminal; A means for generating an optimal product list by comparing the extracted related products with the user's past history and preference data of similar users; means for transmitting the generated optimal product list to a terminal; A means of collecting product reviews from multiple sources; A means for analyzing the collected review information and generating a summary; means for transmitting the summarized review information to a terminal; A means to obtain and compare product prices from multiple purchasing sites, means for identifying an optimal purchasing site based on the price information and transmitting the information to the terminal; A system including:

2. The system according to claim 1, wherein the means for collecting word-of-mouth information collects information from e-commerce sites, social networking sites, and video sharing platforms.

3. 2. The system according to claim 1, wherein the price comparison means takes into consideration information on point redemption and coupon application in the acquired price information.

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