system

Abstract

We provide the system. [Solution] Means for automatically collecting data from information sources, A means of analyzing collected data and generating a summary, A means to customize summaries based on the user's past purchase history and preferences, A means of providing users with summaries and suggesting products, A system that includes means for automating the purchase process through an external database based on user selection.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional method, when a consumer purchases a product, there is a problem that it takes a great deal of time and effort to collect and compare word-of-mouth information from multiple information sources. As a result, the process until the purchase decision is often inefficient. There is a need to improve such problems, provide a system that can obtain product information more efficiently and purchase easily.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects data from information sources, analyzes the collected data, and generates a summary. Furthermore, it customizes the summary based on the user's interests and provides the summary to the user. This system includes means for automating the purchase process based on the user's selection, efficiently integrating information from multiple information sources and supporting rapid purchase decision-making.

[0006] "Information sources" refer to external data sources such as websites, APIs, social media, and video platforms that serve as the starting point for acquiring data.

[0007] "Means of automatically collecting data" refers to a mechanism in which a program or system obtains data from an information source without human intervention.

[0008] "Methods for analyzing and generating summaries" refer to the process of analyzing collected data using natural language processing, machine learning, etc., extracting important information from that data, and summarizing it concisely.

[0009] "Means of customizing based on user interests" refers to a function that changes the information provided to suit each individual user based on their past preferences and set criteria.

[0010] "Means of providing information to the user" refers to methods of presenting analyzed and customized information to the user visually or in an alert manner through a user interface.

[0011] "Methods for automating the purchase process" refer to a system that, after confirming the user's intention to purchase, automatically executes and completes a series of purchase processes. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that allows users to efficiently collect word-of-mouth information from multiple sources, review summaries, and easily complete the purchase process. In this system, the server, terminal, and user each have their own distinct roles.

[0034] The server initially automatically collects word-of-mouth and review data from sources such as e-commerce sites, comparison sites, social media, and video platforms. This collection is performed using scraping techniques and public APIs, and is designed to accommodate updates to the information sources. The collected data is stored in a database in a unified format.

[0035] Next, the server uses natural language processing and machine learning techniques to analyze the collected data and generate a summary. The analysis primarily involves sentiment analysis of the text, identifying positive and negative evaluations and extracting important keywords and phrases. The generated summary is then customized based on the user's past preferences and interests.

[0036] When a user accesses this system, the terminal displays a customized summary provided by the server. The user interface is designed to be intuitive and easy to use, allowing users to easily select detailed information on categories and products that interest them.

[0037] The user considers the products they wish to purchase based on the summary information presented by their device, and if they wish to purchase them, they click the purchase button. The server then automatically initiates the purchase process for the selected products. Using pre-registered payment information and shipping address, the server accesses an external e-commerce site and confirms the order. Once the order is complete, the server sends a confirmation notification to the user's device.

[0038] For example, if a user wants to buy a new smartphone, the system collects the latest user reviews from sources such as Amazon, Twitter, and YouTube (registered trademark), and generates a summary of its features and user experience. Based on this information, the user can find the optimal smartphone and complete the purchase on the spot. In this way, the present invention streamlines the consumer purchasing process and reduces its burden.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server references a list of e-commerce sites and social media platforms as sources of information and prepares to establish connections to access their respective URLs and API endpoints. Access may require authentication credentials, which are then configured.

[0042] Step 2:

[0043] The server collects data from the target information sources. It either uses scraping techniques to read web pages and extract the necessary information, or retrieves data using public APIs. The collected data includes information such as review text, rating scores, and posting dates.

[0044] Step 3:

[0045] The server converts the collected data into a standardized format and stores it in a database. This format conversion is performed to enable data classification and tagging, and to streamline subsequent processing.

[0046] Step 4:

[0047] The server uses natural language processing (NLP) to analyze the stored data. Sentiment analysis classifies review ratings into positive, negative, and neutral, and extracts key keywords and characteristic expressions.

[0048] Step 5:

[0049] The server customizes the extracted information based on each user's configured interests and historical data, generating personalized summaries. This allows for the highlighting and presentation of information that matches the user's interests.

[0050] Step 6:

[0051] The terminal displays a customized summary provided by the server to the user. The user interface is designed to clearly display categories and summaries for each product, allowing the user to easily refer to details.

[0052] Step 7:

[0053] Users view the provided summary information and select the products they wish to purchase. After making their selection, they proceed to the order process by pressing the purchase button.

[0054] Step 8:

[0055] The server automatically executes the purchase process on external e-commerce sites based on the user's selection. Using pre-registered payment information and shipping address, the purchase is completed more quickly.

[0056] Step 9:

[0057] After a purchase is complete, the server sends a confirmation message and order details to the user's device. This allows the user to view their purchase history and request additional support.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In modern society, consumers are confronted with a vast amount of information daily, making it difficult to efficiently gather reliable word-of-mouth information and utilize it in their purchasing decisions. Furthermore, quickly selecting products that meet individual needs based on the collected information and completing the purchase process requires considerable time and effort. Therefore, there is a need for a system that provides users with efficient information gathering, customized summaries, and a smooth purchasing process all in one place.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for automatically collecting digital information from information sources, means for analyzing the collected digital information to generate summary information, and means for personalizing the summary information based on the user's past preferences and interests. This makes it possible for users to efficiently obtain reliable word-of-mouth information and easily obtain product information that is best suited to them.

[0063] "Information sources" refer to the starting points or services for digital information provided from various platforms and media on the internet.

[0064] "Digital information" is a general term for information that is recorded and stored electronically and accessed and processed via a computer, including information in various formats such as text, images, audio, and video.

[0065] "Means of collection" refers to the technologies, devices, and methods used to automatically acquire digital information from information sources.

[0066] "Means of analysis" refers to methods and tools for analyzing collected digital information and extracting meaning and patterns.

[0067] "Summary information" refers to information that extracts the essence of the original digital information and presents it in a concise form.

[0068] "Personalization methods" refer to technologies and methods for customizing information according to the specific needs and interests of each user.

[0069] "Display means" refers to a device or method for providing information to users visually.

[0070] "Purchase-related procedures" refer to the entire process from deciding to purchase to payment and delivery arrangements.

[0071] To implement this invention, the system consists of a server, a terminal, and a user.

[0072] The server automatically collects digital information from multiple internet-connected sources. This collection utilizes programming languages ​​like Python, employing data extraction techniques such as web scraping and public application programming interfaces. Specifically, it processes web pages using the BeautifulSoup library and retrieves information by sending requests to the source's API. The collected information is structured and stored in a database such as MySQL®.

[0073] Subsequently, the server performs natural language processing, analyzing the collected digital information to generate meaningful summary information. This process utilizes Python's natural language processing library NLTK and the machine learning library TENSORFLOW®. These are used to extract reputation trends, frequently occurring phrases, and keywords. The generated summary information is then personalized based on the user's past preferences and interests.

[0074] The terminal presents personalized summary information sent from the server to the user in an intuitively understandable format. This interface was developed using the React library and is designed to allow users to easily obtain information of interest.

[0075] The user makes a purchase decision and takes action based on the summary information displayed on the terminal. The server receives this action and uses pre-registered payment information to automate the purchase-related procedures and complete the order in cooperation with an external online store.

[0076] For example, if a user wants to purchase a new mobile device, the server collects digital information from e-commerce sites and social media, and generates meaningful summary information about its features and reviews. The user can then use this information to make the best choice and complete the purchase process smoothly. This method allows users to conduct their purchasing activities more efficiently.

[0077] Example prompt for input to the generative AI model: "Collect user reviews of the latest mobile devices and summarize the evaluation trends."

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server retrieves digital information from information sources. The input consists of information from multiple platforms on the internet (e.g., e-commerce sites, social media, video sites). The server uses BeautifulSoup to scrape web pages and sends information requests using public APIs. This results in receiving the latest word-of-mouth and review information as output.

[0081] Step 2:

[0082] The server analyzes the acquired review information using natural language processing. The input is the review information collected in the previous step. Using the Python NLTK library, this text data is tokenized, sentiment analysis is performed, and keywords are extracted. As a result of the analysis, summary information of reputation trends and important words is output.

[0083] Step 3:

[0084] The server customizes the generated summary information based on the user's past preferences and interests. The input consists of the summary information and the user's profile information. The summary content is then personalized, taking into account the user settings in the database. This results in the output of personalized summary information.

[0085] Step 4:

[0086] The terminal displays personalized summary information. The input is personalized summary information received from the server. An interface built using React displays this information in an intuitive and easy-to-understand manner. Visually organized summary information is output on the terminal.

[0087] Step 5:

[0088] Users evaluate summary information on their devices and make purchase decisions as needed. The input is the information displayed on the device. When a user decides to purchase and clicks the purchase button, that information is sent to the server. This transmits the purchasing behavior as input to the server.

[0089] Step 6:

[0090] The server automates purchase-related procedures based on the user's purchase intent. The input is the user's purchase intent information. The server uses pre-registered payment information and calls the online store's API to confirm the purchase. Finally, an order confirmation notification is output to the user's terminal.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In traditional online shopping, gathering customer reviews and selecting products was time-consuming, making it difficult for users to choose the best product for them. Furthermore, manually gathering and analyzing information from numerous sources was highly inefficient, reducing user convenience. Additionally, the automation of the purchase process was insufficient, highlighting the need for improved user experience.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for customizing the summary based on the user's past purchase history and preferences. This enables the user to efficiently select the products best suited to them and to complete the purchase process quickly and easily.

[0096] "Information sources" refers to the collective term for websites, social media platforms, or other online data-providing entities used to collect data.

[0097] "Data decomposition techniques" are automated methods or tools used to extract necessary information from specific online resources.

[0098] A "public interface" is a standardized communication method that external systems can use to acquire or transmit information.

[0099] A "generated language model" is an algorithm trained to perform natural language processing based on a large amount of text data.

[0100] "Natural language processing" is a technology that allows computers to understand and analyze natural language used by humans, and to extract or generate appropriate information.

[0101] "High-frequency expressions" are words or phrases that appear particularly frequently within a specific dataset in data analysis.

[0102] "Opinion trends" refer to information that indicates the overall direction of evaluation based on analyzed word-of-mouth data.

[0103] An "external database" is a storage system that stores information located externally and accesses it as needed.

[0104] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0105] First, the server automatically collects data from information sources. This data collection utilizes data decomposition techniques and public interfaces. Specifically, this makes it possible to efficiently obtain reviews and word-of-mouth information from e-commerce sites and social media. The server stores the collected data in a database, analyzes it using natural language processing, and extracts opinion trends and high-frequency expressions. The generated language model is then used to form summaries of information that users may find interesting.

[0106] Based on the analysis results, the server customizes the summary considering the user's past purchase history and preferences, and sends it to the terminal. The terminal provides the received summary to the user and suggests products according to the user's selection. For products selected by the user, the purchase process is automated through an external database, enabling quick purchases.

[0107] For example, when a user is considering purchasing a new gadget, they might enter a prompt message through a smartphone app saying, "I'd like you to collect and summarize reviews of the latest headphones and suggest purchase options that prioritize sound quality and usability." The system then automatically collects and analyzes the information, assisting the user in selecting and purchasing the most suitable product.

[0108] This entire system is implemented using hardware and software such as Python, BeautifulSoup, and TensorFlow, with the aim of improving the user experience.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server receives a user prompt and automatically collects relevant data from its sources. The input is a user prompt requesting "collect and summarize reviews of the latest headphones, and provide purchase recommendations focusing on sound quality and usability." The output is review data from e-commerce sites, social media, and video platforms. This data collection utilizes data decomposition techniques and a public interface.

[0112] Step 2:

[0113] The server stores the acquired review data in a database and performs natural language processing. The input is the review data collected in step 1, and the output is an analysis result that extracts opinion trends and high-frequency expressions. Specifically, a generative AI model is used to analyze evaluation trends and extract keywords.

[0114] Step 3:

[0115] The server generates a summary based on the analysis results and customizes it based on the user's past purchase history and preferences. The input is the analysis results from step 2 and the user's purchase history data, and the output is a product summary customized for the user. This summary takes the user's interests and preferences into account and includes more relevant information.

[0116] Step 4:

[0117] The server sends a customized summary to the terminal. The input is the summary generated in step 3, and the output is data for display on the user's terminal. The terminal provides the received summary to the user, enabling intuitive visualization and manipulation.

[0118] Step 5:

[0119] The user selects products based on the summary information provided on the terminal and proceeds with the purchase. The input is the product summary displayed on the terminal, and the output is the information of the selected products sent to the server, initiating the purchase process. Specifically, clicking the purchase button confirms the order through an external database.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is a system that efficiently collects, analyzes, summarizes, and appropriately customizes word-of-mouth information obtained by users from multiple sources, and further provides an experience that takes into account the user's emotions. In particular, it incorporates an emotion engine that recognizes the user's emotional state and adjusts the information provided according to those emotions.

[0122] The server first automatically collects data from information sources such as e-commerce sites and social media. It uses scraping techniques and public APIs to obtain reviews, comments, and rating data, and stores this information in a database. The database then integrates the information into a unified format for efficient management.

[0123] Subsequently, the server uses natural language processing technology to analyze the collected data and generate a summary. During the analysis process, the sentiment engine continues to operate, ranking and highlighting content based on the user's current emotions and preferences. The user's emotions are inferred from interaction data and recent behavior.

[0124] The terminal displays a customized summary generated by the server to the user. The displayed information dynamically changes based on the user's emotions; if positive, the features of the new product are emphasized, while if somewhat negative, risk and pricing information is given more weight. The user interface is also configured to include visuals and tones that reflect emotions.

[0125] Users can consider purchasing a product based on the provided summary information. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated, including variables that may be influenced by the emotion engine's requests.

[0126] For example, if a user is considering purchasing a wristwatch and has just been browsing luxury jewelry, the system will recognize this excitement and anticipation and prioritize presenting information and reviews of high-end watches that match those feelings. Based on this, the user can make the optimal choice and easily complete the purchase. By understanding and applying emotions, this invention makes it possible to further personalize the user's purchasing experience and improve satisfaction.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server refers to a list of e-commerce sites and social networking platforms that serve as information sources and connects to their respective URLs or API endpoints. If necessary, it prepares for access using authentication information such as API keys.

[0130] Step 2:

[0131] The server collects data from information sources. It utilizes scraping techniques and public APIs to obtain review, rating, and comment data. The collected data is stored in a database as text.

[0132] Step 3:

[0133] The server uses natural language processing (NLP) techniques to analyze the collected data. Here, it analyzes text data to determine sentiment, classifies reviews as positive, negative, or neutral, and extracts important keywords.

[0134] Step 4:

[0135] The server generates a summary based on the analyzed data. This summary is customized to take into account the user's past behavior history and interests. For example, it may highlight the features of products that the user has previously given high ratings to create a personalized summary.

[0136] Step 5:

[0137] The server uses an emotion engine to recognize the user's current emotional state. Based on the user's recent actions and choices, it sets emotional parameters and plans to present information appropriate to that state.

[0138] Step 6:

[0139] The device displays a customized summary provided by the server to the user. The display format changes according to the user's emotions; for example, a simple and minimalist visual is chosen when the user is relaxed, while an impactful design is used when the user is excited.

[0140] Step 7:

[0141] Users can view summaries displayed on their devices, further explore information that interests them, and select products. After making their selection, they can proceed with the purchase using the purchase button.

[0142] Step 8:

[0143] The server integrates with external e-commerce sites to automate the purchase process for the products selected by the user. It uses registered payment information and shipping addresses to ensure the order is completed quickly.

[0144] Step 9:

[0145] The server sends a confirmation message to the user's device after the purchase is complete. This message includes order details and an estimated delivery date, allowing the user to easily manage their purchase history.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] In modern society, users need to quickly obtain the most useful information from a vast amount of data. However, because information sources are so diverse, manually checking each one is extremely time-consuming. Furthermore, users cannot obtain information that is appropriate to their situation or emotional state, leading to inefficiencies in information gathering and decision-making. In addition, in purchasing behavior, the lack of sufficient personalization based on user emotions makes it difficult to enhance user satisfaction.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes means for automatically collecting data from information sources, means for formatting the collected data into a unified format, and means for analyzing the data and generating a summary that takes into account the user's emotional state. As a result, the user receives an information summary customized according to their emotional state based on data automatically collected from diverse information sources, enabling more efficient and personalized information gathering and decision-making.

[0151] "Information sources" refer to online platforms such as e-commerce sites and social media where users can obtain word-of-mouth, reviews, and rating data.

[0152] "Data collection" refers to the process of automatically obtaining necessary data from information sources, and is carried out using scraping techniques and public APIs.

[0153] "Format conversion" refers to the process of converting data in different formats into a unified format, a step that enables effective data integration and management.

[0154] "Summary generation" refers to the process of extracting essential information from analyzed data and expressing it in a concise form.

[0155] An "emotion engine" refers to a system component that analyzes a user's past behavior and real-time interactions to infer their emotional state.

[0156] "Customization" refers to the process of dynamically adjusting the information provided based on the user's emotions and preferences.

[0157] "Feedback" refers to the process by which a system adjusts its behavior based on user interaction data, with the aim of improving the user experience.

[0158] "Automation" refers to the ability of a system to complete a series of processes independently, minimizing manual intervention by the user.

[0159] This invention is a system that efficiently collects word-of-mouth information obtained by users from multiple sources, analyzes and summarizes it, customizes it appropriately, and provides an experience that takes into account the user's emotions.

[0160] The server first automatically collects data from sources such as e-commerce sites and social media. This process utilizes scraping techniques and public APIs. This retrieves review, comment, and rating data, which are then stored in a database on the server. The database converts and integrates the collected data into a unified format for efficient management.

[0161] Next, the server analyzes the collected data using natural language processing technology. This analysis utilizes a generative AI model, enabling the generation of summaries that take user emotions into account. The server also employs an emotion engine, analyzing emotion patterns extracted from the user's past behavior data and real-time interactions to emphasize and prioritize information according to the user's emotions.

[0162] The generated, customized summary is displayed to the user through their device. In this process, the information dynamically changes according to the user's emotional state. For example, if positive emotions are detected, the features of the new product are highlighted, while if negative emotions are present, risk and pricing information takes precedence. This makes the user experience more personalized.

[0163] Users can consider purchasing a product based on the summary information provided. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated within the server, assisted by an emotion engine.

[0164] For example, if a user inputs the prompt "I'm looking for a highly-rated smartwatch" into the AI ​​model, the system will collect, analyze, and summarize information on relevant highly-rated products before providing it to the user. This information will be adjusted according to the user's emotional state and preferences to support the optimal purchasing decision.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server collects data from information sources. Specifically, it extracts review, comment, and rating data from e-commerce sites and social media using scraping techniques and APIs. The input in this process is the information source, and the output is raw data stored in the server's storage. The server temporarily holds this data in preparation for subsequent processing.

[0168] Step 2:

[0169] The server converts the collected raw data into a standardized format. At this stage, the input is the raw data obtained in step 1, and the necessary data processing is performed through the ETL process. Specifically, the data is cleaned, processed, and transformed, and the standardized data is stored in the database as output. The server eliminates data redundancy and manages the information efficiently.

[0170] Step 3:

[0171] The server analyzes the data using a generative AI model and infers the user's emotions using an emotion engine. This process uses the standardized data saved in step 2 as input. Natural language processing techniques are employed to extract evaluation trends and frequently occurring keywords, and the data is highlighted and ranked according to the user's emotions. The output is a customized summary generated with the user's emotions in mind.

[0172] Step 4:

[0173] The terminal displays a summary received from the server to the user. The input is the customized summary, which is the output from step 3, and this is used as the basis for dynamic information display. For example, based on the user's emotions, new product features may be emphasized or risk information may be prioritized. The terminal is designed to display information that reflects emotions visually as well.

[0174] Step 5:

[0175] The user considers the product based on the summary information displayed on the device and decides whether to purchase it. The input is the customized summary presented in step 4, and the output is the final purchase action. When the user clicks the purchase button, the purchase process based on the user's selection is automated within the server. The user's interaction data is also sent back to the server as feedback and used to analyze emotional patterns.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] In modern online shopping, it is difficult for users to obtain the information they need quickly and accurately. Furthermore, there is a need to dynamically customize information according to the user's emotional state and preferences to increase their purchasing intent. However, conventional systems lack the means to provide emotionally conscious information and automate the user's purchasing process, resulting in a limited user experience and difficulty in improving satisfaction.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for dynamically customizing the summary based on the user's emotional state and interests. This enables the user to make purchasing decisions based on information tailored to their own emotional state, allowing them to enjoy a more personalized experience.

[0181] "Information sources" refer to digital data obtained from online platforms and social networks.

[0182] "Means of automatically collecting data" refers to technologies for mechanically obtaining information from the internet, including, for example, scraping techniques and available APIs.

[0183] "Means of generating summaries" refers to techniques for analyzing collected information and expressing its essence in a shortened form.

[0184] "User emotional state" refers to the psychological state of the user, inferred through their interaction with the system and their actions.

[0185] "Means for dynamically customizing summaries" refers to technologies that adjust the summaries provided based on the user's current emotional state and interests.

[0186] "Purchase decision" refers to the final decision a user makes to purchase a product or service.

[0187] A "personalized experience" refers to a customized user experience that is tailored to the specific needs and preferences of individual users.

[0188] To implement this invention, the server first automatically collects data from information sources. The server systematically collects review, comment, and rating data from e-commerce sites and social networks using web scraping techniques and publicly available APIs. The collected data is stored in a database and integrated into a consistent format.

[0189] Next, the server analyzes the collected data using natural language processing and generates a summary. It performs text analysis using a Python natural language processing library (e.g., NLTK) to extract evaluation trends and frequently occurring words. Furthermore, an emotion engine analyzes the user's current emotional state and dynamically customizes the summary for each user based on that information. The user's emotional state is inferred from interface touches and voice input.

[0190] Subsequently, the device presents a customized summary to the user. Smartphones are primarily used as the device, and the summarized information is displayed through a graphical user interface, tailored to the user's emotional state. If the emotional state is determined to be agitated, the display method and tone of the information are adjusted, prioritizing positive reviews and highly-rated products.

[0191] For example, if a user is looking to buy a new camera and their recent search history suggests they are planning a trip, the system will prioritize displaying product reviews and ratings in the "Cameras Best for Travel" category. This allows the user to make the best product choice for their situation.

[0192] Examples of prompt statements include:

[0193] "This user is looking for a travel backpack and is in a cheerful mood. Please generate a product review that is suitable for a cheerful mood."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server automatically collects data from information sources. It accepts URLs and API keys from e-commerce sites and social networks as input, and retrieves raw review and comment data as output. The process involves using scraping techniques and public APIs to collect data, structuring it, and storing it in a database.

[0197] Step 2:

[0198] The server uses natural language processing techniques to analyze collected data and generate summaries. It receives stored review and comment data as input and generates summary data with key information extracted as output. This includes using Python natural language processing libraries (such as NLTK) to perform text analysis and identify evaluation trends and frequently occurring keywords.

[0199] Step 3:

[0200] The server uses an emotion engine to analyze the user's emotional state. It receives user interaction data and recent behavioral history as input and generates information about the user's emotional state as output. The data is analyzed via an emotion analysis API to estimate the user's psychological state.

[0201] Step 4:

[0202] The server dynamically customizes summaries based on the user's emotional state and interests. It receives generated summary data and emotional information as input, and outputs a customized summary ranked according to the user's emotions. This includes determining which information to prioritize and adjusting the tone of the information.

[0203] Step 5:

[0204] The device presents a customized summary to the user. It receives a customized summary from the server as input and displays it to the user in a visualized form as output. This process, delivered through the user interface of a smartphone or other device, enables the display of information in a way that responds to the user's emotions.

[0205] Step 6:

[0206] The user makes a purchase decision based on the displayed summary. The input involves accepting the presented customized summary and their own feelings and needs, while the output involves selecting the necessary products and performing the purchase operation. This includes a decision-making process for selecting the optimal product and the action of completing the purchase procedure by clicking the purchase button.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention provides a system that allows users to efficiently collect word-of-mouth information from multiple sources, review summaries, and easily complete the purchase process. In this system, the server, terminal, and user each have their own distinct roles.

[0224] The server initially automatically collects word-of-mouth and review data from sources such as e-commerce sites, comparison sites, social media, and video platforms. This collection is performed using scraping techniques and public APIs, and is designed to accommodate updates to the information sources. The collected data is stored in a database in a unified format.

[0225] Next, the server uses natural language processing and machine learning techniques to analyze the collected data and generate a summary. The analysis primarily involves sentiment analysis of the text, identifying positive and negative evaluations and extracting important keywords and phrases. The generated summary is then customized based on the user's past preferences and interests.

[0226] When a user accesses this system, the terminal displays a customized summary provided by the server. The user interface is designed to be intuitive and easy to use, allowing users to easily select detailed information on categories and products that interest them.

[0227] The user considers the products they wish to purchase based on the summary information presented by their device, and if they wish to purchase them, they click the purchase button. The server then automatically initiates the purchase process for the selected products. Using pre-registered payment information and shipping address, the server accesses an external e-commerce site and confirms the order. Once the order is complete, the server sends a confirmation notification to the user's device.

[0228] For example, if a user wants to buy a new smartphone, the system collects the latest user reviews from sources such as Amazon, Twitter, and YouTube, and generates a summary of its features and user experience. Based on this information, the user can find the optimal smartphone and complete the purchase on the spot. In this way, the present invention streamlines the consumer purchasing process and reduces its burden.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server references a list of e-commerce sites and social media platforms as sources of information and prepares to establish connections to access their respective URLs and API endpoints. Access may require authentication credentials, which are then configured.

[0232] Step 2:

[0233] The server collects data from the target information sources. It either uses scraping techniques to read web pages and extract the necessary information, or retrieves data using public APIs. The collected data includes information such as review text, rating scores, and posting dates.

[0234] Step 3:

[0235] The server converts the collected data into a standardized format and stores it in a database. This format conversion is performed to enable data classification and tagging, and to streamline subsequent processing.

[0236] Step 4:

[0237] The server uses natural language processing (NLP) to analyze the stored data. Sentiment analysis classifies review ratings into positive, negative, and neutral, and extracts key keywords and characteristic expressions.

[0238] Step 5:

[0239] The server customizes the extracted information based on each user's configured interests and historical data, generating personalized summaries. This allows for the highlighting and presentation of information that matches the user's interests.

[0240] Step 6:

[0241] The terminal displays a customized summary provided by the server to the user. The user interface is designed to clearly display categories and summaries for each product, allowing the user to easily refer to details.

[0242] Step 7:

[0243] Users view the provided summary information and select the products they wish to purchase. After making their selection, they proceed to the order process by pressing the purchase button.

[0244] Step 8:

[0245] The server automatically executes the purchase process on external e-commerce sites based on the user's selection. Using pre-registered payment information and shipping address, the purchase is completed more quickly.

[0246] Step 9:

[0247] After a purchase is complete, the server sends a confirmation message and order details to the user's device. This allows the user to view their purchase history and request additional support.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] In modern society, consumers are confronted with a vast amount of information daily, making it difficult to efficiently gather reliable word-of-mouth information and utilize it in their purchasing decisions. Furthermore, quickly selecting products that meet individual needs based on the collected information and completing the purchase process requires considerable time and effort. Therefore, there is a need for a system that provides users with efficient information gathering, customized summaries, and a smooth purchasing process all in one place.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for automatically collecting digital information from information sources, means for analyzing the collected digital information to generate summary information, and means for personalizing the summary information based on the user's past preferences and interests. This makes it possible for users to efficiently obtain reliable word-of-mouth information and easily obtain product information that is best suited to them.

[0253] "Information sources" refer to the starting points or services for digital information provided from various platforms and media on the internet.

[0254] "Digital information" is a general term for information that is recorded and stored electronically and accessed and processed via a computer, including information in various formats such as text, images, audio, and video.

[0255] "Means of collection" refers to the technologies, devices, and methods used to automatically acquire digital information from information sources.

[0256] "Means of analysis" refers to methods and tools for analyzing collected digital information and extracting meaning and patterns.

[0257] "Summary information" refers to information that extracts the essence of the original digital information and presents it in a concise form.

[0258] "Personalization methods" refer to technologies and methods for customizing information according to the specific needs and interests of each user.

[0259] "Display means" refers to a device or method for providing information to users visually.

[0260] "Purchase-related procedures" refer to the entire process from deciding to purchase to payment and delivery arrangements.

[0261] To implement this invention, the system consists of a server, a terminal, and a user.

[0262] The server automatically collects digital information from multiple internet-connected sources. This collection utilizes programming languages ​​like Python, employing data extraction techniques such as web scraping and public application programming interfaces. Specifically, it processes web pages using the BeautifulSoup library and retrieves information by sending requests to the source's API. The collected information is structured and stored in a database such as MySQL.

[0263] Subsequently, the server performs natural language processing, analyzing the collected digital information to generate meaningful summary information. This process utilizes Python's natural language processing library NLTK and the machine learning library TensorFlow. These are used to extract sentiment trends, frequently occurring phrases, and keywords. The generated summary information is then personalized based on the user's past preferences and interests.

[0264] The terminal presents personalized summary information sent from the server to the user in an intuitively understandable format. This interface was developed using the React library and is designed to allow users to easily obtain information of interest.

[0265] The user makes a purchase decision and takes action based on the summary information displayed on the terminal. The server receives this action and uses pre-registered payment information to automate the purchase-related procedures and complete the order in cooperation with an external online store.

[0266] For example, if a user wants to purchase a new mobile device, the server collects digital information from e-commerce sites and social media, and generates meaningful summary information about its features and reviews. The user can then use this information to make the best choice and complete the purchase process smoothly. This method allows users to conduct their purchasing activities more efficiently.

[0267] Example prompt for input to the generative AI model: "Collect user reviews of the latest mobile devices and summarize the evaluation trends."

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The server retrieves digital information from information sources. The input consists of information from multiple platforms on the internet (e.g., e-commerce sites, social media, video sites). The server uses BeautifulSoup to scrape web pages and sends information requests using public APIs. This results in receiving the latest word-of-mouth and review information as output.

[0271] Step 2:

[0272] The server analyzes the acquired review information using natural language processing. The input is the review information collected in the previous step. Using the Python NLTK library, this text data is tokenized, sentiment analysis is performed, and keywords are extracted. As a result of the analysis, summary information of reputation trends and important words is output.

[0273] Step 3:

[0274] The server customizes the generated summary information based on the user's past preferences and interests. The input consists of the summary information and the user's profile information. The summary content is then personalized, taking into account the user settings in the database. This results in the output of personalized summary information.

[0275] Step 4:

[0276] The terminal displays personalized summary information. The input is personalized summary information received from the server. An interface built using React displays this information in an intuitive and easy-to-understand manner. Visually organized summary information is output on the terminal.

[0277] Step 5:

[0278] Users evaluate summary information on their devices and make purchase decisions as needed. The input is the information displayed on the device. When a user decides to purchase and clicks the purchase button, that information is sent to the server. This transmits the purchasing behavior as input to the server.

[0279] Step 6:

[0280] The server automates purchase-related procedures based on the user's purchase intent. The input is the user's purchase intent information. The server uses pre-registered payment information and calls the online store's API to confirm the purchase. Finally, an order confirmation notification is output to the user's terminal.

[0281] (Application Example 1)

[0282] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0283] In traditional online shopping, collecting word-of-mouth information and selecting products took a long time, making it difficult for users to select the most suitable products for themselves. In addition, manually collecting and analyzing information from multiple information sources was very inefficient and was a factor reducing user convenience. Furthermore, the automation of the purchase procedure was insufficient, and an improvement in the user experience was required.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for customizing the summary based on the user's past purchase history and preferences. As a result, the user can efficiently select the most suitable products for themselves and can perform the purchase procedure quickly and easily.

[0286] The "information source" is a general term for a website, social media platform, or other online data providing entity used to collect data.

[0287] The "data decomposition technology" is an automated method or tool used to obtain necessary information from specific online resources.

[0288] The "public interface" is a standardized communication method that an external system can use to obtain or transmit information.

[0289] The "generated language model" is an algorithm trained to perform natural language processing based on a large amount of text data.

[0290] "Natural language processing" is a technology in which a computer understands and analyzes the natural language used by humans and extracts or generates appropriate information.

[0291] "High-frequency expressions" are words or phrases that appear particularly frequently within a specific dataset in data analysis.

[0292] "Opinion trends" refer to information that indicates the overall direction of evaluation based on analyzed word-of-mouth data.

[0293] An "external database" is a storage system that stores information located externally and accesses it as needed.

[0294] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0295] First, the server automatically collects data from information sources. This data collection utilizes data decomposition techniques and public interfaces. Specifically, this makes it possible to efficiently obtain reviews and word-of-mouth information from e-commerce sites and social media. The server stores the collected data in a database, analyzes it using natural language processing, and extracts opinion trends and high-frequency expressions. The generated language model is then used to form summaries of information that users may find interesting.

[0296] Based on the analysis results, the server customizes the summary considering the user's past purchase history and preferences, and sends it to the terminal. The terminal provides the received summary to the user and suggests products according to the user's selection. For products selected by the user, the purchase process is automated through an external database, enabling quick purchases.

[0297] For example, when a user is considering purchasing a new gadget, they might enter a prompt message through a smartphone app saying, "I'd like you to collect and summarize reviews of the latest headphones and suggest purchase options that prioritize sound quality and usability." The system then automatically collects and analyzes the information, assisting the user in selecting and purchasing the most suitable product.

[0298] This entire system is implemented using hardware and software such as Python, BeautifulSoup, and TensorFlow, with the aim of improving the user experience.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server receives a user prompt and automatically collects relevant data from its sources. The input is a user prompt requesting "collect and summarize reviews of the latest headphones, and provide purchase recommendations focusing on sound quality and usability." The output is review data from e-commerce sites, social media, and video platforms. This data collection utilizes data decomposition techniques and a public interface.

[0302] Step 2:

[0303] The server stores the acquired review data in a database and performs natural language processing. The input is the review data collected in step 1, and the output is an analysis result that extracts opinion trends and high-frequency expressions. Specifically, a generative AI model is used to analyze evaluation trends and extract keywords.

[0304] Step 3:

[0305] The server generates a summary based on the analysis results and customizes it based on the user's past purchase history and preferences. The input is the analysis results from step 2 and the user's purchase history data, and the output is a product summary customized for the user. This summary takes the user's interests and preferences into account and includes more relevant information.

[0306] Step 4:

[0307] The server sends the customized summary to the terminal. The input is the summary generated in step 3, and as output, data for display on the user's terminal is obtained. The terminal provides the received summary to the user, enabling intuitive visualization and operations.

[0308] Step 5:

[0309] Based on the summary information provided on the terminal, the user selects a product and proceeds with the purchase procedure. The input is the product summary displayed on the terminal, and as output, information on the selected product is sent to the server, and the purchase procedure begins. Specifically, when the purchase button is clicked, an order is confirmed through an external database. <S

[0310] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0311] The present invention is a system that efficiently collects, analyzes, summarizes, and appropriately customizes word-of-mouth information obtained by a user from multiple information sources, and further provides an experience that takes into account the user's emotions. In particular, an emotion engine is incorporated, which recognizes the user's emotional state and adjusts information provision according to the emotion.

[0312] First, the server automatically collects data from information sources such as e-commerce sites and SNSs. Using scraping technology and public APIs, reviews, comments, evaluation data, etc. are acquired and stored in a database. The database integrates the information into a unified format for efficient management. [[ID=二十一]] [[ID=二十二]]

[0313] [[ID=二十三]] [[ID=二十四]]After that, the server analyzes the collected data using natural language processing technology and generates a summary. In the analysis process, the emotion engine continues to act, ranking and emphasizing content based on the user's current emotions and preferences. The user's emotions are inferred from interaction data and recent actions.

[0314] The terminal displays a customized summary generated by the server to the user. The displayed information dynamically changes based on the user's emotions; if positive, the features of the new product are emphasized, while if somewhat negative, risk and pricing information is given more weight. The user interface is also configured to include visuals and tones that reflect emotions.

[0315] Users can consider purchasing a product based on the provided summary information. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated, including variables that may be influenced by the emotion engine's requests.

[0316] For example, if a user is considering purchasing a wristwatch and has just been browsing luxury jewelry, the system will recognize this excitement and anticipation and prioritize presenting information and reviews of high-end watches that match those feelings. Based on this, the user can make the optimal choice and easily complete the purchase. By understanding and applying emotions, this invention makes it possible to further personalize the user's purchasing experience and improve satisfaction.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server refers to a list of e-commerce sites and social networking platforms that serve as information sources and connects to their respective URLs or API endpoints. If necessary, it prepares for access using authentication information such as API keys.

[0320] Step 2:

[0321] The server collects data from information sources. It utilizes scraping techniques and public APIs to obtain review, rating, and comment data. The collected data is stored in a database as text.

[0322] Step 3:

[0323] The server uses natural language processing (NLP) techniques to analyze the collected data. Here, it analyzes text data to determine sentiment, classifies reviews as positive, negative, or neutral, and extracts important keywords.

[0324] Step 4:

[0325] The server generates a summary based on the analyzed data. This summary is customized to take into account the user's past behavior history and interests. For example, it may highlight the features of products that the user has previously given high ratings to create a personalized summary.

[0326] Step 5:

[0327] The server uses an emotion engine to recognize the user's current emotional state. Based on the user's recent actions and choices, it sets emotional parameters and plans to present information appropriate to that state.

[0328] Step 6:

[0329] The device displays a customized summary provided by the server to the user. The display format changes according to the user's emotions; for example, a simple and minimalist visual is chosen when the user is relaxed, while an impactful design is used when the user is excited.

[0330] Step 7:

[0331] Users can view summaries displayed on their devices, further explore information that interests them, and select products. After making their selection, they can proceed with the purchase using the purchase button.

[0332] Step 8:

[0333] The server integrates with external e-commerce sites to automate the purchase process for the products selected by the user. It uses registered payment information and shipping addresses to ensure the order is completed quickly.

[0334] Step 9:

[0335] The server sends a confirmation message to the user's device after the purchase is complete. This message includes order details and an estimated delivery date, allowing the user to easily manage their purchase history.

[0336] (Example 2)

[0337] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0338] In modern society, users need to quickly obtain the most useful information from a vast amount of data. However, because information sources are so diverse, manually checking each one is extremely time-consuming. Furthermore, users cannot obtain information that is appropriate to their situation or emotional state, leading to inefficiencies in information gathering and decision-making. In addition, in purchasing behavior, the lack of sufficient personalization based on user emotions makes it difficult to enhance user satisfaction.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes means for automatically collecting data from information sources, means for formatting the collected data into a unified format, and means for analyzing the data and generating a summary that takes into account the user's emotional state. As a result, the user receives an information summary customized according to their emotional state based on data automatically collected from diverse information sources, enabling more efficient and personalized information gathering and decision-making.

[0341] "Information sources" refer to online platforms such as e-commerce sites and social media where users can obtain word-of-mouth, reviews, and rating data.

[0342] "Data collection" refers to the process of automatically obtaining necessary data from information sources, and is carried out using scraping techniques and public APIs.

[0343] "Format conversion" refers to the process of converting data in different formats into a unified format, a step that enables effective data integration and management.

[0344] "Summary generation" refers to the process of extracting essential information from analyzed data and expressing it in a concise form.

[0345] An "emotion engine" refers to a system component that analyzes a user's past behavior and real-time interactions to infer their emotional state.

[0346] "Customization" refers to the process of dynamically adjusting the information provided based on the user's emotions and preferences.

[0347] "Feedback" refers to the process by which a system adjusts its behavior based on user interaction data, with the aim of improving the user experience.

[0348] "Automation" refers to the ability of a system to complete a series of processes independently, minimizing manual intervention by the user.

[0349] This invention is a system that efficiently collects word-of-mouth information obtained by users from multiple sources, analyzes and summarizes it, customizes it appropriately, and provides an experience that takes into account the user's emotions.

[0350] The server first automatically collects data from sources such as e-commerce sites and social media. This process utilizes scraping techniques and public APIs. This retrieves review, comment, and rating data, which are then stored in a database on the server. The database converts and integrates the collected data into a unified format for efficient management.

[0351] Next, the server analyzes the collected data using natural language processing technology. This analysis utilizes a generative AI model, enabling the generation of summaries that take user emotions into account. The server also employs an emotion engine, analyzing emotion patterns extracted from the user's past behavior data and real-time interactions to emphasize and prioritize information according to the user's emotions.

[0352] The generated, customized summary is displayed to the user through their device. In this process, the information dynamically changes according to the user's emotional state. For example, if positive emotions are detected, the features of the new product are highlighted, while if negative emotions are present, risk and pricing information takes precedence. This makes the user experience more personalized.

[0353] Users can consider purchasing a product based on the summary information provided. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated within the server, assisted by an emotion engine.

[0354] For example, if a user inputs the prompt "I'm looking for a highly-rated smartwatch" into the AI ​​model, the system will collect, analyze, and summarize information on relevant highly-rated products before providing it to the user. This information will be adjusted according to the user's emotional state and preferences to support the optimal purchasing decision.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The server collects data from information sources. Specifically, it extracts review, comment, and rating data from e-commerce sites and social media using scraping techniques and APIs. The input in this process is the information source, and the output is raw data stored in the server's storage. The server temporarily holds this data in preparation for subsequent processing.

[0358] Step 2:

[0359] The server converts the collected raw data into a standardized format. At this stage, the input is the raw data obtained in step 1, and the necessary data processing is performed through the ETL process. Specifically, the data is cleaned, processed, and transformed, and the standardized data is stored in the database as output. The server eliminates data redundancy and manages the information efficiently.

[0360] Step 3:

[0361] The server analyzes the data using a generative AI model and infers the user's emotions using an emotion engine. This process uses the standardized data saved in step 2 as input. Natural language processing techniques are employed to extract evaluation trends and frequently occurring keywords, and the data is highlighted and ranked according to the user's emotions. The output is a customized summary generated with the user's emotions in mind.

[0362] Step 4:

[0363] The terminal displays a summary received from the server to the user. The input is the customized summary, which is the output from step 3, and this is used as the basis for dynamic information display. For example, based on the user's emotions, new product features may be emphasized or risk information may be prioritized. The terminal is designed to display information that reflects emotions visually as well.

[0364] Step 5:

[0365] The user considers the product based on the summary information displayed on the device and decides whether to purchase it. The input is the customized summary presented in step 4, and the output is the final purchase action. When the user clicks the purchase button, the purchase process based on the user's selection is automated within the server. The user's interaction data is also sent back to the server as feedback and used to analyze emotional patterns.

[0366] (Application Example 2)

[0367] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0368] In modern online shopping, it is difficult for users to obtain the information they need quickly and accurately. Furthermore, there is a need to dynamically customize information according to the user's emotional state and preferences to increase their purchasing intent. However, conventional systems lack the means to provide emotionally conscious information and automate the user's purchasing process, resulting in a limited user experience and difficulty in improving satisfaction.

[0369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0370] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for dynamically customizing the summary based on the user's emotional state and interests. This enables the user to make purchasing decisions based on information tailored to their own emotional state, allowing them to enjoy a more personalized experience.

[0371] "Information sources" refer to digital data obtained from online platforms and social networks.

[0372] "Means of automatically collecting data" refers to technologies for mechanically obtaining information from the internet, including, for example, scraping techniques and available APIs.

[0373] "Means of generating summaries" refers to techniques for analyzing collected information and expressing its essence in a shortened form.

[0374] "User emotional state" refers to the psychological state of the user, inferred through their interaction with the system and their actions.

[0375] "Means for dynamically customizing summaries" refers to technologies that adjust the summaries provided based on the user's current emotional state and interests.

[0376] "Purchase decision" refers to the final decision a user makes to purchase a product or service.

[0377] A "personalized experience" refers to a customized user experience that is tailored to the specific needs and preferences of individual users.

[0378] To implement this invention, the server first automatically collects data from information sources. The server systematically collects review, comment, and rating data from e-commerce sites and social networks using web scraping techniques and publicly available APIs. The collected data is stored in a database and integrated into a consistent format.

[0379] Next, the server analyzes the collected data using natural language processing and generates a summary. It performs text analysis using a Python natural language processing library (e.g., NLTK) to extract evaluation trends and frequently occurring words. Furthermore, an emotion engine analyzes the user's current emotional state and dynamically customizes the summary for each user based on that information. The user's emotional state is inferred from interface touches and voice input.

[0380] Subsequently, the device presents a customized summary to the user. Smartphones are primarily used as the device, and the summarized information is displayed through a graphical user interface, tailored to the user's emotional state. If the emotional state is determined to be agitated, the display method and tone of the information are adjusted, prioritizing positive reviews and highly-rated products.

[0381] For example, if a user is looking to buy a new camera and their recent search history suggests they are planning a trip, the system will prioritize displaying product reviews and ratings in the "Cameras Best for Travel" category. This allows the user to make the best product choice for their situation.

[0382] Examples of prompt statements include:

[0383] "This user is looking for a travel backpack and is in a cheerful mood. Please generate a product review that is suitable for a cheerful mood."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server automatically collects data from information sources. It accepts URLs and API keys from e-commerce sites and social networks as input, and retrieves raw review and comment data as output. The process involves using scraping techniques and public APIs to collect data, structuring it, and storing it in a database.

[0387] Step 2:

[0388] The server uses natural language processing techniques to analyze collected data and generate summaries. It receives stored review and comment data as input and generates summary data with key information extracted as output. This includes using Python natural language processing libraries (such as NLTK) to perform text analysis and identify evaluation trends and frequently occurring keywords.

[0389] Step 3:

[0390] The server uses an emotion engine to analyze the user's emotional state. It receives user interaction data and recent behavioral history as input and generates information about the user's emotional state as output. The data is analyzed via an emotion analysis API to estimate the user's psychological state.

[0391] Step 4:

[0392] The server dynamically customizes summaries based on the user's emotional state and interests. It receives generated summary data and emotional information as input, and outputs a customized summary ranked according to the user's emotions. This includes determining which information to prioritize and adjusting the tone of the information.

[0393] Step 5:

[0394] The device presents a customized summary to the user. It receives a customized summary from the server as input and displays it to the user in a visualized form as output. This process, delivered through the user interface of a smartphone or other device, enables the display of information in a way that responds to the user's emotions.

[0395] Step 6:

[0396] The user makes a purchase decision based on the displayed summary. The input involves accepting the presented customized summary and their own feelings and needs, while the output involves selecting the necessary products and performing the purchase operation. This includes a decision-making process for selecting the optimal product and the action of completing the purchase procedure by clicking the purchase button.

[0397] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention provides a system that allows users to efficiently collect word-of-mouth information from multiple sources, review summaries, and easily complete the purchase process. In this system, the server, terminal, and user each have their own distinct roles.

[0414] The server initially automatically collects word-of-mouth and review data from sources such as e-commerce sites, comparison sites, social media, and video platforms. This collection is performed using scraping techniques and public APIs, and is designed to accommodate updates to the information sources. The collected data is stored in a database in a unified format.

[0415] Next, the server uses natural language processing and machine learning techniques to analyze the collected data and generate a summary. The analysis primarily involves sentiment analysis of the text, identifying positive and negative evaluations and extracting important keywords and phrases. The generated summary is then customized based on the user's past preferences and interests.

[0416] When a user accesses this system, the terminal displays a customized summary provided by the server. The user interface is designed to be intuitive and easy to use, allowing users to easily select detailed information on categories and products that interest them.

[0417] The user considers the products they wish to purchase based on the summary information presented by their device, and if they wish to purchase them, they click the purchase button. The server then automatically initiates the purchase process for the selected products. Using pre-registered payment information and shipping address, the server accesses an external e-commerce site and confirms the order. Once the order is complete, the server sends a confirmation notification to the user's device.

[0418] For example, if a user wants to buy a new smartphone, the system collects the latest user reviews from sources such as Amazon, Twitter, and YouTube, and generates a summary of its features and user experience. Based on this information, the user can find the optimal smartphone and complete the purchase on the spot. In this way, the present invention streamlines the consumer purchasing process and reduces its burden.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server references a list of e-commerce sites and social media platforms as sources of information and prepares to establish connections to access their respective URLs and API endpoints. Access may require authentication credentials, which are then configured.

[0422] Step 2:

[0423] The server collects data from the target information sources. It either uses scraping techniques to read web pages and extract the necessary information, or retrieves data using public APIs. The collected data includes information such as review text, rating scores, and posting dates.

[0424] Step 3:

[0425] The server converts the collected data into a standardized format and stores it in a database. This format conversion is performed to enable data classification and tagging, and to streamline subsequent processing.

[0426] Step 4:

[0427] The server uses natural language processing (NLP) to analyze the stored data. Sentiment analysis classifies review ratings into positive, negative, and neutral, and extracts key keywords and characteristic expressions.

[0428] Step 5:

[0429] The server customizes the extracted information based on each user's configured interests and historical data, generating personalized summaries. This allows for the highlighting and presentation of information that matches the user's interests.

[0430] Step 6:

[0431] The terminal displays a customized summary provided by the server to the user. The user interface is designed to clearly display categories and summaries for each product, allowing the user to easily refer to details.

[0432] Step 7:

[0433] Users view the provided summary information and select the products they wish to purchase. After making their selection, they proceed to the order process by pressing the purchase button.

[0434] Step 8:

[0435] The server automatically executes the purchase process on external e-commerce sites based on the user's selection. Using pre-registered payment information and shipping address, the purchase is completed more quickly.

[0436] Step 9:

[0437] After a purchase is complete, the server sends a confirmation message and order details to the user's device. This allows the user to view their purchase history and request additional support.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0440] In modern society, consumers are confronted with a vast amount of information daily, making it difficult to efficiently gather reliable word-of-mouth information and utilize it in their purchasing decisions. Furthermore, quickly selecting products that meet individual needs based on the collected information and completing the purchase process requires considerable time and effort. Therefore, there is a need for a system that provides users with efficient information gathering, customized summaries, and a smooth purchasing process all in one place.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes means for automatically collecting digital information from information sources, means for analyzing the collected digital information to generate summary information, and means for personalizing the summary information based on the user's past preferences and interests. This makes it possible for users to efficiently obtain reliable word-of-mouth information and easily obtain product information that is best suited to them.

[0443] "Information sources" refer to the starting points or services for digital information provided from various platforms and media on the internet.

[0444] "Digital information" is a general term for information that is recorded and stored electronically and accessed and processed via a computer, including information in various formats such as text, images, audio, and video.

[0445] "Means of collection" refers to the technologies, devices, and methods used to automatically acquire digital information from information sources.

[0446] "Means of analysis" refers to methods and tools for analyzing collected digital information and extracting meaning and patterns.

[0447] "Summary information" refers to information that extracts the essence of the original digital information and presents it in a concise form.

[0448] "Personalization methods" refer to technologies and methods for customizing information according to the specific needs and interests of each user.

[0449] "Display means" refers to a device or method for providing information to users visually.

[0450] "Purchase-related procedures" refer to the entire process from deciding to purchase to payment and delivery arrangements.

[0451] To implement this invention, the system consists of a server, a terminal, and a user.

[0452] The server automatically collects digital information from multiple internet-connected sources. This collection utilizes programming languages ​​like Python, employing data extraction techniques such as web scraping and public application programming interfaces. Specifically, it processes web pages using the BeautifulSoup library and retrieves information by sending requests to the source's API. The collected information is structured and stored in a database such as MySQL.

[0453] Subsequently, the server performs natural language processing, analyzing the collected digital information to generate meaningful summary information. This process utilizes Python's natural language processing library NLTK and the machine learning library TensorFlow. These are used to extract sentiment trends, frequently occurring phrases, and keywords. The generated summary information is then personalized based on the user's past preferences and interests.

[0454] The terminal presents personalized summary information sent from the server to the user in an intuitively understandable format. This interface was developed using the React library and is designed to allow users to easily obtain information of interest.

[0455] The user makes a purchase decision and takes action based on the summary information displayed on the terminal. The server receives this action and uses pre-registered payment information to automate the purchase-related procedures and complete the order in cooperation with an external online store.

[0456] For example, if a user wants to purchase a new mobile device, the server collects digital information from e-commerce sites and social media, and generates meaningful summary information about its features and reviews. The user can then use this information to make the best choice and complete the purchase process smoothly. This method allows users to conduct their purchasing activities more efficiently.

[0457] Example prompt for input to the generative AI model: "Collect user reviews of the latest mobile devices and summarize the evaluation trends."

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The server retrieves digital information from information sources. The input consists of information from multiple platforms on the internet (e.g., e-commerce sites, social media, video sites). The server uses BeautifulSoup to scrape web pages and sends information requests using public APIs. This results in receiving the latest word-of-mouth and review information as output.

[0461] Step 2:

[0462] The server analyzes the acquired review information using natural language processing. The input is the review information collected in the previous step. Using the Python NLTK library, this text data is tokenized, sentiment analysis is performed, and keywords are extracted. As a result of the analysis, summary information of reputation trends and important words is output.

[0463] Step 3:

[0464] The server customizes the generated summary information based on the user's past preferences and interests. The input consists of the summary information and the user's profile information. The summary content is then personalized, taking into account the user settings in the database. This results in the output of personalized summary information.

[0465] Step 4:

[0466] The terminal displays personalized summary information. The input is personalized summary information received from the server. An interface built using React displays this information in an intuitive and easy-to-understand manner. Visually organized summary information is output on the terminal.

[0467] Step 5:

[0468] Users evaluate summary information on their devices and make purchase decisions as needed. The input is the information displayed on the device. When a user decides to purchase and clicks the purchase button, that information is sent to the server. This transmits the purchasing behavior as input to the server.

[0469] Step 6:

[0470] The server automates purchase-related procedures based on the user's purchase intent. The input is the user's purchase intent information. The server uses pre-registered payment information and calls the online store's API to confirm the purchase. Finally, an order confirmation notification is output to the user's terminal.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0473] In traditional online shopping, gathering customer reviews and selecting products was time-consuming, making it difficult for users to choose the best product for them. Furthermore, manually gathering and analyzing information from numerous sources was highly inefficient, reducing user convenience. Additionally, the automation of the purchase process was insufficient, highlighting the need for improved user experience.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for customizing the summary based on the user's past purchase history and preferences. This enables the user to efficiently select the products best suited to them and to complete the purchase process quickly and easily.

[0476] "Information sources" refers to the collective term for websites, social media platforms, or other online data-providing entities used to collect data.

[0477] "Data decomposition techniques" are automated methods or tools used to extract necessary information from specific online resources.

[0478] A "public interface" is a standardized communication method that external systems can use to acquire or transmit information.

[0479] A "generated language model" is an algorithm trained to perform natural language processing based on a large amount of text data.

[0480] "Natural language processing" is a technology that allows computers to understand and analyze natural language used by humans, and to extract or generate appropriate information.

[0481] "High-frequency expressions" are words or phrases that appear particularly frequently within a specific dataset in data analysis.

[0482] "Opinion trends" refer to information that indicates the overall direction of evaluation based on analyzed word-of-mouth data.

[0483] An "external database" is a storage system that stores information located externally and accesses it as needed.

[0484] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0485] First, the server automatically collects data from information sources. This data collection utilizes data decomposition techniques and public interfaces. Specifically, this makes it possible to efficiently obtain reviews and word-of-mouth information from e-commerce sites and social media. The server stores the collected data in a database, analyzes it using natural language processing, and extracts opinion trends and high-frequency expressions. The generated language model is then used to form summaries of information that users may find interesting.

[0486] Based on the analysis results, the server customizes the summary considering the user's past purchase history and preferences, and sends it to the terminal. The terminal provides the received summary to the user and suggests products according to the user's selection. For products selected by the user, the purchase process is automated through an external database, enabling quick purchases.

[0487] For example, when a user is considering purchasing a new gadget, they might enter a prompt message through a smartphone app saying, "I'd like you to collect and summarize reviews of the latest headphones and suggest purchase options that prioritize sound quality and usability." The system then automatically collects and analyzes the information, assisting the user in selecting and purchasing the most suitable product.

[0488] This entire system is implemented using hardware and software such as Python, BeautifulSoup, and TensorFlow, with the aim of improving the user experience.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The server receives a user prompt and automatically collects relevant data from its sources. The input is a user prompt requesting "collect and summarize reviews of the latest headphones, and provide purchase recommendations focusing on sound quality and usability." The output is review data from e-commerce sites, social media, and video platforms. This data collection utilizes data decomposition techniques and a public interface.

[0492] Step 2:

[0493] The server stores the acquired review data in a database and performs natural language processing. The input is the review data collected in step 1, and the output is an analysis result that extracts opinion trends and high-frequency expressions. Specifically, a generative AI model is used to analyze evaluation trends and extract keywords.

[0494] Step 3:

[0495] The server generates a summary based on the analysis results and customizes it based on the user's past purchase history and preferences. The input is the analysis results from step 2 and the user's purchase history data, and the output is a product summary customized for the user. This summary takes the user's interests and preferences into account and includes more relevant information.

[0496] Step 4:

[0497] The server sends a customized summary to the terminal. The input is the summary generated in step 3, and the output is data for display on the user's terminal. The terminal provides the received summary to the user, enabling intuitive visualization and manipulation.

[0498] Step 5:

[0499] The user selects products based on the summary information provided on the terminal and proceeds with the purchase. The input is the product summary displayed on the terminal, and the output is the information of the selected products sent to the server, initiating the purchase process. Specifically, clicking the purchase button confirms the order through an external database.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention is a system that efficiently collects, analyzes, summarizes, and appropriately customizes word-of-mouth information obtained by users from multiple sources, and further provides an experience that takes into account the user's emotions. In particular, it incorporates an emotion engine that recognizes the user's emotional state and adjusts the information provided according to those emotions.

[0502] The server first automatically collects data from information sources such as e-commerce sites and social media. It uses scraping techniques and public APIs to obtain reviews, comments, and rating data, and stores this information in a database. The database then integrates the information into a unified format for efficient management.

[0503] Subsequently, the server uses natural language processing technology to analyze the collected data and generate a summary. During the analysis process, the sentiment engine continues to operate, ranking and highlighting content based on the user's current emotions and preferences. The user's emotions are inferred from interaction data and recent behavior.

[0504] The terminal displays a customized summary generated by the server to the user. The displayed information dynamically changes based on the user's emotions; if positive, the features of the new product are emphasized, while if somewhat negative, risk and pricing information is given more weight. The user interface is also configured to include visuals and tones that reflect emotions.

[0505] Users can consider purchasing a product based on the provided summary information. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated, including variables that may be influenced by the emotion engine's requests.

[0506] For example, if a user is considering purchasing a wristwatch and has just been browsing luxury jewelry, the system will recognize this excitement and anticipation and prioritize presenting information and reviews of high-end watches that match those feelings. Based on this, the user can make the optimal choice and easily complete the purchase. By understanding and applying emotions, this invention makes it possible to further personalize the user's purchasing experience and improve satisfaction.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The server refers to a list of e-commerce sites and social networking platforms that serve as information sources and connects to their respective URLs or API endpoints. If necessary, it prepares for access using authentication information such as API keys.

[0510] Step 2:

[0511] The server collects data from information sources. It utilizes scraping techniques and public APIs to obtain review, rating, and comment data. The collected data is stored in a database as text.

[0512] Step 3:

[0513] The server uses natural language processing (NLP) techniques to analyze the collected data. Here, it analyzes text data to determine sentiment, classifies reviews as positive, negative, or neutral, and extracts important keywords.

[0514] Step 4:

[0515] The server generates a summary based on the analyzed data. This summary is customized to take into account the user's past behavior history and interests. For example, it may highlight the features of products that the user has previously given high ratings to create a personalized summary.

[0516] Step 5:

[0517] The server uses an emotion engine to recognize the user's current emotional state. Based on the user's recent actions and choices, it sets emotional parameters and plans to present information appropriate to that state.

[0518] Step 6:

[0519] The device displays a customized summary provided by the server to the user. The display format changes according to the user's emotions; for example, a simple and minimalist visual is chosen when the user is relaxed, while an impactful design is used when the user is excited.

[0520] Step 7:

[0521] Users can view summaries displayed on their devices, further explore information that interests them, and select products. After making their selection, they can proceed with the purchase using the purchase button.

[0522] Step 8:

[0523] The server integrates with external e-commerce sites to automate the purchase process for the products selected by the user. It uses registered payment information and shipping addresses to ensure the order is completed quickly.

[0524] Step 9:

[0525] The server sends a confirmation message to the user's device after the purchase is complete. This message includes order details and an estimated delivery date, allowing the user to easily manage their purchase history.

[0526] (Example 2)

[0527] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0528] In modern society, users need to quickly obtain the most useful information from a vast amount of data. However, because information sources are so diverse, manually checking each one is extremely time-consuming. Furthermore, users cannot obtain information that is appropriate to their situation or emotional state, leading to inefficiencies in information gathering and decision-making. In addition, in purchasing behavior, the lack of sufficient personalization based on user emotions makes it difficult to enhance user satisfaction.

[0529] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0530] In this invention, the server includes means for automatically collecting data from information sources, means for formatting the collected data into a unified format, and means for analyzing the data and generating a summary that takes into account the user's emotional state. As a result, the user receives an information summary customized according to their emotional state based on data automatically collected from diverse information sources, enabling more efficient and personalized information gathering and decision-making.

[0531] "Information sources" refer to online platforms such as e-commerce sites and social media where users can obtain word-of-mouth, reviews, and rating data.

[0532] "Data collection" refers to the process of automatically obtaining necessary data from information sources, and is carried out using scraping techniques and public APIs.

[0533] "Format conversion" refers to the process of converting data in different formats into a unified format, a step that enables effective data integration and management.

[0534] "Summary generation" refers to the process of extracting essential information from analyzed data and expressing it in a concise form.

[0535] An "emotion engine" refers to a system component that analyzes a user's past behavior and real-time interactions to infer their emotional state.

[0536] "Customization" refers to the process of dynamically adjusting the information provided based on the user's emotions and preferences.

[0537] "Feedback" refers to the process by which a system adjusts its behavior based on user interaction data, with the aim of improving the user experience.

[0538] "Automation" refers to the ability of a system to complete a series of processes independently, minimizing manual intervention by the user.

[0539] This invention is a system that efficiently collects word-of-mouth information obtained by users from multiple sources, analyzes and summarizes it, customizes it appropriately, and provides an experience that takes into account the user's emotions.

[0540] The server first automatically collects data from sources such as e-commerce sites and social media. This process utilizes scraping techniques and public APIs. This retrieves review, comment, and rating data, which are then stored in a database on the server. The database converts and integrates the collected data into a unified format for efficient management.

[0541] Next, the server analyzes the collected data using natural language processing technology. This analysis utilizes a generative AI model, enabling the generation of summaries that take user emotions into account. The server also employs an emotion engine, analyzing emotion patterns extracted from the user's past behavior data and real-time interactions to emphasize and prioritize information according to the user's emotions.

[0542] The generated, customized summary is displayed to the user through their device. In this process, the information dynamically changes according to the user's emotional state. For example, if positive emotions are detected, the features of the new product are highlighted, while if negative emotions are present, risk and pricing information takes precedence. This makes the user experience more personalized.

[0543] Users can consider purchasing a product based on the summary information provided. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated within the server, assisted by an emotion engine.

[0544] For example, if a user inputs the prompt "I'm looking for a highly-rated smartwatch" into the AI ​​model, the system will collect, analyze, and summarize information on relevant highly-rated products before providing it to the user. This information will be adjusted according to the user's emotional state and preferences to support the optimal purchasing decision.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The server collects data from information sources. Specifically, it extracts review, comment, and rating data from e-commerce sites and social media using scraping techniques and APIs. The input in this process is the information source, and the output is raw data stored in the server's storage. The server temporarily holds this data in preparation for subsequent processing.

[0548] Step 2:

[0549] The server converts the collected raw data into a standardized format. At this stage, the input is the raw data obtained in step 1, and the necessary data processing is performed through the ETL process. Specifically, the data is cleaned, processed, and transformed, and the standardized data is stored in the database as output. The server eliminates data redundancy and manages the information efficiently.

[0550] Step 3:

[0551] The server analyzes the data using a generative AI model and infers the user's emotions using an emotion engine. This process uses the standardized data saved in step 2 as input. Natural language processing techniques are employed to extract evaluation trends and frequently occurring keywords, and the data is highlighted and ranked according to the user's emotions. The output is a customized summary generated with the user's emotions in mind.

[0552] Step 4:

[0553] The terminal displays a summary received from the server to the user. The input is the customized summary, which is the output from step 3, and this is used as the basis for dynamic information display. For example, based on the user's emotions, new product features may be emphasized or risk information may be prioritized. The terminal is designed to display information that reflects emotions visually as well.

[0554] Step 5:

[0555] The user considers the product based on the summary information displayed on the device and decides whether to purchase it. The input is the customized summary presented in step 4, and the output is the final purchase action. When the user clicks the purchase button, the purchase process based on the user's selection is automated within the server. The user's interaction data is also sent back to the server as feedback and used to analyze emotional patterns.

[0556] (Application Example 2)

[0557] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0558] In modern online shopping, it is difficult for users to obtain the information they need quickly and accurately. Furthermore, there is a need to dynamically customize information according to the user's emotional state and preferences to increase their purchasing intent. However, conventional systems lack the means to provide emotionally conscious information and automate the user's purchasing process, resulting in a limited user experience and difficulty in improving satisfaction.

[0559] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0560] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for dynamically customizing the summary based on the user's emotional state and interests. This enables the user to make purchasing decisions based on information tailored to their own emotional state, allowing them to enjoy a more personalized experience.

[0561] "Information sources" refer to digital data obtained from online platforms and social networks.

[0562] "Means of automatically collecting data" refers to technologies for mechanically obtaining information from the internet, including, for example, scraping techniques and available APIs.

[0563] "Means of generating summaries" refers to techniques for analyzing collected information and expressing its essence in a shortened form.

[0564] "User emotional state" refers to the psychological state of the user, inferred through their interaction with the system and their actions.

[0565] "Means for dynamically customizing summaries" refers to technologies that adjust the summaries provided based on the user's current emotional state and interests.

[0566] "Purchase decision" refers to the final decision a user makes to purchase a product or service.

[0567] A "personalized experience" refers to a customized user experience that is tailored to the specific needs and preferences of individual users.

[0568] To implement this invention, the server first automatically collects data from information sources. The server systematically collects review, comment, and rating data from e-commerce sites and social networks using web scraping techniques and publicly available APIs. The collected data is stored in a database and integrated into a consistent format.

[0569] Next, the server analyzes the collected data using natural language processing and generates a summary. It performs text analysis using a Python natural language processing library (e.g., NLTK) to extract evaluation trends and frequently occurring words. Furthermore, an emotion engine analyzes the user's current emotional state and dynamically customizes the summary for each user based on that information. The user's emotional state is inferred from interface touches and voice input.

[0570] Subsequently, the device presents a customized summary to the user. Smartphones are primarily used as the device, and the summarized information is displayed through a graphical user interface, tailored to the user's emotional state. If the emotional state is determined to be agitated, the display method and tone of the information are adjusted, prioritizing positive reviews and highly-rated products.

[0571] For example, if a user is looking to buy a new camera and their recent search history suggests they are planning a trip, the system will prioritize displaying product reviews and ratings in the "Cameras Best for Travel" category. This allows the user to make the best product choice for their situation.

[0572] Examples of prompt statements include:

[0573] "This user is looking for a travel backpack and is in a cheerful mood. Please generate a product review that is suitable for a cheerful mood."

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The server automatically collects data from information sources. It accepts URLs and API keys from e-commerce sites and social networks as input, and retrieves raw review and comment data as output. The process involves using scraping techniques and public APIs to collect data, structuring it, and storing it in a database.

[0577] Step 2:

[0578] The server uses natural language processing techniques to analyze collected data and generate summaries. It receives stored review and comment data as input and generates summary data with key information extracted as output. This includes using Python natural language processing libraries (such as NLTK) to perform text analysis and identify evaluation trends and frequently occurring keywords.

[0579] Step 3:

[0580] The server uses an emotion engine to analyze the user's emotional state. It receives user interaction data and recent behavioral history as input and generates information about the user's emotional state as output. The data is analyzed via an emotion analysis API to estimate the user's psychological state.

[0581] Step 4:

[0582] The server dynamically customizes summaries based on the user's emotional state and interests. It receives generated summary data and emotional information as input, and outputs a customized summary ranked according to the user's emotions. This includes determining which information to prioritize and adjusting the tone of the information.

[0583] Step 5:

[0584] The device presents a customized summary to the user. It receives a customized summary from the server as input and displays it to the user in a visualized form as output. This process, delivered through the user interface of a smartphone or other device, enables the display of information in a way that responds to the user's emotions.

[0585] Step 6:

[0586] The user makes a purchase decision based on the displayed summary. The input involves accepting the presented customized summary and their own feelings and needs, while the output involves selecting the necessary products and performing the purchase operation. This includes a decision-making process for selecting the optimal product and the action of completing the purchase procedure by clicking the purchase button.

[0587] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0588] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0589] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0593] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0594] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0595] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0596] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0597] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0598] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0599] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0600] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0601] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0602] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0603] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0604] This invention provides a system that allows users to efficiently collect word-of-mouth information from multiple sources, review summaries, and easily complete the purchase process. In this system, the server, terminal, and user each have their own distinct roles.

[0605] The server initially automatically collects word-of-mouth and review data from sources such as e-commerce sites, comparison sites, social media, and video platforms. This collection is performed using scraping techniques and public APIs, and is designed to accommodate updates to the information sources. The collected data is stored in a database in a unified format.

[0606] Next, the server uses natural language processing and machine learning techniques to analyze the collected data and generate a summary. The analysis primarily involves sentiment analysis of the text, identifying positive and negative evaluations and extracting important keywords and phrases. The generated summary is then customized based on the user's past preferences and interests.

[0607] When a user accesses this system, the terminal displays a customized summary provided by the server. The user interface is designed to be intuitive and easy to use, allowing users to easily select detailed information on categories and products that interest them.

[0608] The user considers the products they wish to purchase based on the summary information presented by their device, and if they wish to purchase them, they click the purchase button. The server then automatically initiates the purchase process for the selected products. Using pre-registered payment information and shipping address, the server accesses an external e-commerce site and confirms the order. Once the order is complete, the server sends a confirmation notification to the user's device.

[0609] For example, if a user wants to buy a new smartphone, the system collects the latest user reviews from sources such as Amazon, Twitter, and YouTube, and generates a summary of its features and user experience. Based on this information, the user can find the optimal smartphone and complete the purchase on the spot. In this way, the present invention streamlines the consumer purchasing process and reduces its burden.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The server references a list of e-commerce sites and social media platforms as sources of information and prepares to establish connections to access their respective URLs and API endpoints. Access may require authentication credentials, which are then configured.

[0613] Step 2:

[0614] The server collects data from the target information sources. It either uses scraping techniques to read web pages and extract the necessary information, or retrieves data using public APIs. The collected data includes information such as review text, rating scores, and posting dates.

[0615] Step 3:

[0616] The server converts the collected data into a standardized format and stores it in a database. This format conversion is performed to enable data classification and tagging, and to streamline subsequent processing.

[0617] Step 4:

[0618] The server uses natural language processing (NLP) to analyze the stored data. Sentiment analysis classifies review ratings into positive, negative, and neutral, and extracts key keywords and characteristic expressions.

[0619] Step 5:

[0620] The server customizes the extracted information based on each user's configured interests and historical data, generating personalized summaries. This allows for the highlighting and presentation of information that matches the user's interests.

[0621] Step 6:

[0622] The terminal displays a customized summary provided by the server to the user. The user interface is designed to clearly display categories and summaries for each product, allowing the user to easily refer to details.

[0623] Step 7:

[0624] Users view the provided summary information and select the products they wish to purchase. After making their selection, they proceed to the order process by pressing the purchase button.

[0625] Step 8:

[0626] The server automatically executes the purchase process on external e-commerce sites based on the user's selection. Using pre-registered payment information and shipping address, the purchase is completed more quickly.

[0627] Step 9:

[0628] After a purchase is complete, the server sends a confirmation message and order details to the user's device. This allows the user to view their purchase history and request additional support.

[0629] (Example 1)

[0630] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] In modern society, consumers are confronted with a vast amount of information daily, making it difficult to efficiently gather reliable word-of-mouth information and utilize it in their purchasing decisions. Furthermore, quickly selecting products that meet individual needs based on the collected information and completing the purchase process requires considerable time and effort. Therefore, there is a need for a system that provides users with efficient information gathering, customized summaries, and a smooth purchasing process all in one place.

[0632] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0633] In this invention, the server includes means for automatically collecting digital information from information sources, means for analyzing the collected digital information to generate summary information, and means for personalizing the summary information based on the user's past preferences and interests. This makes it possible for users to efficiently obtain reliable word-of-mouth information and easily obtain product information that is best suited to them.

[0634] "Information sources" refer to the starting points or services for digital information provided from various platforms and media on the internet.

[0635] "Digital information" is a general term for information that is recorded and stored electronically and accessed and processed via a computer, including information in various formats such as text, images, audio, and video.

[0636] "Means of collection" refers to the technologies, devices, and methods used to automatically acquire digital information from information sources.

[0637] "Means of analysis" refers to methods and tools for analyzing collected digital information and extracting meaning and patterns.

[0638] "Summary information" refers to information that extracts the essence of the original digital information and presents it in a concise form.

[0639] "Personalization methods" refer to technologies and methods for customizing information according to the specific needs and interests of each user.

[0640] "Display means" refers to a device or method for providing information to users visually.

[0641] "Purchase-related procedures" refer to the entire process from deciding to purchase to payment and delivery arrangements.

[0642] To implement this invention, the system consists of a server, a terminal, and a user.

[0643] The server automatically collects digital information from multiple internet-connected sources. This collection utilizes programming languages ​​like Python, employing data extraction techniques such as web scraping and public application programming interfaces. Specifically, it processes web pages using the BeautifulSoup library and retrieves information by sending requests to the source's API. The collected information is structured and stored in a database such as MySQL.

[0644] Subsequently, the server performs natural language processing, analyzing the collected digital information to generate meaningful summary information. This process utilizes Python's natural language processing library NLTK and the machine learning library TensorFlow. These are used to extract sentiment trends, frequently occurring phrases, and keywords. The generated summary information is then personalized based on the user's past preferences and interests.

[0645] The terminal presents personalized summary information sent from the server to the user in an intuitively understandable format. This interface was developed using the React library and is designed to allow users to easily obtain information of interest.

[0646] The user makes a purchase decision and takes action based on the summary information displayed on the terminal. The server receives this action and uses pre-registered payment information to automate the purchase-related procedures and complete the order in cooperation with an external online store.

[0647] For example, if a user wants to purchase a new mobile device, the server collects digital information from e-commerce sites and social media, and generates meaningful summary information about its features and reviews. The user can then use this information to make the best choice and complete the purchase process smoothly. This method allows users to conduct their purchasing activities more efficiently.

[0648] Example prompt for input to the generative AI model: "Collect user reviews of the latest mobile devices and summarize the evaluation trends."

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server retrieves digital information from information sources. The input consists of information from multiple platforms on the internet (e.g., e-commerce sites, social media, video sites). The server uses BeautifulSoup to scrape web pages and sends information requests using public APIs. This results in receiving the latest word-of-mouth and review information as output.

[0652] Step 2:

[0653] The server analyzes the acquired review information using natural language processing. The input is the review information collected in the previous step. Using the Python NLTK library, this text data is tokenized, sentiment analysis is performed, and keywords are extracted. As a result of the analysis, summary information of reputation trends and important words is output.

[0654] Step 3:

[0655] The server customizes the generated summary information based on the user's past preferences and interests. The input consists of the summary information and the user's profile information. The summary content is then personalized, taking into account the user settings in the database. This results in the output of personalized summary information.

[0656] Step 4:

[0657] The terminal displays personalized summary information. The input is personalized summary information received from the server. An interface built using React displays this information in an intuitive and easy-to-understand manner. Visually organized summary information is output on the terminal.

[0658] Step 5:

[0659] Users evaluate summary information on their devices and make purchase decisions as needed. The input is the information displayed on the device. When a user decides to purchase and clicks the purchase button, that information is sent to the server. This transmits the purchasing behavior as input to the server.

[0660] Step 6:

[0661] The server automates purchase-related procedures based on the user's purchase intent. The input is the user's purchase intent information. The server uses pre-registered payment information and calls the online store's API to confirm the purchase. Finally, an order confirmation notification is output to the user's terminal.

[0662] (Application Example 1)

[0663] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] In traditional online shopping, gathering customer reviews and selecting products was time-consuming, making it difficult for users to choose the best product for them. Furthermore, manually gathering and analyzing information from numerous sources was highly inefficient, reducing user convenience. Additionally, the automation of the purchase process was insufficient, highlighting the need for improved user experience.

[0665] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0666] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for customizing the summary based on the user's past purchase history and preferences. This enables the user to efficiently select the products best suited to them and to complete the purchase process quickly and easily.

[0667] "Information sources" refers to the collective term for websites, social media platforms, or other online data-providing entities used to collect data.

[0668] "Data decomposition techniques" are automated methods or tools used to extract necessary information from specific online resources.

[0669] A "public interface" is a standardized communication method that external systems can use to acquire or transmit information.

[0670] A "generated language model" is an algorithm trained to perform natural language processing based on a large amount of text data.

[0671] "Natural language processing" is a technology that allows computers to understand and analyze natural language used by humans, and to extract or generate appropriate information.

[0672] "High-frequency expressions" are words or phrases that appear particularly frequently within a specific dataset in data analysis.

[0673] "Opinion trends" refer to information that indicates the overall direction of evaluation based on analyzed word-of-mouth data.

[0674] An "external database" is a storage system that stores information located externally and accesses it as needed.

[0675] The system that realizes this invention mainly consists of three components: a server, a terminal, and a user.

[0676] First, the server automatically collects data from information sources. This data collection utilizes data decomposition techniques and public interfaces. Specifically, this makes it possible to efficiently obtain reviews and word-of-mouth information from e-commerce sites and social media. The server stores the collected data in a database, analyzes it using natural language processing, and extracts opinion trends and high-frequency expressions. The generated language model is then used to form summaries of information that users may find interesting.

[0677] Based on the analysis results, the server customizes the summary considering the user's past purchase history and preferences, and sends it to the terminal. The terminal provides the received summary to the user and suggests products according to the user's selection. For products selected by the user, the purchase process is automated through an external database, enabling quick purchases.

[0678] For example, when a user is considering purchasing a new gadget, they might enter a prompt message through a smartphone app saying, "I'd like you to collect and summarize reviews of the latest headphones and suggest purchase options that prioritize sound quality and usability." The system then automatically collects and analyzes the information, assisting the user in selecting and purchasing the most suitable product.

[0679] This entire system is implemented using hardware and software such as Python, BeautifulSoup, and TensorFlow, with the aim of improving the user experience.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The server receives a user prompt and automatically collects relevant data from its sources. The input is a user prompt requesting "collect and summarize reviews of the latest headphones, and provide purchase recommendations focusing on sound quality and usability." The output is review data from e-commerce sites, social media, and video platforms. This data collection utilizes data decomposition techniques and a public interface.

[0683] Step 2:

[0684] The server stores the acquired review data in a database and performs natural language processing. The input is the review data collected in step 1, and the output is an analysis result that extracts opinion trends and high-frequency expressions. Specifically, a generative AI model is used to analyze evaluation trends and extract keywords.

[0685] Step 3:

[0686] The server generates a summary based on the analysis results and customizes it based on the user's past purchase history and preferences. The input is the analysis results from step 2 and the user's purchase history data, and the output is a product summary customized for the user. This summary takes the user's interests and preferences into account and includes more relevant information.

[0687] Step 4:

[0688] The server sends a customized summary to the terminal. The input is the summary generated in step 3, and the output is data for display on the user's terminal. The terminal provides the received summary to the user, enabling intuitive visualization and manipulation.

[0689] Step 5:

[0690] The user selects products based on the summary information provided on the terminal and proceeds with the purchase. The input is the product summary displayed on the terminal, and the output is the information of the selected products sent to the server, initiating the purchase process. Specifically, clicking the purchase button confirms the order through an external database.

[0691] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0692] This invention is a system that efficiently collects, analyzes, summarizes, and appropriately customizes word-of-mouth information obtained by users from multiple sources, and further provides an experience that takes into account the user's emotions. In particular, it incorporates an emotion engine that recognizes the user's emotional state and adjusts the information provided according to those emotions.

[0693] The server first automatically collects data from information sources such as e-commerce sites and social media. It uses scraping techniques and public APIs to obtain reviews, comments, and rating data, and stores this information in a database. The database then integrates the information into a unified format for efficient management.

[0694] Subsequently, the server uses natural language processing technology to analyze the collected data and generate a summary. During the analysis process, the sentiment engine continues to operate, ranking and highlighting content based on the user's current emotions and preferences. The user's emotions are inferred from interaction data and recent behavior.

[0695] The terminal displays a customized summary generated by the server to the user. The displayed information dynamically changes based on the user's emotions; if positive, the features of the new product are emphasized, while if somewhat negative, risk and pricing information is given more weight. The user interface is also configured to include visuals and tones that reflect emotions.

[0696] Users can consider purchasing a product based on the provided summary information. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated, including variables that may be influenced by the emotion engine's requests.

[0697] For example, if a user is considering purchasing a wristwatch and has just been browsing luxury jewelry, the system will recognize this excitement and anticipation and prioritize presenting information and reviews of high-end watches that match those feelings. Based on this, the user can make the optimal choice and easily complete the purchase. By understanding and applying emotions, this invention makes it possible to further personalize the user's purchasing experience and improve satisfaction.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The server refers to a list of e-commerce sites and social networking platforms that serve as information sources and connects to their respective URLs or API endpoints. If necessary, it prepares for access using authentication information such as API keys.

[0701] Step 2:

[0702] The server collects data from information sources. It utilizes scraping techniques and public APIs to obtain review, rating, and comment data. The collected data is stored in a database as text.

[0703] Step 3:

[0704] The server uses natural language processing (NLP) techniques to analyze the collected data. Here, it analyzes text data to determine sentiment, classifies reviews as positive, negative, or neutral, and extracts important keywords.

[0705] Step 4:

[0706] The server generates a summary based on the analyzed data. This summary is customized to take into account the user's past behavior history and interests. For example, it may highlight the features of products that the user has previously given high ratings to create a personalized summary.

[0707] Step 5:

[0708] The server uses an emotion engine to recognize the user's current emotional state. Based on the user's recent actions and choices, it sets emotional parameters and plans to present information appropriate to that state.

[0709] Step 6:

[0710] The device displays a customized summary provided by the server to the user. The display format changes according to the user's emotions; for example, a simple and minimalist visual is chosen when the user is relaxed, while an impactful design is used when the user is excited.

[0711] Step 7:

[0712] Users can view summaries displayed on their devices, further explore information that interests them, and select products. After making their selection, they can proceed with the purchase using the purchase button.

[0713] Step 8:

[0714] The server integrates with external e-commerce sites to automate the purchase process for the products selected by the user. It uses registered payment information and shipping addresses to ensure the order is completed quickly.

[0715] Step 9:

[0716] The server sends a confirmation message to the user's device after the purchase is complete. This message includes order details and an estimated delivery date, allowing the user to easily manage their purchase history.

[0717] (Example 2)

[0718] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] In modern society, users need to quickly obtain the most useful information from a vast amount of data. However, because information sources are so diverse, manually checking each one is extremely time-consuming. Furthermore, users cannot obtain information that is appropriate to their situation or emotional state, leading to inefficiencies in information gathering and decision-making. In addition, in purchasing behavior, the lack of sufficient personalization based on user emotions makes it difficult to enhance user satisfaction.

[0720] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0721] In this invention, the server includes means for automatically collecting data from information sources, means for formatting the collected data into a unified format, and means for analyzing the data and generating a summary that takes into account the user's emotional state. As a result, the user receives an information summary customized according to their emotional state based on data automatically collected from diverse information sources, enabling more efficient and personalized information gathering and decision-making.

[0722] "Information sources" refer to online platforms such as e-commerce sites and social media where users can obtain word-of-mouth, reviews, and rating data.

[0723] "Data collection" refers to the process of automatically obtaining necessary data from information sources, and is carried out using scraping techniques and public APIs.

[0724] "Format conversion" refers to the process of converting data in different formats into a unified format, a step that enables effective data integration and management.

[0725] "Summary generation" refers to the process of extracting essential information from analyzed data and expressing it in a concise form.

[0726] An "emotion engine" refers to a system component that analyzes a user's past behavior and real-time interactions to infer their emotional state.

[0727] "Customization" refers to the process of dynamically adjusting the information provided based on the user's emotions and preferences.

[0728] "Feedback" refers to the process by which a system adjusts its behavior based on user interaction data, with the aim of improving the user experience.

[0729] "Automation" refers to the ability of a system to complete a series of processes independently, minimizing manual intervention by the user.

[0730] This invention is a system that efficiently collects word-of-mouth information obtained by users from multiple sources, analyzes and summarizes it, customizes it appropriately, and provides an experience that takes into account the user's emotions.

[0731] The server first automatically collects data from sources such as e-commerce sites and social media. This process utilizes scraping techniques and public APIs. This retrieves review, comment, and rating data, which are then stored in a database on the server. The database converts and integrates the collected data into a unified format for efficient management.

[0732] Next, the server analyzes the collected data using natural language processing technology. This analysis utilizes a generative AI model, enabling the generation of summaries that take user emotions into account. The server also employs an emotion engine, analyzing emotion patterns extracted from the user's past behavior data and real-time interactions to emphasize and prioritize information according to the user's emotions.

[0733] The generated, customized summary is displayed to the user through their device. In this process, the information dynamically changes according to the user's emotional state. For example, if positive emotions are detected, the features of the new product are highlighted, while if negative emotions are present, risk and pricing information takes precedence. This makes the user experience more personalized.

[0734] Users can consider purchasing a product based on the summary information provided. If they decide to purchase, they click the purchase button. At this point, the purchase process is automated within the server, assisted by an emotion engine.

[0735] For example, if a user inputs the prompt "I'm looking for a highly-rated smartwatch" into the AI ​​model, the system will collect, analyze, and summarize information on relevant highly-rated products before providing it to the user. This information will be adjusted according to the user's emotional state and preferences to support the optimal purchasing decision.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The server collects data from information sources. Specifically, it extracts review, comment, and rating data from e-commerce sites and social media using scraping techniques and APIs. The input in this process is the information source, and the output is raw data stored in the server's storage. The server temporarily holds this data in preparation for subsequent processing.

[0739] Step 2:

[0740] The server converts the collected raw data into a standardized format. At this stage, the input is the raw data obtained in step 1, and the necessary data processing is performed through the ETL process. Specifically, the data is cleaned, processed, and transformed, and the standardized data is stored in the database as output. The server eliminates data redundancy and manages the information efficiently.

[0741] Step 3:

[0742] The server analyzes the data using a generative AI model and infers the user's emotions using an emotion engine. This process uses the standardized data saved in step 2 as input. Natural language processing techniques are employed to extract evaluation trends and frequently occurring keywords, and the data is highlighted and ranked according to the user's emotions. The output is a customized summary generated with the user's emotions in mind.

[0743] Step 4:

[0744] The terminal displays a summary received from the server to the user. The input is the customized summary, which is the output from step 3, and this is used as the basis for dynamic information display. For example, based on the user's emotions, new product features may be emphasized or risk information may be prioritized. The terminal is designed to display information that reflects emotions visually as well.

[0745] Step 5:

[0746] The user considers the product based on the summary information displayed on the device and decides whether to purchase it. The input is the customized summary presented in step 4, and the output is the final purchase action. When the user clicks the purchase button, the purchase process based on the user's selection is automated within the server. The user's interaction data is also sent back to the server as feedback and used to analyze emotional patterns.

[0747] (Application Example 2)

[0748] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] In modern online shopping, it is difficult for users to obtain the information they need quickly and accurately. Furthermore, there is a need to dynamically customize information according to the user's emotional state and preferences to increase their purchasing intent. However, conventional systems lack the means to provide emotionally conscious information and automate the user's purchasing process, resulting in a limited user experience and difficulty in improving satisfaction.

[0750] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0751] In this invention, the server includes means for automatically collecting data from information sources, means for analyzing the collected data to generate a summary, and means for dynamically customizing the summary based on the user's emotional state and interests. This enables the user to make purchasing decisions based on information tailored to their own emotional state, allowing them to enjoy a more personalized experience.

[0752] "Information sources" refer to digital data obtained from online platforms and social networks.

[0753] "Means of automatically collecting data" refers to technologies for mechanically obtaining information from the internet, including, for example, scraping techniques and available APIs.

[0754] "Means of generating summaries" refers to techniques for analyzing collected information and expressing its essence in a shortened form.

[0755] "User emotional state" refers to the psychological state of the user, inferred through their interaction with the system and their actions.

[0756] "Means for dynamically customizing summaries" refers to technologies that adjust the summaries provided based on the user's current emotional state and interests.

[0757] "Purchase decision" refers to the final decision a user makes to purchase a product or service.

[0758] A "personalized experience" refers to a customized user experience that is tailored to the specific needs and preferences of individual users.

[0759] To implement this invention, the server first automatically collects data from information sources. The server systematically collects review, comment, and rating data from e-commerce sites and social networks using web scraping techniques and publicly available APIs. The collected data is stored in a database and integrated into a consistent format.

[0760] Next, the server analyzes the collected data using natural language processing and generates a summary. It performs text analysis using a Python natural language processing library (e.g., NLTK) to extract evaluation trends and frequently occurring words. Furthermore, an emotion engine analyzes the user's current emotional state and dynamically customizes the summary for each user based on that information. The user's emotional state is inferred from interface touches and voice input.

[0761] Subsequently, the device presents a customized summary to the user. Smartphones are primarily used as the device, and the summarized information is displayed through a graphical user interface, tailored to the user's emotional state. If the emotional state is determined to be agitated, the display method and tone of the information are adjusted, prioritizing positive reviews and highly-rated products.

[0762] For example, if a user is looking to buy a new camera and their recent search history suggests they are planning a trip, the system will prioritize displaying product reviews and ratings in the "Cameras Best for Travel" category. This allows the user to make the best product choice for their situation.

[0763] Examples of prompt statements include:

[0764] "This user is looking for a travel backpack and is in a cheerful mood. Please generate a product review that is suitable for a cheerful mood."

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The server automatically collects data from information sources. It accepts URLs and API keys from e-commerce sites and social networks as input, and retrieves raw review and comment data as output. The process involves using scraping techniques and public APIs to collect data, structuring it, and storing it in a database.

[0768] Step 2:

[0769] The server uses natural language processing techniques to analyze collected data and generate summaries. It receives stored review and comment data as input and generates summary data with key information extracted as output. This includes using Python natural language processing libraries (such as NLTK) to perform text analysis and identify evaluation trends and frequently occurring keywords.

[0770] Step 3:

[0771] The server uses an emotion engine to analyze the user's emotional state. It receives user interaction data and recent behavioral history as input and generates information about the user's emotional state as output. The data is analyzed via an emotion analysis API to estimate the user's psychological state.

[0772] Step 4:

[0773] The server dynamically customizes summaries based on the user's emotional state and interests. It receives generated summary data and emotional information as input, and outputs a customized summary ranked according to the user's emotions. This includes determining which information to prioritize and adjusting the tone of the information.

[0774] Step 5:

[0775] The device presents a customized summary to the user. It receives a customized summary from the server as input and displays it to the user in a visualized form as output. This process, delivered through the user interface of a smartphone or other device, enables the display of information in a way that responds to the user's emotions.

[0776] Step 6:

[0777] The user makes a purchase decision based on the displayed summary. The input involves accepting the presented customized summary and their own feelings and needs, while the output involves selecting the necessary products and performing the purchase operation. This includes a decision-making process for selecting the optimal product and the action of completing the purchase procedure by clicking the purchase button.

[0778] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0781] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0782] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0783] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0784] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0785] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0786] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0787] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0788] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0789] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0790] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0791] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0792] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0793] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0794] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0795] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0796] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0797] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0798] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] Means for automatically collecting data from information sources,

[0802] A means of analyzing collected data and generating a summary,

[0803] A means to customize summaries based on user interests,

[0804] A means of providing a summary to the user,

[0805] A system that includes means to automate the purchase process based on user selection.

[0806] (Claim 2)

[0807] The system according to claim 1, which utilizes scraping techniques or public APIs to collect data from information sources.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising a natural language processing means for extracting evaluation trends and frequently occurring keywords from the analyzed data.

[0810] "Example 1"

[0811] (Claim 1)

[0812] Means for automatically collecting digital information from information sources,

[0813] A means of analyzing collected digital information to generate summary information,

[0814] A means of personalizing summary information based on the user's past preferences and interests,

[0815] A display means for visually displaying summary information to the user,

[0816] A system that includes means for automating purchase-related procedures based on user actions.

[0817] (Claim 2)

[0818] The system according to claim 1, which uses data mining techniques or a public application programming interface to collect digital information from a source.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising a natural language processing means for extracting reputation trends and important phrases from analyzed digital information.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] Means for automatically collecting data from information sources,

[0824] A means of analyzing collected data and generating a summary,

[0825] A means to customize summaries based on the user's past purchase history and preferences,

[0826] A means of providing users with summaries and suggesting products,

[0827] A system that includes means for automating the purchase process through an external database based on user selection.

[0828] (Claim 2)

[0829] The system according to claim 1, which utilizes data decomposition techniques or an open interface for data collection from an information source.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising a natural language processing means using a generated language model to extract opinion trends and high-frequency expressions from analyzed data.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] Means for automatically collecting data from information sources,

[0835] A means of formatting the collected data into a unified format,

[0836] A means of analyzing data and generating a summary that takes into account the user's emotional state,

[0837] A means of dynamically customizing the generated summary according to the user's emotions,

[0838] A means of providing users with customized summaries,

[0839] A means of analyzing emotional patterns based on user interaction data and providing feedback,

[0840] A system that includes means to automate the purchase process based on user selection.

[0841] (Claim 2)

[0842] The system according to claim 1, which utilizes scraping techniques or public APIs to collect data from information sources.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising means for analyzing data and recognizing emotional patterns using a generative AI model.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] Means for automatically collecting data from information sources,

[0848] A means of analyzing collected data and generating a summary,

[0849] A means of dynamically customizing summaries based on the user's emotional state and interests,

[0850] A means of providing users with customized summaries,

[0851] A system that includes means of automating the purchase process based on user choices and emotions.

[0852] (Claim 2)

[0853] The system according to claim 1, which utilizes scraping techniques or public APIs to collect data from information sources.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising means for extracting evaluation trends and frequently occurring keywords from analyzed data, and ranking information according to the user's sentiment based on these. [Explanation of Symbols]

[0856] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for automatically collecting data from information sources, A means of analyzing collected data and generating a summary, A means to customize summaries based on the user's past purchase history and preferences, A means of providing users with summaries and suggesting products, A system that includes means for automating the purchase process through an external database based on user selection.

2. The system according to claim 1, which utilizes data decomposition techniques or a public interface for data collection from an information source.

3. The system according to claim 1, comprising a natural language processing means using a generated language model to extract opinion trends and high-frequency expressions from analyzed data.

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