system

Abstract

We provide the system. [Solution] Means for obtaining personal attribute information, A means of searching for information based on acquired attribute information, A means of presenting the searched information in a format suitable for the individual, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 IT and communication industries, there is a problem that it is difficult for newly recruited employees and employees with different backgrounds to quickly understand technical terms and technical content. For this reason, work efficiency has decreased and communication barriers have occurred. Furthermore, there is a lack of an appropriate interface for effectively utilizing AI technology, which has become a major obstacle to its promotion.

Means for Solving the Problems

[0005] This system supports user understanding by acquiring personal attribute information and providing a means to automatically search for relevant technical information and materials based on that information. Furthermore, it promotes optimal understanding of the information by presenting the retrieved information in a format tailored to the individual's profile. In addition, it supports users' rapid knowledge acquisition by optimizing information considering their learning history and ensuring diversity of information sources.

[0006] "Personal attribute information" refers to information unique to an individual, such as the user's knowledge level, work experience, and skill set.

[0007] "Means of retrieving information" refers to a method or process of electronically collecting relevant information based on user attribute information.

[0008] "Information presentation format" refers to methods of presenting information, such as text, charts, and video links, in a way that is optimized for the user.

[0009] "Learning history" refers to a record of what information a user has learned in the past.

[0010] "Information sources" refer to starting resources such as databases, websites, and repositories used to obtain information. [Brief explanation of the drawing]

[0011] [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]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is an AI system that searches for relevant information based on a user's personal attribute information and provides it in a personalized format. Specifically, when a user enters a question or keyword into a terminal, this information and the user's profile data (knowledge level, work experience, etc.) are sent to a server. The server analyzes this information and uses natural language processing and related technologies to collect relevant information from multiple sources.

[0033] The server filters the collected information based on the user's profile and reorganizes it into the most suitable format. For example, it can generate guides with detailed explanations and procedures for beginners, and provide in-depth technical analysis and concise summaries for advanced users. The generated information is sent to the terminal and displayed through the user interface. This allows users to quickly and efficiently obtain the information they need.

[0034] As a concrete example, if a new employee wants to learn the "basics of cloud computing," they would enter this information into their terminal, and the server would perform a search based on the request and the user's knowledge level. Information including basic concepts for beginners, a visual guide, and links to relevant videos would be generated and displayed on the terminal. This process allows users to efficiently acquire the necessary knowledge without waste and apply it to their work.

[0035] The following describes the processing flow.

[0036] Step 1:

[0037] The user enters keywords or questions for the information they want to know into the device. In addition to this, the device retrieves the user's profile information (knowledge level, work experience, etc.).

[0038] Step 2:

[0039] The terminal sends the entered information and profile data to the server. The server receives this data and prepares it for analysis.

[0040] Step 3:

[0041] The server uses natural language processing to understand the user's actual information needs from their input. Based on this, it generates search queries to obtain relevant information.

[0042] Step 4:

[0043] The server uses the generated query to search for information from multiple databases and sources. The search results are stored in temporary memory and used for later processing.

[0044] Step 5:

[0045] The server references the user's profile and filters the retrieved information. It selects detailed explanations for beginners and concise summaries for advanced users.

[0046] Step 6:

[0047] The server personalizes filtered information and organizes it in the format best suited to the user, including text, charts, and video links as needed.

[0048] Step 7:

[0049] The server sends the organized content to the terminal. The terminal displays this to the user, and the user verifies the information.

[0050] Step 8:

[0051] Users can review the information and, if necessary, ask additional questions or perform more detailed searches.

[0052] (Example 1)

[0053] 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."

[0054] Existing information provision systems lack sufficient methods for personalizing information based on user characteristics and knowledge levels, making it difficult to efficiently acquire relevant information and provide it to users in the most optimal format.

[0055] 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.

[0056] In this invention, the server includes means for receiving and analyzing user characteristic information, means for collecting relevant information from multiple information sources using natural language processing technology, and means for organizing and providing the collected information in a format most suitable to the user's characteristics. This enables users to quickly obtain optimal information tailored to their individual characteristics.

[0057] "User characteristic information" refers to information about an individual's level of knowledge, work experience, and individual needs.

[0058] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used for information gathering and text analysis.

[0059] "Information sources" refer to entities that provide relevant information, such as various databases, websites, and online media on the internet.

[0060] "Collection" refers to the activity of a server searching for and gathering necessary information based on specified conditions.

[0061] "Organization" refers to the process of structuring collected information in a way that is easy for users to understand and converting it into a format suitable for its characteristics.

[0062] "Providing information in the optimal format" means presenting information in a way that is easy to understand and use, tailored to the user's level of understanding and characteristics.

[0063] To implement this invention, the system should be constructed as follows.

[0064] First, the user needs a device, which can be a computer or a smart device. The device must have an interface to receive user input, specifically including on-screen text boxes and voice input capabilities. The process begins when the user enters a prompt, such as "I want to learn the basics of cloud computing."

[0065] Next, the server receives data sent by the user and analyzes the user's characteristics. This process requires natural language processing technology, and it is recommended to use Python's NLTK library or the machine learning library TENSORFLOW®. Based on the individual's level of knowledge and characteristics, the server collects relevant information from the internet. This is done via data acquisition interfaces such as Google's® search API.

[0066] After information gathering is complete, the server organizes the information according to its characteristics. The information is processed as text, images, and video data, and visual guides and introductory videos are provided to explain concepts in an easy-to-understand way, especially for beginners. Next, this information is sent back to the terminal, and the user obtains the information through the interface.

[0067] Through this system, users can quickly acquire the knowledge they need and efficiently utilize the information in their work. An example of a prompt message would be, "Please create an introductory document on cloud computing that is easy for new employees to understand." In this way, by establishing specific embodiments of the invention, information provision optimized for the user can be realized.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] The user enters questions or keywords using a device. For example, the user might enter "I want to learn the basics of cloud computing." The input data is sent to the server in text format, and this becomes the initial data.

[0071] Step 2:

[0072] The server analyzes the text data received from the terminal. Natural language processing techniques are used for this analysis. Specifically, the Python NLTK library is used to tokenize the input text and extract the user's intent. This tokenized data is output as annotated keywords.

[0073] Step 3:

[0074] The server collects information from relevant databases and online sources based on the analysis results. It uses the Google Search API to gather relevant information from the internet. The input is processed keywords, and the output is a collection of primary data obtained from the information sources.

[0075] Step 4:

[0076] The server filters and organizes the collected data based on user characteristics. Information is organized according to the user's knowledge level. Beginner-friendly information is restructured to include visual guides and introductory videos. Input is information from diverse data sources, and output is customized information organized into a specific format.

[0077] Step 5:

[0078] The server sends organized information to the terminal. The output here is information presented in a format suitable for the user, which the terminal receives and displays through its user interface. The user can quickly check and understand the necessary information through the screen. Upon completion of this process, the user can efficiently acquire the necessary knowledge.

[0079] (Application Example 1)

[0080] 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."

[0081] Many digital services fail to effectively utilize user attribute information and past usage history, resulting in insufficient provision of personalized information and discounts. Such inefficient information delivery hinders the improvement of the user experience, and solutions are needed to address this issue.

[0082] 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.

[0083] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information, means for presenting the retrieved information in a format suitable for the individual, and means for providing payment methods and discount information based on the individual's transaction history. This enables users to receive the necessary information and discount services in real time in an individually optimized format.

[0084] "Acquiring personal attribute information" refers to the process of collecting basic user information and profile data from information systems.

[0085] "Searching for information based on acquired attribute information" refers to the operation of extracting data corresponding to the user's attribute information from existing databases or network information sources.

[0086] "Presenting searched information in a format tailored to the individual" refers to the process of optimizing and displaying collected information based on the user's level of understanding and preferences.

[0087] "Providing payment methods and discount information based on an individual's transaction history" refers to the process of analyzing a user's past purchase history and then presenting the most suitable payment method and benefits based on that analysis.

[0088] To realize this invention, it is first necessary to build a system for acquiring personal attribute information. The server will work in conjunction with the user's smartphone or personal computer to securely collect the user's basic information and profile data. In this process, information will be exchanged in real time using a cloud-based platform (e.g., AWS®, Google Cloud).

[0089] Next, information is searched based on the acquired attribute information. The server uses natural language processing technology to quickly extract relevant information from multiple databases and information sources. The retrieved information is analyzed using machine learning algorithms such as TensorFlow and optimized to suit the user's level of understanding and preferences.

[0090] To present information in a format tailored to the individual, the server generates a user interface configuration. This user interface is developed using UI frameworks such as React Native and designed to be intuitive for users to operate. This allows users to easily access information on their smartphones or personal computers.

[0091] In addition, based on an individual's transaction history, the server provides payment methods and discount information. The server analyzes past purchase data and presents the optimal payment method and relevant coupon information. Based on this, users can maximize the benefits they receive.

[0092] As a concrete example, when a user purchases tickets for a music event, the server provides discount information for cards previously used at similar events and suggests it to the user. This allows the user to purchase tickets in a more advantageous way.

[0093] Example of a prompt:

[0094] "Based on user macro information, please suggest the best discounts and payment methods for the next music event."

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The server retrieves user attribute information from the terminal. Input includes basic user information and profile data, and output is the storage of this data on the server. Specifically, information is collected through a secure data transfer protocol using SSL / TLS.

[0098] Step 2:

[0099] The server searches for relevant information based on the acquired attribute information. It receives user profile data as input and creates a list of relevant information as output. Data processing involves keyword extraction using natural language processing techniques and generating queries to information sources, collecting the necessary data from each source.

[0100] Step 3:

[0101] The server presents information in a format suitable for the user. Using searched information and user attribute information as input, it generates personalized information content as output. Specifically, it uses machine learning algorithms to estimate the user's level of understanding and selects the optimal presentation format for articles and reports.

[0102] Step 4:

[0103] The server provides payment methods and discount information based on the user's transaction history. It uses previous purchase history as input and generates recommended payment methods and discount information as output. Specifically, it analyzes transaction history using database queries and calculates the optimal payment option and discount using an algorithm.

[0104] Step 5:

[0105] The device displays information sent from the server. It uses personalized information received from the server as input and provides a visual display on the user interface as output. Specifically, it uses a UI framework to render content in a way that allows the user to intuitively interact with the information.

[0106] 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.

[0107] This invention is an AI system that recognizes emotions along with the user's personal attribute information and provides information based on that. When a user enters a question or keyword into a terminal, the terminal acquires that information and sends it to the server along with profile information such as the user's knowledge level and work experience. At the same time, the emotion engine analyzes the user's voice, facial expressions, input speed, etc., and generates emotion data.

[0108] The server receives user profile data and emotional data. This allows it to provide not only information tailored to the user's knowledge level, but also appropriate support based on their emotions. For example, if a user is feeling anxious or stressed, the server will present information in an easy-to-understand and simplified way, prioritizing reassuring language.

[0109] In actual operation, if the system determines that a user is feeling anxious about "learning new technology," it generates content with a gentle tone and plenty of supplementary explanations. Information is presented to the user in stages, and important points are highlighted repeatedly, employing techniques to aid understanding.

[0110] By presenting information in a way that takes the user's emotional state into consideration, learning and information absorption become more comfortable and effective, and flexible responses to user needs become possible.

[0111] The following describes the processing flow.

[0112] Step 1:

[0113] Users input what they want to learn or their questions into the device. Additionally, data on the user's voice and facial expressions are collected by an emotion engine via sensors and cameras.

[0114] Step 2:

[0115] The device sends the entered questions and keywords, the user's personal profile, and collected sentiment data to the server.

[0116] Step 3:

[0117] The server analyzes the received profile and sentiment data and generates search queries tailored to individual user needs.

[0118] Step 4:

[0119] The server uses the generated query to search for relevant information from multiple databases and sources. The results are stored in temporary memory.

[0120] Step 5:

[0121] The server filters the information it retrieves based on the user's knowledge level and emotional state. For users who are feeling anxious, it selects information that includes detailed explanations starting from the basics.

[0122] Step 6:

[0123] The server takes emotional data into account and personalizes and formats the information. It constructs information with a tone and structure that matches the emotional state and organizes it in the format that is best suited to the user.

[0124] Step 7:

[0125] The server sends the final content to the terminal. The terminal displays the information in a user interface, allowing the user to view it.

[0126] Step 8:

[0127] Users can review the presented information and repeat the process if they need further questions or to search for more details. Changes in emotions are also measured again and used in the repeating cycle.

[0128] (Example 2)

[0129] 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".

[0130] Users often experience anxiety and stress during learning and information gathering when presented with information that does not take into account their individual knowledge levels or emotional states. This can lead to difficulties in understanding the information and a decrease in learning effectiveness. Therefore, there is a need to provide more personalized information based on the user's personal attributes and emotional state.

[0131] 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.

[0132] In this invention, the server includes means for acquiring personal attribute information, means for analyzing the personal emotional state, and means for generating information in an optimal format according to the emotional state. This enables the provision of information adapted to the user's individual needs and current feelings, resulting in easy-to-understand and effective learning support.

[0133] "Personal attribute information" refers to information about a user's individual characteristics and background, such as their knowledge level, work experience, and learning history.

[0134] "Emotional state" refers to data about the emotions a user feels at a particular moment, such as anxiety, stress, and a sense of security.

[0135] "Analysis" is the process of examining collected data in detail in order to find certain laws or patterns.

[0136] "Generating in the optimal format" means creating information in a shape or structure that is easiest for the user to understand.

[0137] "Acquiring information in parallel" means collecting data from multiple sources simultaneously.

[0138] To implement this invention, the user first inputs questions or keywords using a terminal. The terminal acquires this input information and also retrieves attribute information such as the user's knowledge level and work experience from a database. Furthermore, it analyzes the user's emotional state using an emotion engine. For this analysis, it is recommended to use an image processing library for facial expression recognition and speech recognition software for speech analysis.

[0139] The device sends user input information, attribute information, and sentiment data to the server. The server receives this information and uses a generative AI model to generate information best suited to the user. For example, if information needs to be conveyed in a gentle tone, the server can send a prompt to the generative AI model such as, "Generate text that gently explains the concept of a new technology in a situation where the user is feeling anxious," thereby generating appropriate content.

[0140] The generated information is sent from the server to the terminal and presented to the user. For example, if a user feels anxious about learning a new technology, the server presents the information step-by-step, repeatedly highlighting key points to make it easier to understand. This increases the user's sense of security and improves the learning effect.

[0141] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0142] Step 1:

[0143] The user enters a question or keyword into the device.

[0144] The entered data is retrieved within the terminal and temporarily stored in a database. The terminal then prepares this input data for the next processing step.

[0145] Step 2:

[0146] The device retrieves the user's personal attribute information.

[0147] This involves referencing past user databases to retrieve personal attributes such as user knowledge levels and work experience. The obtained attribute information is then packetized along with the input data.

[0148] Step 3:

[0149] The device uses an emotion engine to analyze the user's emotional state.

[0150] A speech recognition module and image processing library are used to analyze the user's emotions from their voice and facial expressions. Voice data and camera footage are provided as input, and emotion data is generated based on this.

[0151] Step 4:

[0152] The terminal sends user input information, attribute information, and sentiment data to the server.

[0153] This involves securely and efficiently transmitting data to the server using protocols such as HTTP. This data is then used as input for the next step on the server.

[0154] Step 5:

[0155] The server receives data, and an AI model generates information.

[0156] The server analyzes the received data and sends prompt messages to an AI model. This AI model then outputs text optimized for the user's emotions and attributes, following the prompt messages.

[0157] Step 6:

[0158] The server sends the generated information to the terminal, and the terminal presents the information to the user.

[0159] At this stage, the information is processed to be displayed in a format that is easy for users to see and understand. User reactions are then analyzed again and used to improve future information provision.

[0160] (Application Example 2)

[0161] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0162] In the field of elderly care, there is a need for appropriate information provision and care that responds to the emotions of the users. However, conventional systems have made it difficult to accurately grasp the emotional state of users and provide information that responds accordingly in real time. In particular, when users are experiencing anxiety or stress, individualized support is necessary, but there has been a problem in providing effective support.

[0163] 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.

[0164] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information and emotional data, and means for presenting the retrieved information in a format optimized according to the individual's emotional state. This enables personalized information provision and optimized care based on the user's emotional state.

[0165] "Personal attribute information" refers to basic information such as the user's age, gender, occupation, learning history, and work experience.

[0166] "Emotional data" refers to data that indicates a user's psychological state, analyzed from factors such as their voice, facial expressions, and typing speed.

[0167] "Means of retrieving information" refers to the process of efficiently searching for appropriate information from databases and other sources based on user attribute information and sentiment data.

[0168] "Means of presenting retrieved information" refers to methods and technologies for displaying retrieved information in a format that is easy for the user to understand.

[0169] "Means for analyzing voice and facial expressions" refers to technical methods that use voice recognition and facial recognition technologies to infer the user's emotional state.

[0170] "Optimization" refers to the process of adjusting the content and format of the information provided to the user in the most effective way for that user, based on user attribute information and sentiment data.

[0171] "Information acquisition in parallel" refers to a technical method of simultaneously collecting and processing information from multiple sources.

[0172] "Prioritization" refers to the process of ranking the importance of acquired information and providing the most relevant information first.

[0173] This invention relates to a system for recognizing a user's personal attribute information and emotions, and providing information based on that information. The implementation method of this system is described below.

[0174] The server first retrieves user attribute information, including age, gender, learning history, and work experience. At this stage, the server centrally manages the attribute information provided by the user's device. Next, it collects user voice and facial expression data, for example, using the device's microphone and camera, and analyzes it using an emotion engine. This analysis utilizes emotion analysis technologies such as Google Cloud's Speech-to-Text API and Emotion AI.

[0175] Based on the acquired attribute information and sentiment data, the server utilizes a generative AI model to search for information appropriate to the user's state and generate it if necessary. In particular, advanced language models such as OpenAI's GPT series are used as generative AI models. This model acquires information in parallel from multiple sources and prioritizes them according to the user's sentiment state.

[0176] The searched information is presented in a format optimized for each individual user. This means that the content and presentation of the information are adjusted according to the user's level of understanding and emotional state. For example, for anxious users, relaxing language and visually reassuring designs are used.

[0177] For example, if a user is feeling anxious about learning a new technology, the generated prompt might read: "Provide additional materials and calm-toned explanations to users who are feeling anxious about learning a new technology to help them understand better." In this way, it becomes possible to provide user-optimized information and support in caregiving settings and various other fields.

[0178] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0179] Step 1:

[0180] The device collects the user's personal attribute information. This input includes the user's age, gender, learning history, and work experience. This data is then compiled and prepared for transmission to the server.

[0181] Step 2:

[0182] The device acquires the user's voice and facial expressions. It captures data using the camera and microphone and provides it as input to the emotion analysis engine. Emotion AI is used to analyze and output emotion data from the voice and facial expressions.

[0183] Step 3:

[0184] The server receives personal attribute information and sentiment data sent from the terminal. Based on this input, it uses a generative AI model to create prompts for searching and generating appropriate information.

[0185] Step 4:

[0186] The server sends prompt text to an AI model that generates or searches for the most relevant information based on the user's sentiment. The input is the prompt text, and the output is the informational content presented to the user.

[0187] Step 5:

[0188] The device receives optimized information and presents it to the user. Information is displayed in a format that responds to the user's emotional state, using visual and auditory elements. This allows the user to receive appropriate information and gain a sense of security.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Second Embodiment]

[0193] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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".

[0205] This invention is an AI system that searches for relevant information based on a user's personal attribute information and provides it in a personalized format. Specifically, when a user enters a question or keyword into a terminal, this information and the user's profile data (knowledge level, work experience, etc.) are sent to a server. The server analyzes this information and uses natural language processing and related technologies to collect relevant information from multiple sources.

[0206] The server filters the collected information based on the user's profile and reorganizes it into the most suitable format. For example, it can generate guides with detailed explanations and procedures for beginners, and provide in-depth technical analysis and concise summaries for advanced users. The generated information is sent to the terminal and displayed through the user interface. This allows users to quickly and efficiently obtain the information they need.

[0207] As a concrete example, if a new employee wants to learn the "basics of cloud computing," they would enter this information into their terminal, and the server would perform a search based on the request and the user's knowledge level. Information including basic concepts for beginners, a visual guide, and links to relevant videos would be generated and displayed on the terminal. This process allows users to efficiently acquire the necessary knowledge without waste and apply it to their work.

[0208] The following describes the processing flow.

[0209] Step 1:

[0210] The user enters keywords or questions for the information they want to know into the device. In addition to this, the device retrieves the user's profile information (knowledge level, work experience, etc.).

[0211] Step 2:

[0212] The terminal sends the entered information and profile data to the server. The server receives this data and prepares it for analysis.

[0213] Step 3:

[0214] The server uses natural language processing to understand the user's actual information needs from their input. Based on this, it generates search queries to obtain relevant information.

[0215] Step 4:

[0216] The server uses the generated query to search for information from multiple databases and sources. The search results are stored in temporary memory and used for later processing.

[0217] Step 5:

[0218] The server references the user's profile and filters the retrieved information. It selects detailed explanations for beginners and concise summaries for advanced users.

[0219] Step 6:

[0220] The server personalizes filtered information and organizes it in the format best suited to the user, including text, charts, and video links as needed.

[0221] Step 7:

[0222] The server sends the organized content to the terminal. The terminal displays this to the user, and the user verifies the information.

[0223] Step 8:

[0224] Users can review the information and, if necessary, ask additional questions or perform more detailed searches.

[0225] (Example 1)

[0226] 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."

[0227] Existing information provision systems lack sufficient methods for personalizing information based on user characteristics and knowledge levels, making it difficult to efficiently acquire relevant information and provide it to users in the most optimal format.

[0228] 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.

[0229] In this invention, the server includes means for receiving and analyzing user characteristic information, means for collecting relevant information from multiple information sources using natural language processing technology, and means for organizing and providing the collected information in a format most suitable to the user's characteristics. This enables users to quickly obtain optimal information tailored to their individual characteristics.

[0230] "User characteristic information" refers to information about an individual's level of knowledge, work experience, and individual needs.

[0231] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used for information gathering and text analysis.

[0232] "Information sources" refer to entities that provide relevant information, such as various databases, websites, and online media on the internet.

[0233] "Collection" refers to the activity of a server searching for and gathering necessary information based on specified conditions.

[0234] "Organization" refers to the process of structuring collected information in a way that is easy for users to understand and converting it into a format suitable for its characteristics.

[0235] "Providing information in the optimal format" means presenting information in a way that is easy to understand and use, tailored to the user's level of understanding and characteristics.

[0236] To implement this invention, the system should be constructed as follows.

[0237] First, the user needs a device, which can be a computer or a smart device. The device must have an interface to receive user input, specifically including on-screen text boxes and voice input capabilities. The process begins when the user enters a prompt, such as "I want to learn the basics of cloud computing."

[0238] Next, the server receives data sent by the user and analyzes the user's characteristics. This process requires natural language processing technology, and it is recommended to use Python's NLTK library or the machine learning library TensorFlow. Based on the individual's level of knowledge and characteristics, the server collects relevant information from the internet. This is done via data acquisition interfaces such as Google's search API.

[0239] After information gathering is complete, the server organizes the information according to its characteristics. The information is processed as text, images, and video data, and visual guides and introductory videos are provided to explain concepts in an easy-to-understand way, especially for beginners. Next, this information is sent back to the terminal, and the user obtains the information through the interface.

[0240] Through this system, users can quickly acquire the knowledge they need and efficiently utilize the information in their work. An example of a prompt message would be, "Please create an introductory document on cloud computing that is easy for new employees to understand." In this way, by establishing specific embodiments of the invention, information provision optimized for the user can be realized.

[0241] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0242] Step 1:

[0243] The user enters questions or keywords using a device. For example, the user might enter "I want to learn the basics of cloud computing." The input data is sent to the server in text format, and this becomes the initial data.

[0244] Step 2:

[0245] The server analyzes the text data received from the terminal. Natural language processing techniques are used for this analysis. Specifically, the Python NLTK library is used to tokenize the input text and extract the user's intent. This tokenized data is output as annotated keywords.

[0246] Step 3:

[0247] The server collects information from relevant databases and online sources based on the analysis results. It uses the Google Search API to gather relevant information from the internet. The input is processed keywords, and the output is a collection of primary data obtained from the information sources.

[0248] Step 4:

[0249] The server filters and organizes the collected data based on user characteristics. Information is organized according to the user's knowledge level. Beginner-friendly information is restructured to include visual guides and introductory videos. Input is information from diverse data sources, and output is customized information organized into a specific format.

[0250] Step 5:

[0251] The server sends organized information to the terminal. The output here is information presented in a format suitable for the user, which the terminal receives and displays through its user interface. The user can quickly check and understand the necessary information through the screen. Upon completion of this process, the user can efficiently acquire the necessary knowledge.

[0252] (Application Example 1)

[0253] 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."

[0254] Many digital services fail to effectively utilize user attribute information and past usage history, resulting in insufficient provision of personalized information and discounts. Such inefficient information delivery hinders the improvement of the user experience, and solutions are needed to address this issue.

[0255] 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.

[0256] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information, means for presenting the retrieved information in a format suitable for the individual, and means for providing payment methods and discount information based on the individual's transaction history. This enables users to receive the necessary information and discount services in real time in an individually optimized format.

[0257] "Acquiring personal attribute information" refers to the process of collecting basic user information and profile data from information systems.

[0258] "Searching for information based on acquired attribute information" refers to the operation of extracting data corresponding to the user's attribute information from existing databases or network information sources.

[0259] "Presenting searched information in a format tailored to the individual" refers to the process of optimizing and displaying collected information based on the user's level of understanding and preferences.

[0260] "Providing payment methods and discount information based on an individual's transaction history" refers to the process of analyzing a user's past purchase history and then presenting the most suitable payment method and benefits based on that analysis.

[0261] To realize this invention, it is first necessary to build a system for acquiring personal attribute information. The server will work in conjunction with the user's smartphone or personal computer to securely collect the user's basic information and profile data. In this process, information will be exchanged in real time using a cloud-based platform (e.g., AWS, Google Cloud).

[0262] Next, information is searched based on the acquired attribute information. The server uses natural language processing technology to quickly extract relevant information from multiple databases and information sources. The retrieved information is analyzed using machine learning algorithms such as TensorFlow and optimized to suit the user's level of understanding and preferences.

[0263] To present information in a format tailored to the individual, the server generates a user interface configuration. This user interface is developed using UI frameworks such as React Native and designed to be intuitive for users to operate. This allows users to easily access information on their smartphones or personal computers.

[0264] In addition, based on an individual's transaction history, the server provides payment methods and discount information. The server analyzes past purchase data and presents the optimal payment method and relevant coupon information. Based on this, users can maximize the benefits they receive.

[0265] As a concrete example, when a user purchases tickets for a music event, the server provides discount information for cards previously used at similar events and suggests it to the user. This allows the user to purchase tickets in a more advantageous way.

[0266] Example of a prompt:

[0267] "Based on user macro information, please suggest the best discounts and payment methods for the next music event."

[0268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0269] Step 1:

[0270] The server retrieves user attribute information from the terminal. Input includes basic user information and profile data, and output is the storage of this data on the server. Specifically, information is collected through a secure data transfer protocol using SSL / TLS.

[0271] Step 2:

[0272] The server searches for relevant information based on the acquired attribute information. It receives user profile data as input and creates a list of relevant information as output. Data processing involves keyword extraction using natural language processing techniques and generating queries to information sources, collecting the necessary data from each source.

[0273] Step 3:

[0274] The server presents information in a format suitable for the user. Using searched information and user attribute information as input, it generates personalized information content as output. Specifically, it uses machine learning algorithms to estimate the user's level of understanding and selects the optimal presentation format for articles and reports.

[0275] Step 4:

[0276] The server provides payment methods and discount information based on the user's transaction history. It uses previous purchase history as input and generates recommended payment methods and discount information as output. Specifically, it analyzes transaction history using database queries and calculates the optimal payment option and discount using an algorithm.

[0277] Step 5:

[0278] The device displays information sent from the server. It uses personalized information received from the server as input and provides a visual display on the user interface as output. Specifically, it uses a UI framework to render content in a way that allows the user to intuitively interact with the information.

[0279] 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.

[0280] This invention is an AI system that recognizes emotions along with the user's personal attribute information and provides information based on that. When a user enters a question or keyword into a terminal, the terminal acquires that information and sends it to the server along with profile information such as the user's knowledge level and work experience. At the same time, the emotion engine analyzes the user's voice, facial expressions, input speed, etc., and generates emotion data.

[0281] The server receives user profile data and emotional data. This allows it to provide not only information tailored to the user's knowledge level, but also appropriate support based on their emotions. For example, if a user is feeling anxious or stressed, the server will present information in an easy-to-understand and simplified way, prioritizing reassuring language.

[0282] In actual operation, if the system determines that a user is feeling anxious about "learning new technology," it generates content with a gentle tone and plenty of supplementary explanations. Information is presented to the user in stages, and important points are highlighted repeatedly, employing techniques to aid understanding.

[0283] By presenting information in a way that takes the user's emotional state into consideration, learning and information absorption become more comfortable and effective, and flexible responses to user needs become possible.

[0284] The processing flow will be described below.

[0285] Step 1:

[0286] The user inputs the content to be learned and questions into the terminal. Also, through sensors and cameras, voice and facial expression data of the user are collected by the emotion engine.

[0287] Step 2:

[0288] The terminal transmits the input questions, keywords, the user's personal profile, and the collected emotion data to the server.

[0289] Step 3:

[0290] The server analyzes the received profile data and emotion data and generates a search query according to individual user needs.

[0291] Step 4:

[0292] The server uses the generated query to search for relevant information from multiple databases and information sources. The obtained results are stored in temporary memory.

[0293] Step 5:

[0294] The server filters the obtained information based on the user's knowledge level and emotional state. For users feeling anxious, information containing detailed explanations from the basics is selected.

[0295] Step 6:

[0296] The server takes the emotion data into account, personalizes and formats the information. The information is constructed in a tone and structure according to the emotional state, and organized in the optimal format for the user.

[0297] Step 7:

[0298] The server sends the final content to the terminal. The terminal displays the information in a user interface, allowing the user to view it.

[0299] Step 8:

[0300] Users can review the presented information and repeat the process if they need further questions or to search for more details. Changes in emotions are also measured again and used in the repeating cycle.

[0301] (Example 2)

[0302] 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".

[0303] Users often experience anxiety and stress during learning and information gathering when presented with information that does not take into account their individual knowledge levels or emotional states. This can lead to difficulties in understanding the information and a decrease in learning effectiveness. Therefore, there is a need to provide more personalized information based on the user's personal attributes and emotional state.

[0304] 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.

[0305] In this invention, the server includes means for acquiring personal attribute information, means for analyzing the personal emotional state, and means for generating information in an optimal format according to the emotional state. This enables the provision of information adapted to the user's individual needs and current feelings, resulting in easy-to-understand and effective learning support.

[0306] "Personal attribute information" refers to information about a user's individual characteristics and background, such as their knowledge level, work experience, and learning history.

[0307] The "emotional state" refers to data related to emotions such as uneasiness, stress, and a sense of security that a user feels at a specific moment.

[0308] "Analyze" refers to the process of examining data in detail to find certain laws or patterns from the collected data.

[0309] "Generate in the optimal format" means creating information in a shape or structure that is most easily understood by the user.

[0310] "Acquire information in parallel" means collecting data simultaneously from multiple information sources.

[0311] To implement this invention, the user first uses a terminal to input questions or keywords. The terminal acquires this input information and also acquires attribute information such as the user's knowledge level and work experience from a database. In addition, it analyzes the user's emotional state using an emotion engine. For this analysis, it is recommended to use an image processing library for facial expression recognition and speech recognition software for voice analysis.

[0312] The terminal transmits the user's input information, attribute information, and emotional data to the server. The server receives this information and uses a generation AI model to generate optimal information for the user. For example, when it is necessary to convey information in a gentle tone, appropriate content is generated by transmitting a prompt sentence such as "Generate a text that gently explains the concept of a new technology in a situation where the user is feeling uneasy" to the generation AI model.

[0313] The generated information is transmitted from the server to the terminal and presented to the user. For example, when the user feels uneasy about learning a new technology, the server presents the information step by step and emphasizes important points repeatedly in a format that is easy to understand. As a result, the user's sense of security is improved and the learning effect is enhanced.

[0314] The flow of the specific process in Example 2 will be described using FIG. 13.

[0315] Step 1:

[0316] The user enters a question or keyword into the device.

[0317] The entered data is retrieved within the terminal and temporarily stored in a database. The terminal then prepares this input data for the next processing step.

[0318] Step 2:

[0319] The device retrieves the user's personal attribute information.

[0320] This involves referencing past user databases to retrieve personal attributes such as user knowledge levels and work experience. The obtained attribute information is then packetized along with the input data.

[0321] Step 3:

[0322] The device uses an emotion engine to analyze the user's emotional state.

[0323] A speech recognition module and image processing library are used to analyze the user's emotions from their voice and facial expressions. Voice data and camera footage are provided as input, and emotion data is generated based on this.

[0324] Step 4:

[0325] The terminal sends user input information, attribute information, and sentiment data to the server.

[0326] This involves securely and efficiently transmitting data to the server using protocols such as HTTP. This data is then used as input for the next step on the server.

[0327] Step 5:

[0328] The server receives data, and an AI model generates information.

[0329] The server analyzes the received data and sends prompt messages to an AI model. This AI model then outputs text optimized for the user's emotions and attributes, following the prompt messages.

[0330] Step 6:

[0331] The server sends the generated information to the terminal, and the terminal presents the information to the user.

[0332] At this stage, the information is processed to be displayed in a format that is easy for users to see and understand. User reactions are then analyzed again and used to improve future information provision.

[0333] (Application Example 2)

[0334] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0335] In the field of elderly care, there is a need for appropriate information provision and care that responds to the emotions of the users. However, conventional systems have made it difficult to accurately grasp the emotional state of users and provide information that responds accordingly in real time. In particular, when users are experiencing anxiety or stress, individualized support is necessary, but there has been a problem in providing effective support.

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

[0337] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information and emotional data, and means for presenting the retrieved information in a format optimized according to the individual's emotional state. This enables personalized information provision and optimized care based on the user's emotional state.

[0338] "Personal attribute information" refers to basic information such as the user's age, gender, occupation, learning history, and work experience.

[0339] "Emotional data" refers to data that indicates a user's psychological state, analyzed from factors such as their voice, facial expressions, and typing speed.

[0340] "Means of retrieving information" refers to the process of efficiently searching for appropriate information from databases and other sources based on user attribute information and sentiment data.

[0341] "Means of presenting retrieved information" refers to methods and technologies for displaying retrieved information in a format that is easy for the user to understand.

[0342] "Means for analyzing voice and facial expressions" refers to technical methods that use voice recognition and facial recognition technologies to infer the user's emotional state.

[0343] "Optimization" refers to the process of adjusting the content and format of the information provided to the user in the most effective way for that user, based on user attribute information and sentiment data.

[0344] "Information acquisition in parallel" refers to a technical method of simultaneously collecting and processing information from multiple sources.

[0345] "Prioritization" refers to the process of ranking the importance of acquired information and providing the most relevant information first.

[0346] This invention relates to a system for recognizing a user's personal attribute information and emotions, and providing information based on that information. The implementation method of this system is described below.

[0347] The server first retrieves user attribute information, including age, gender, learning history, and work experience. At this stage, the server centrally manages the attribute information provided by the user's device. Next, it collects user voice and facial expression data, for example, using the device's microphone and camera, and analyzes it using an emotion engine. This analysis utilizes emotion analysis technologies such as Google Cloud's Speech-to-Text API and Emotion AI.

[0348] Based on the acquired attribute information and sentiment data, the server utilizes a generative AI model to search for information appropriate to the user's state and generate it if necessary. In particular, advanced language models such as OpenAI's GPT series are used as generative AI models. This model acquires information in parallel from multiple sources and prioritizes them according to the user's sentiment state.

[0349] The searched information is presented in a format optimized for each individual user. This means that the content and presentation of the information are adjusted according to the user's level of understanding and emotional state. For example, for anxious users, relaxing language and visually reassuring designs are used.

[0350] For example, if a user is feeling anxious about learning a new technology, the generated prompt might read: "Provide additional materials and calm-toned explanations to users who are feeling anxious about learning a new technology to help them understand better." In this way, it becomes possible to provide user-optimized information and support in caregiving settings and various other fields.

[0351] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0352] Step 1:

[0353] The device collects the user's personal attribute information. This input includes the user's age, gender, learning history, and work experience. This data is then compiled and prepared for transmission to the server.

[0354] Step 2:

[0355] The device acquires the user's voice and facial expressions. It captures data using the camera and microphone and provides it as input to the emotion analysis engine. Emotion AI is used to analyze and output emotion data from the voice and facial expressions.

[0356] Step 3:

[0357] The server receives personal attribute information and sentiment data sent from the terminal. Based on this input, it uses a generative AI model to create prompts for searching and generating appropriate information.

[0358] Step 4:

[0359] The server sends prompt text to an AI model that generates or searches for the most relevant information based on the user's sentiment. The input is the prompt text, and the output is the informational content presented to the user.

[0360] Step 5:

[0361] The device receives optimized information and presents it to the user. Information is displayed in a format that responds to the user's emotional state, using visual and auditory elements. This allows the user to receive appropriate information and gain a sense of security.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] [Third Embodiment]

[0366] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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".

[0378] This invention is an AI system that searches for relevant information based on a user's personal attribute information and provides it in a personalized format. Specifically, when a user enters a question or keyword into a terminal, this information and the user's profile data (knowledge level, work experience, etc.) are sent to a server. The server analyzes this information and uses natural language processing and related technologies to collect relevant information from multiple sources.

[0379] The server filters the collected information based on the user's profile and reorganizes it into the most suitable format. For example, it can generate guides with detailed explanations and procedures for beginners, and provide in-depth technical analysis and concise summaries for advanced users. The generated information is sent to the terminal and displayed through the user interface. This allows users to quickly and efficiently obtain the information they need.

[0380] As a concrete example, if a new employee wants to learn the "basics of cloud computing," they would enter this information into their terminal, and the server would perform a search based on the request and the user's knowledge level. Information including basic concepts for beginners, a visual guide, and links to relevant videos would be generated and displayed on the terminal. This process allows users to efficiently acquire the necessary knowledge without waste and apply it to their work.

[0381] The following describes the processing flow.

[0382] Step 1:

[0383] The user enters keywords or questions for the information they want to know into the device. In addition to this, the device retrieves the user's profile information (knowledge level, work experience, etc.).

[0384] Step 2:

[0385] The terminal sends the entered information and profile data to the server. The server receives this data and prepares it for analysis.

[0386] Step 3:

[0387] The server uses natural language processing to understand the user's actual information needs from their input. Based on this, it generates search queries to obtain relevant information.

[0388] Step 4:

[0389] The server uses the generated query to search for information from multiple databases and sources. The search results are stored in temporary memory and used for later processing.

[0390] Step 5:

[0391] The server references the user's profile and filters the retrieved information. It selects detailed explanations for beginners and concise summaries for advanced users.

[0392] Step 6:

[0393] The server personalizes filtered information and organizes it in the format best suited to the user, including text, charts, and video links as needed.

[0394] Step 7:

[0395] The server sends the organized content to the terminal. The terminal displays this to the user, and the user verifies the information.

[0396] Step 8:

[0397] Users can review the information and, if necessary, ask additional questions or perform more detailed searches.

[0398] (Example 1)

[0399] 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."

[0400] Existing information provision systems lack sufficient methods for personalizing information based on user characteristics and knowledge levels, making it difficult to efficiently acquire relevant information and provide it to users in the most optimal format.

[0401] 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.

[0402] In this invention, the server includes means for receiving and analyzing user characteristic information, means for collecting relevant information from multiple information sources using natural language processing technology, and means for organizing and providing the collected information in a format most suitable to the user's characteristics. This enables users to quickly obtain optimal information tailored to their individual characteristics.

[0403] "User characteristic information" refers to information about an individual's level of knowledge, work experience, and individual needs.

[0404] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used for information gathering and text analysis.

[0405] "Information sources" refer to entities that provide relevant information, such as various databases, websites, and online media on the internet.

[0406] "Collection" refers to the activity of a server searching for and gathering necessary information based on specified conditions.

[0407] "Organization" refers to the process of structuring collected information in a way that is easy for users to understand and converting it into a format suitable for its characteristics.

[0408] "Providing information in the optimal format" means presenting information in a way that is easy to understand and use, tailored to the user's level of understanding and characteristics.

[0409] To implement this invention, the system should be constructed as follows.

[0410] First, the user needs a device, which can be a computer or a smart device. The device must have an interface to receive user input, specifically including on-screen text boxes and voice input capabilities. The process begins when the user enters a prompt, such as "I want to learn the basics of cloud computing."

[0411] Next, the server receives data sent by the user and analyzes the user's characteristics. This process requires natural language processing technology, and it is recommended to use Python's NLTK library or the machine learning library TensorFlow. Based on the individual's level of knowledge and characteristics, the server collects relevant information from the internet. This is done via data acquisition interfaces such as Google's search API.

[0412] After information gathering is complete, the server organizes the information according to its characteristics. The information is processed as text, images, and video data, and visual guides and introductory videos are provided to explain concepts in an easy-to-understand way, especially for beginners. Next, this information is sent back to the terminal, and the user obtains the information through the interface.

[0413] Through this system, users can quickly acquire the knowledge they need and efficiently utilize the information in their work. An example of a prompt message would be, "Please create an introductory document on cloud computing that is easy for new employees to understand." In this way, by establishing specific embodiments of the invention, information provision optimized for the user can be realized.

[0414] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0415] Step 1:

[0416] The user enters questions or keywords using a device. For example, the user might enter "I want to learn the basics of cloud computing." The input data is sent to the server in text format, and this becomes the initial data.

[0417] Step 2:

[0418] The server analyzes the text data received from the terminal. Natural language processing techniques are used for this analysis. Specifically, the Python NLTK library is used to tokenize the input text and extract the user's intent. This tokenized data is output as annotated keywords.

[0419] Step 3:

[0420] The server collects information from relevant databases and online sources based on the analysis results. It uses the Google Search API to gather relevant information from the internet. The input is processed keywords, and the output is a collection of primary data obtained from the information sources.

[0421] Step 4:

[0422] The server filters and organizes the collected data based on user characteristics. Information is organized according to the user's knowledge level. Beginner-friendly information is restructured to include visual guides and introductory videos. Input is information from diverse data sources, and output is customized information organized into a specific format.

[0423] Step 5:

[0424] The server sends organized information to the terminal. The output here is information presented in a format suitable for the user, which the terminal receives and displays through its user interface. The user can quickly check and understand the necessary information through the screen. Upon completion of this process, the user can efficiently acquire the necessary knowledge.

[0425] (Application Example 1)

[0426] 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."

[0427] Many digital services fail to effectively utilize user attribute information and past usage history, resulting in insufficient provision of personalized information and discounts. Such inefficient information delivery hinders the improvement of the user experience, and solutions are needed to address this issue.

[0428] 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.

[0429] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information, means for presenting the retrieved information in a format suitable for the individual, and means for providing payment methods and discount information based on the individual's transaction history. This enables users to receive the necessary information and discount services in real time in an individually optimized format.

[0430] "Acquiring personal attribute information" refers to the process of collecting basic user information and profile data from information systems.

[0431] "Searching for information based on acquired attribute information" refers to the operation of extracting data corresponding to the user's attribute information from existing databases or network information sources.

[0432] "Presenting searched information in a format tailored to the individual" refers to the process of optimizing and displaying collected information based on the user's level of understanding and preferences.

[0433] "Providing payment methods and discount information based on an individual's transaction history" refers to the process of analyzing a user's past purchase history and then presenting the most suitable payment method and benefits based on that analysis.

[0434] To realize this invention, it is first necessary to build a system for acquiring personal attribute information. The server will work in conjunction with the user's smartphone or personal computer to securely collect the user's basic information and profile data. In this process, information will be exchanged in real time using a cloud-based platform (e.g., AWS, Google Cloud).

[0435] Next, information is searched based on the acquired attribute information. The server uses natural language processing technology to quickly extract relevant information from multiple databases and information sources. The retrieved information is analyzed using machine learning algorithms such as TensorFlow and optimized to suit the user's level of understanding and preferences.

[0436] To present information in a format tailored to the individual, the server generates a user interface configuration. This user interface is developed using UI frameworks such as React Native and designed to be intuitive for users to operate. This allows users to easily access information on their smartphones or personal computers.

[0437] In addition, based on an individual's transaction history, the server provides payment methods and discount information. The server analyzes past purchase data and presents the optimal payment method and relevant coupon information. Based on this, users can maximize the benefits they receive.

[0438] As a concrete example, when a user purchases tickets for a music event, the server provides discount information for cards previously used at similar events and suggests it to the user. This allows the user to purchase tickets in a more advantageous way.

[0439] Example of a prompt:

[0440] "Based on user macro information, please suggest the best discounts and payment methods for the next music event."

[0441] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0442] Step 1:

[0443] The server retrieves user attribute information from the terminal. Input includes basic user information and profile data, and output is the storage of this data on the server. Specifically, information is collected through a secure data transfer protocol using SSL / TLS.

[0444] Step 2:

[0445] The server searches for relevant information based on the acquired attribute information. It receives user profile data as input and creates a list of relevant information as output. Data processing involves keyword extraction using natural language processing techniques and generating queries to information sources, collecting the necessary data from each source.

[0446] Step 3:

[0447] The server presents information in a format suitable for the user. Using searched information and user attribute information as input, it generates personalized information content as output. Specifically, it uses machine learning algorithms to estimate the user's level of understanding and selects the optimal presentation format for articles and reports.

[0448] Step 4:

[0449] The server provides payment methods and discount information based on the user's transaction history. It uses previous purchase history as input and generates recommended payment methods and discount information as output. Specifically, it analyzes transaction history using database queries and calculates the optimal payment option and discount using an algorithm.

[0450] Step 5:

[0451] The device displays information sent from the server. It uses personalized information received from the server as input and provides a visual display on the user interface as output. Specifically, it uses a UI framework to render content in a way that allows the user to intuitively interact with the information.

[0452] 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.

[0453] This invention is an AI system that recognizes emotions along with the user's personal attribute information and provides information based on that. When a user enters a question or keyword into a terminal, the terminal acquires that information and sends it to the server along with profile information such as the user's knowledge level and work experience. At the same time, the emotion engine analyzes the user's voice, facial expressions, input speed, etc., and generates emotion data.

[0454] The server receives user profile data and emotional data. This allows it to provide not only information tailored to the user's knowledge level, but also appropriate support based on their emotions. For example, if a user is feeling anxious or stressed, the server will present information in an easy-to-understand and simplified way, prioritizing reassuring language.

[0455] In actual operation, if the system determines that a user is feeling anxious about "learning new technology," it generates content with a gentle tone and plenty of supplementary explanations. Information is presented to the user in stages, and important points are highlighted repeatedly, employing techniques to aid understanding.

[0456] By presenting information in a way that takes the user's emotional state into consideration, learning and information absorption become more comfortable and effective, and flexible responses to user needs become possible.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] Users input what they want to learn or their questions into the device. Additionally, data on the user's voice and facial expressions are collected by an emotion engine via sensors and cameras.

[0460] Step 2:

[0461] The device sends the entered questions and keywords, the user's personal profile, and collected sentiment data to the server.

[0462] Step 3:

[0463] The server analyzes the received profile and sentiment data and generates search queries tailored to individual user needs.

[0464] Step 4:

[0465] The server uses the generated query to search for relevant information from multiple databases and sources. The results are stored in temporary memory.

[0466] Step 5:

[0467] The server filters the information it retrieves based on the user's knowledge level and emotional state. For users who are feeling anxious, it selects information that includes detailed explanations starting from the basics.

[0468] Step 6:

[0469] The server takes emotional data into account and personalizes and formats the information. It constructs information with a tone and structure that matches the emotional state and organizes it in the format that is best suited to the user.

[0470] Step 7:

[0471] The server sends the final content to the terminal. The terminal displays the information in a user interface, allowing the user to view it.

[0472] Step 8:

[0473] Users can review the presented information and repeat the process if they need further questions or to search for more details. Changes in emotions are also measured again and used in the repeating cycle.

[0474] (Example 2)

[0475] 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."

[0476] Users often experience anxiety and stress during learning and information gathering when presented with information that does not take into account their individual knowledge levels or emotional states. This can lead to difficulties in understanding the information and a decrease in learning effectiveness. Therefore, there is a need to provide more personalized information based on the user's personal attributes and emotional state.

[0477] 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.

[0478] In this invention, the server includes means for acquiring personal attribute information, means for analyzing the personal emotional state, and means for generating information in an optimal format according to the emotional state. This enables the provision of information adapted to the user's individual needs and current feelings, resulting in easy-to-understand and effective learning support.

[0479] "Personal attribute information" refers to information about a user's individual characteristics and background, such as their knowledge level, work experience, and learning history.

[0480] "Emotional state" refers to data about the emotions a user feels at a particular moment, such as anxiety, stress, and a sense of security.

[0481] "Analysis" is the process of examining collected data in detail in order to find certain laws or patterns.

[0482] "Generating in the optimal format" means creating information in a shape or structure that is easiest for the user to understand.

[0483] "Acquiring information in parallel" means collecting data from multiple sources simultaneously.

[0484] To implement this invention, the user first inputs questions or keywords using a terminal. The terminal acquires this input information and also retrieves attribute information such as the user's knowledge level and work experience from a database. Furthermore, it analyzes the user's emotional state using an emotion engine. For this analysis, it is recommended to use an image processing library for facial expression recognition and speech recognition software for speech analysis.

[0485] The device sends user input information, attribute information, and sentiment data to the server. The server receives this information and uses a generative AI model to generate information best suited to the user. For example, if information needs to be conveyed in a gentle tone, the server can send a prompt to the generative AI model such as, "Generate text that gently explains the concept of a new technology in a situation where the user is feeling anxious," thereby generating appropriate content.

[0486] The generated information is sent from the server to the terminal and presented to the user. For example, if a user feels anxious about learning a new technology, the server presents the information step-by-step, repeatedly highlighting key points to make it easier to understand. This increases the user's sense of security and improves the learning effect.

[0487] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0488] Step 1:

[0489] The user enters a question or keyword into the device.

[0490] The entered data is retrieved within the terminal and temporarily stored in a database. The terminal then prepares this input data for the next processing step.

[0491] Step 2:

[0492] The device retrieves the user's personal attribute information.

[0493] This involves referencing past user databases to retrieve personal attributes such as user knowledge levels and work experience. The obtained attribute information is then packetized along with the input data.

[0494] Step 3:

[0495] The device uses an emotion engine to analyze the user's emotional state.

[0496] A speech recognition module and image processing library are used to analyze the user's emotions from their voice and facial expressions. Voice data and camera footage are provided as input, and emotion data is generated based on this.

[0497] Step 4:

[0498] The terminal sends user input information, attribute information, and sentiment data to the server.

[0499] This involves securely and efficiently transmitting data to the server using protocols such as HTTP. This data is then used as input for the next step on the server.

[0500] Step 5:

[0501] The server receives data, and an AI model generates information.

[0502] The server analyzes the received data and sends prompt messages to an AI model. This AI model then outputs text optimized for the user's emotions and attributes, following the prompt messages.

[0503] Step 6:

[0504] The server sends the generated information to the terminal, and the terminal presents the information to the user.

[0505] At this stage, the information is processed to be displayed in a format that is easy for users to see and understand. User reactions are then analyzed again and used to improve future information provision.

[0506] (Application Example 2)

[0507] 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."

[0508] In the field of elderly care, there is a need for appropriate information provision and care that responds to the emotions of the users. However, conventional systems have made it difficult to accurately grasp the emotional state of users and provide information that responds accordingly in real time. In particular, when users are experiencing anxiety or stress, individualized support is necessary, but there has been a problem in providing effective support.

[0509] 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.

[0510] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information and emotional data, and means for presenting the retrieved information in a format optimized according to the individual's emotional state. This enables personalized information provision and optimized care based on the user's emotional state.

[0511] "Personal attribute information" refers to basic information such as the user's age, gender, occupation, learning history, and work experience.

[0512] "Emotional data" refers to data that indicates a user's psychological state, analyzed from factors such as their voice, facial expressions, and typing speed.

[0513] "Means of retrieving information" refers to the process of efficiently searching for appropriate information from databases and other sources based on user attribute information and sentiment data.

[0514] "Means of presenting retrieved information" refers to methods and technologies for displaying retrieved information in a format that is easy for the user to understand.

[0515] "Means for analyzing voice and facial expressions" refers to technical methods that use voice recognition and facial recognition technologies to infer the user's emotional state.

[0516] "Optimization" refers to the process of adjusting the content and format of the information provided to the user in the most effective way for that user, based on user attribute information and sentiment data.

[0517] "Information acquisition in parallel" refers to a technical method of simultaneously collecting and processing information from multiple sources.

[0518] "Prioritization" refers to the process of ranking the importance of acquired information and providing the most relevant information first.

[0519] This invention relates to a system for recognizing a user's personal attribute information and emotions, and providing information based on that information. The implementation method of this system is described below.

[0520] The server first retrieves user attribute information, including age, gender, learning history, and work experience. At this stage, the server centrally manages the attribute information provided by the user's device. Next, it collects user voice and facial expression data, for example, using the device's microphone and camera, and analyzes it using an emotion engine. This analysis utilizes emotion analysis technologies such as Google Cloud's Speech-to-Text API and Emotion AI.

[0521] Based on the acquired attribute information and sentiment data, the server utilizes a generative AI model to search for information appropriate to the user's state and generate it if necessary. In particular, advanced language models such as OpenAI's GPT series are used as generative AI models. This model acquires information in parallel from multiple sources and prioritizes them according to the user's sentiment state.

[0522] The searched information is presented in a format optimized for each individual user. This means that the content and presentation of the information are adjusted according to the user's level of understanding and emotional state. For example, for anxious users, relaxing language and visually reassuring designs are used.

[0523] For example, if a user is feeling anxious about learning a new technology, the generated prompt might read: "Provide additional materials and calm-toned explanations to users who are feeling anxious about learning a new technology to help them understand better." In this way, it becomes possible to provide user-optimized information and support in caregiving settings and various other fields.

[0524] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0525] Step 1:

[0526] The device collects the user's personal attribute information. This input includes the user's age, gender, learning history, and work experience. This data is then compiled and prepared for transmission to the server.

[0527] Step 2:

[0528] The device acquires the user's voice and facial expressions. It captures data using the camera and microphone and provides it as input to the emotion analysis engine. Emotion AI is used to analyze and output emotion data from the voice and facial expressions.

[0529] Step 3:

[0530] The server receives personal attribute information and sentiment data sent from the terminal. Based on this input, it uses a generative AI model to create prompts for searching and generating appropriate information.

[0531] Step 4:

[0532] The server sends prompt text to an AI model that generates or searches for the most relevant information based on the user's sentiment. The input is the prompt text, and the output is the informational content presented to the user.

[0533] Step 5:

[0534] The device receives optimized information and presents it to the user. Information is displayed in a format that responds to the user's emotional state, using visual and auditory elements. This allows the user to receive appropriate information and gain a sense of security.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] [Fourth Embodiment]

[0539] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0540] 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.

[0541] 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).

[0542] 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.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] 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".

[0552] This invention is an AI system that searches for relevant information based on a user's personal attribute information and provides it in a personalized format. Specifically, when a user enters a question or keyword into a terminal, this information and the user's profile data (knowledge level, work experience, etc.) are sent to a server. The server analyzes this information and uses natural language processing and related technologies to collect relevant information from multiple sources.

[0553] The server filters the collected information based on the user's profile and reorganizes it into the most suitable format. For example, it can generate guides with detailed explanations and procedures for beginners, and provide in-depth technical analysis and concise summaries for advanced users. The generated information is sent to the terminal and displayed through the user interface. This allows users to quickly and efficiently obtain the information they need.

[0554] As a concrete example, if a new employee wants to learn the "basics of cloud computing," they would enter this information into their terminal, and the server would perform a search based on the request and the user's knowledge level. Information including basic concepts for beginners, a visual guide, and links to relevant videos would be generated and displayed on the terminal. This process allows users to efficiently acquire the necessary knowledge without waste and apply it to their work.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The user enters keywords or questions for the information they want to know into the device. In addition to this, the device retrieves the user's profile information (knowledge level, work experience, etc.).

[0558] Step 2:

[0559] The terminal sends the entered information and profile data to the server. The server receives this data and prepares it for analysis.

[0560] Step 3:

[0561] The server uses natural language processing to understand the user's actual information needs from their input. Based on this, it generates search queries to obtain relevant information.

[0562] Step 4:

[0563] The server uses the generated query to search for information from multiple databases and sources. The search results are stored in temporary memory and used for later processing.

[0564] Step 5:

[0565] The server references the user's profile and filters the retrieved information. It selects detailed explanations for beginners and concise summaries for advanced users.

[0566] Step 6:

[0567] The server personalizes filtered information and organizes it in the format best suited to the user, including text, charts, and video links as needed.

[0568] Step 7:

[0569] The server sends the organized content to the terminal. The terminal displays this to the user, and the user verifies the information.

[0570] Step 8:

[0571] Users can review the information and, if necessary, ask additional questions or perform more detailed searches.

[0572] (Example 1)

[0573] 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".

[0574] Existing information provision systems lack sufficient methods for personalizing information based on user characteristics and knowledge levels, making it difficult to efficiently acquire relevant information and provide it to users in the most optimal format.

[0575] 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.

[0576] In this invention, the server includes means for receiving and analyzing user characteristic information, means for collecting relevant information from multiple information sources using natural language processing technology, and means for organizing and providing the collected information in a format most suitable to the user's characteristics. This enables users to quickly obtain optimal information tailored to their individual characteristics.

[0577] "User characteristic information" refers to information about an individual's level of knowledge, work experience, and individual needs.

[0578] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used for information gathering and text analysis.

[0579] "Information sources" refer to entities that provide relevant information, such as various databases, websites, and online media on the internet.

[0580] "Collection" refers to the activity of a server searching for and gathering necessary information based on specified conditions.

[0581] "Organization" refers to the process of structuring collected information in a way that is easy for users to understand and converting it into a format suitable for its characteristics.

[0582] "Providing information in the optimal format" means presenting information in a way that is easy to understand and use, tailored to the user's level of understanding and characteristics.

[0583] To implement this invention, the system should be constructed as follows.

[0584] First, the user needs a device, which can be a computer or a smart device. The device must have an interface to receive user input, specifically including on-screen text boxes and voice input capabilities. The process begins when the user enters a prompt, such as "I want to learn the basics of cloud computing."

[0585] Next, the server receives data sent by the user and analyzes the user's characteristics. This process requires natural language processing technology, and it is recommended to use Python's NLTK library or the machine learning library TensorFlow. Based on the individual's level of knowledge and characteristics, the server collects relevant information from the internet. This is done via data acquisition interfaces such as Google's search API.

[0586] After information gathering is complete, the server organizes the information according to its characteristics. The information is processed as text, images, and video data, and visual guides and introductory videos are provided to explain concepts in an easy-to-understand way, especially for beginners. Next, this information is sent back to the terminal, and the user obtains the information through the interface.

[0587] Through this system, users can quickly acquire the knowledge they need and efficiently utilize the information in their work. An example of a prompt message would be, "Please create an introductory document on cloud computing that is easy for new employees to understand." In this way, by establishing specific embodiments of the invention, information provision optimized for the user can be realized.

[0588] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0589] Step 1:

[0590] The user enters questions or keywords using a device. For example, the user might enter "I want to learn the basics of cloud computing." The input data is sent to the server in text format, and this becomes the initial data.

[0591] Step 2:

[0592] The server analyzes the text data received from the terminal. Natural language processing techniques are used for this analysis. Specifically, the Python NLTK library is used to tokenize the input text and extract the user's intent. This tokenized data is output as annotated keywords.

[0593] Step 3:

[0594] The server collects information from relevant databases and online sources based on the analysis results. It uses the Google Search API to gather relevant information from the internet. The input is processed keywords, and the output is a collection of primary data obtained from the information sources.

[0595] Step 4:

[0596] The server filters and organizes the collected data based on user characteristics. Information is organized according to the user's knowledge level. Beginner-friendly information is restructured to include visual guides and introductory videos. Input is information from diverse data sources, and output is customized information organized into a specific format.

[0597] Step 5:

[0598] The server sends organized information to the terminal. The output here is information presented in a format suitable for the user, which the terminal receives and displays through its user interface. The user can quickly check and understand the necessary information through the screen. Upon completion of this process, the user can efficiently acquire the necessary knowledge.

[0599] (Application Example 1)

[0600] 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".

[0601] Many digital services fail to effectively utilize user attribute information and past usage history, resulting in insufficient provision of personalized information and discounts. Such inefficient information delivery hinders the improvement of the user experience, and solutions are needed to address this issue.

[0602] 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.

[0603] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information, means for presenting the retrieved information in a format suitable for the individual, and means for providing payment methods and discount information based on the individual's transaction history. This enables users to receive the necessary information and discount services in real time in an individually optimized format.

[0604] "Acquiring personal attribute information" refers to the process of collecting basic user information and profile data from information systems.

[0605] "Searching for information based on acquired attribute information" refers to the operation of extracting data corresponding to the user's attribute information from existing databases or network information sources.

[0606] "Presenting searched information in a format tailored to the individual" refers to the process of optimizing and displaying collected information based on the user's level of understanding and preferences.

[0607] "Providing payment methods and discount information based on an individual's transaction history" refers to the process of analyzing a user's past purchase history and then presenting the most suitable payment method and benefits based on that analysis.

[0608] To realize this invention, it is first necessary to build a system for acquiring personal attribute information. The server will work in conjunction with the user's smartphone or personal computer to securely collect the user's basic information and profile data. In this process, information will be exchanged in real time using a cloud-based platform (e.g., AWS, Google Cloud).

[0609] Next, information is searched based on the acquired attribute information. The server uses natural language processing technology to quickly extract relevant information from multiple databases and information sources. The retrieved information is analyzed using machine learning algorithms such as TensorFlow and optimized to suit the user's level of understanding and preferences.

[0610] To present information in a format tailored to the individual, the server generates a user interface configuration. This user interface is developed using UI frameworks such as React Native and designed to be intuitive for users to operate. This allows users to easily access information on their smartphones or personal computers.

[0611] In addition, based on an individual's transaction history, the server provides payment methods and discount information. The server analyzes past purchase data and presents the optimal payment method and relevant coupon information. Based on this, users can maximize the benefits they receive.

[0612] As a concrete example, when a user purchases tickets for a music event, the server provides discount information for cards previously used at similar events and suggests it to the user. This allows the user to purchase tickets in a more advantageous way.

[0613] Example of a prompt:

[0614] "Based on user macro information, please suggest the best discounts and payment methods for the next music event."

[0615] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0616] Step 1:

[0617] The server retrieves user attribute information from the terminal. Input includes basic user information and profile data, and output is the storage of this data on the server. Specifically, information is collected through a secure data transfer protocol using SSL / TLS.

[0618] Step 2:

[0619] The server searches for relevant information based on the acquired attribute information. It receives user profile data as input and creates a list of relevant information as output. Data processing involves keyword extraction using natural language processing techniques and generating queries to information sources, collecting the necessary data from each source.

[0620] Step 3:

[0621] The server presents information in a format suitable for the user. Using searched information and user attribute information as input, it generates personalized information content as output. Specifically, it uses machine learning algorithms to estimate the user's level of understanding and selects the optimal presentation format for articles and reports.

[0622] Step 4:

[0623] The server provides payment methods and discount information based on the user's transaction history. It uses previous purchase history as input and generates recommended payment methods and discount information as output. Specifically, it analyzes transaction history using database queries and calculates the optimal payment option and discount using an algorithm.

[0624] Step 5:

[0625] The device displays information sent from the server. It uses personalized information received from the server as input and provides a visual display on the user interface as output. Specifically, it uses a UI framework to render content in a way that allows the user to intuitively interact with the information.

[0626] 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.

[0627] This invention is an AI system that recognizes emotions along with the user's personal attribute information and provides information based on that. When a user enters a question or keyword into a terminal, the terminal acquires that information and sends it to the server along with profile information such as the user's knowledge level and work experience. At the same time, the emotion engine analyzes the user's voice, facial expressions, input speed, etc., and generates emotion data.

[0628] The server receives user profile data and emotional data. This allows it to provide not only information tailored to the user's knowledge level, but also appropriate support based on their emotions. For example, if a user is feeling anxious or stressed, the server will present information in an easy-to-understand and simplified way, prioritizing reassuring language.

[0629] In actual operation, if the system determines that a user is feeling anxious about "learning new technology," it generates content with a gentle tone and plenty of supplementary explanations. Information is presented to the user in stages, and important points are highlighted repeatedly, employing techniques to aid understanding.

[0630] By presenting information in a way that takes the user's emotional state into consideration, learning and information absorption become more comfortable and effective, and flexible responses to user needs become possible.

[0631] The following describes the processing flow.

[0632] Step 1:

[0633] Users input what they want to learn or their questions into the device. Additionally, data on the user's voice and facial expressions are collected by an emotion engine via sensors and cameras.

[0634] Step 2:

[0635] The device sends the entered questions and keywords, the user's personal profile, and collected sentiment data to the server.

[0636] Step 3:

[0637] The server analyzes the received profile and sentiment data and generates search queries tailored to individual user needs.

[0638] Step 4:

[0639] The server uses the generated query to search for relevant information from multiple databases and sources. The results are stored in temporary memory.

[0640] Step 5:

[0641] The server filters the information it retrieves based on the user's knowledge level and emotional state. For users who are feeling anxious, it selects information that includes detailed explanations starting from the basics.

[0642] Step 6:

[0643] The server takes emotional data into account and personalizes and formats the information. It constructs information with a tone and structure that matches the emotional state and organizes it in the format that is best suited to the user.

[0644] Step 7:

[0645] The server sends the final content to the terminal. The terminal displays the information in a user interface, allowing the user to view it.

[0646] Step 8:

[0647] Users can review the presented information and repeat the process if they need further questions or to search for more details. Changes in emotions are also measured again and used in the repeating cycle.

[0648] (Example 2)

[0649] 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".

[0650] Users often experience anxiety and stress during learning and information gathering when presented with information that does not take into account their individual knowledge levels or emotional states. This can lead to difficulties in understanding the information and a decrease in learning effectiveness. Therefore, there is a need to provide more personalized information based on the user's personal attributes and emotional state.

[0651] 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.

[0652] In this invention, the server includes means for acquiring personal attribute information, means for analyzing the personal emotional state, and means for generating information in an optimal format according to the emotional state. This enables the provision of information adapted to the user's individual needs and current feelings, resulting in easy-to-understand and effective learning support.

[0653] "Personal attribute information" refers to information about a user's individual characteristics and background, such as their knowledge level, work experience, and learning history.

[0654] "Emotional state" refers to data about the emotions a user feels at a particular moment, such as anxiety, stress, and a sense of security.

[0655] "Analysis" is the process of examining collected data in detail in order to find certain laws or patterns.

[0656] "Generating in the optimal format" means creating information in a shape or structure that is easiest for the user to understand.

[0657] "Acquiring information in parallel" means collecting data from multiple sources simultaneously.

[0658] To implement this invention, the user first inputs questions or keywords using a terminal. The terminal acquires this input information and also retrieves attribute information such as the user's knowledge level and work experience from a database. Furthermore, it analyzes the user's emotional state using an emotion engine. For this analysis, it is recommended to use an image processing library for facial expression recognition and speech recognition software for speech analysis.

[0659] The device sends user input information, attribute information, and sentiment data to the server. The server receives this information and uses a generative AI model to generate information best suited to the user. For example, if information needs to be conveyed in a gentle tone, the server can send a prompt to the generative AI model such as, "Generate text that gently explains the concept of a new technology in a situation where the user is feeling anxious," thereby generating appropriate content.

[0660] The generated information is sent from the server to the terminal and presented to the user. For example, if a user feels anxious about learning a new technology, the server presents the information step-by-step, repeatedly highlighting key points to make it easier to understand. This increases the user's sense of security and improves the learning effect.

[0661] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0662] Step 1:

[0663] The user enters a question or keyword into the device.

[0664] The entered data is retrieved within the terminal and temporarily stored in a database. The terminal then prepares this input data for the next processing step.

[0665] Step 2:

[0666] The device retrieves the user's personal attribute information.

[0667] This involves referencing past user databases to retrieve personal attributes such as user knowledge levels and work experience. The obtained attribute information is then packetized along with the input data.

[0668] Step 3:

[0669] The device uses an emotion engine to analyze the user's emotional state.

[0670] A speech recognition module and image processing library are used to analyze the user's emotions from their voice and facial expressions. Voice data and camera footage are provided as input, and emotion data is generated based on this.

[0671] Step 4:

[0672] The terminal sends user input information, attribute information, and sentiment data to the server.

[0673] This involves securely and efficiently transmitting data to the server using protocols such as HTTP. This data is then used as input for the next step on the server.

[0674] Step 5:

[0675] The server receives data, and an AI model generates information.

[0676] The server analyzes the received data and sends prompt messages to an AI model. This AI model then outputs text optimized for the user's emotions and attributes, following the prompt messages.

[0677] Step 6:

[0678] The server sends the generated information to the terminal, and the terminal presents the information to the user.

[0679] At this stage, the information is processed to be displayed in a format that is easy for users to see and understand. User reactions are then analyzed again and used to improve future information provision.

[0680] (Application Example 2)

[0681] 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".

[0682] In the field of elderly care, there is a need for appropriate information provision and care that responds to the emotions of the users. However, conventional systems have made it difficult to accurately grasp the emotional state of users and provide information that responds accordingly in real time. In particular, when users are experiencing anxiety or stress, individualized support is necessary, but there has been a problem in providing effective support.

[0683] 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.

[0684] In this invention, the server includes means for acquiring personal attribute information, means for retrieving information based on the acquired attribute information and emotional data, and means for presenting the retrieved information in a format optimized according to the individual's emotional state. This enables personalized information provision and optimized care based on the user's emotional state.

[0685] "Personal attribute information" refers to basic information such as the user's age, gender, occupation, learning history, and work experience.

[0686] "Emotional data" refers to data that indicates a user's psychological state, analyzed from factors such as their voice, facial expressions, and typing speed.

[0687] "Means of retrieving information" refers to the process of efficiently searching for appropriate information from databases and other sources based on user attribute information and sentiment data.

[0688] "Means of presenting retrieved information" refers to methods and technologies for displaying retrieved information in a format that is easy for the user to understand.

[0689] "Means for analyzing voice and facial expressions" refers to technical methods that use voice recognition and facial recognition technologies to infer the user's emotional state.

[0690] "Optimization" refers to the process of adjusting the content and format of the information provided to the user in the most effective way for that user, based on user attribute information and sentiment data.

[0691] "Information acquisition in parallel" refers to a technical method of simultaneously collecting and processing information from multiple sources.

[0692] "Prioritization" refers to the process of ranking the importance of acquired information and providing the most relevant information first.

[0693] This invention relates to a system for recognizing a user's personal attribute information and emotions, and providing information based on that information. The implementation method of this system is described below.

[0694] The server first retrieves user attribute information, including age, gender, learning history, and work experience. At this stage, the server centrally manages the attribute information provided by the user's device. Next, it collects user voice and facial expression data, for example, using the device's microphone and camera, and analyzes it using an emotion engine. This analysis utilizes emotion analysis technologies such as Google Cloud's Speech-to-Text API and Emotion AI.

[0695] Based on the acquired attribute information and sentiment data, the server utilizes a generative AI model to search for information appropriate to the user's state and generate it if necessary. In particular, advanced language models such as OpenAI's GPT series are used as generative AI models. This model acquires information in parallel from multiple sources and prioritizes them according to the user's sentiment state.

[0696] The searched information is presented in a format optimized for each individual user. This means that the content and presentation of the information are adjusted according to the user's level of understanding and emotional state. For example, for anxious users, relaxing language and visually reassuring designs are used.

[0697] For example, if a user is feeling anxious about learning a new technology, the generated prompt might read: "Provide additional materials and calm-toned explanations to users who are feeling anxious about learning a new technology to help them understand better." In this way, it becomes possible to provide user-optimized information and support in caregiving settings and various other fields.

[0698] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0699] Step 1:

[0700] The device collects the user's personal attribute information. This input includes the user's age, gender, learning history, and work experience. This data is then compiled and prepared for transmission to the server.

[0701] Step 2:

[0702] The device acquires the user's voice and facial expressions. It captures data using the camera and microphone and provides it as input to the emotion analysis engine. Emotion AI is used to analyze and output emotion data from the voice and facial expressions.

[0703] Step 3:

[0704] The server receives personal attribute information and sentiment data sent from the terminal. Based on this input, it uses a generative AI model to create prompts for searching and generating appropriate information.

[0705] Step 4:

[0706] The server sends prompt text to an AI model that generates or searches for the most relevant information based on the user's sentiment. The input is the prompt text, and the output is the informational content presented to the user.

[0707] Step 5:

[0708] The device receives optimized information and presents it to the user. Information is displayed in a format that responds to the user's emotional state, using visual and auditory elements. This allows the user to receive appropriate information and gain a sense of security.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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."

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0730] The following is further disclosed regarding the embodiments described above.

[0731] (Claim 1)

[0732] Means of obtaining personal attribute information,

[0733] A means of searching for information based on acquired attribute information,

[0734] A means of presenting the searched information in a format suitable for the individual,

[0735] A system that includes this.

[0736] (Claim 2)

[0737] The system according to claim 1, which optimizes information taking into account an individual's learning history.

[0738] (Claim 3)

[0739] The system according to claim 1, which acquires information in parallel from multiple sources.

[0740] "Example 1"

[0741] (Claim 1)

[0742] A means of receiving and analyzing user characteristic information,

[0743] A means of collecting relevant information from multiple sources using natural language processing technology,

[0744] A means of organizing and providing the collected information in a format best suited to the user's characteristics,

[0745] A system that includes this.

[0746] (Claim 2)

[0747] The system according to claim 1, which provides information according to the level of knowledge.

[0748] (Claim 3)

[0749] The system according to claim 1, which efficiently acquires information through parallel processing.

[0750] "Application Example 1"

[0751] (Claim 1)

[0752] Means of obtaining personal attribute information,

[0753] A means of searching for information based on acquired attribute information,

[0754] A means of presenting the searched information in a format suitable for the individual,

[0755] A means of providing payment methods and discount information based on an individual's transaction history,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, which optimizes information taking into account an individual's learning history.

[0759] (Claim 3)

[0760] The system according to claim 1, which acquires information in parallel from multiple sources.

[0761] "Example 2 of combining an emotion engine"

[0762] (Claim 1)

[0763] Means of obtaining personal attribute information,

[0764] A means of searching for information based on acquired attribute information,

[0765] A means of analyzing an individual's emotional state,

[0766] A means of generating information in the optimal format according to emotional state,

[0767] Means of presenting generated information to individuals,

[0768] A system that includes this.

[0769] (Claim 2)

[0770] The system according to claim 1, which optimizes information taking into account an individual's learning history.

[0771] (Claim 3)

[0772] The system according to claim 1, which acquires information in parallel from multiple sources.

[0773] "Application example 2 when combining with an emotional engine"

[0774] (Claim 1)

[0775] Means of obtaining personal attribute information,

[0776] A means for searching for information based on acquired attribute information and sentiment data,

[0777] A means of presenting retrieved information in a format optimized according to an individual's emotional state,

[0778] A means for analyzing voice and facial expressions to generate emotional data,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, which optimizes information taking into account an individual's learning history and emotional state.

[0782] (Claim 3)

[0783] The system according to claim 1, which acquires information in parallel from multiple sources and prioritizes the information based on emotional state. [Explanation of symbols]

[0784] 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 of obtaining personal attribute information, A means of searching for information based on acquired attribute information, A means of presenting the searched information in a format suitable for the individual, A system that includes this.

2. The system according to claim 1, which optimizes information taking into account an individual's learning history.

3. The system according to claim 1, which acquires information in parallel from multiple information sources.

Images