system

Abstract

Provide a system. 【Solution means】 Means for receiving an input from an information processing device, Means for analyzing the received input using natural language processing technology, Means for sending an information acquisition request to a related data storage medium or an external service based on the analysis result, Means for personalizing the information obtained using the user's personal information data, Means for presenting the personalized information to the user, Means for obtaining related information from an external service based on location information, Means for organizing the obtained information based on the user's interests, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult for residents and visitors in a smart city to quickly and efficiently obtain necessary information. For this reason, users will spend a lot of time in the process of searching for information and may feel stressed in daily life. In addition, there is a lack of customized information provision corresponding to individual needs of users, and an improvement in the usage experience is required.

Means for Solving the Problems

[0005] This invention receives user input through an information processing device and analyzes the input using natural language processing technology. Based on the analysis results, it quickly obtains the information the user requests by sending an information retrieval request to a relevant database or external service. The system also includes a configuration that utilizes the user's profile data to personalize and present the retrieved information according to the user's needs. As a result, users can easily obtain optimal information based on their individual preferences and past behavioral patterns, improving the quality of life in smart cities.

[0006] An "information processing device" is a hardware and software system that receives input from a user and performs specified computational processing.

[0007] "Natural language processing" is a technology that enables computers to understand and analyze human language, and it is a technology that converts text data into a machine-readable format.

[0008] A "database" is a systematically structured collection of data, a system that enables efficient information retrieval and updating.

[0009] An "external service" is a software application that enables access to functions and information resources managed by a provider outside the system.

[0010] An "information retrieval request" is a request issued to a database or external service to obtain specific information.

[0011] "Profile data" refers to a dataset containing attribute information and behavioral history about a user, and represents information that shows the characteristics of an individual user.

[0012] Personalization is the process of adjusting and individualizing information and services according to the user's needs and preferences. [Brief explanation of the drawing]

[0013] [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]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled 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.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system of this invention aims to provide residents and visitors within a smart city with information that meets their needs quickly and efficiently. This system consists of user terminals, servers, a database, and external services.

[0035] First, the user uses the system to input questions or requests into the terminal in natural language. For example, they might make a request such as, "Please tell me today's weather." The terminal receives this input and sends it to the server as digital data.

[0036] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to relevant databases or external services to obtain the necessary information. For example, it might send a request to an external API that provides weather information to obtain weather data for the current location.

[0037] The acquired data is integrated with user profile data on the server. This profile data records the user's past behavior history and preferences, and is used to personalize the information. For example, if a user prefers information about a particular event, the system will adjust to prioritize providing that information.

[0038] Furthermore, the server generates specific answers for the user based on personalized information and sends them to the device. The device then displays the received information to the user in a visually easy-to-understand format.

[0039] For example, if a user wants to find a new restaurant, the system provides a list of restaurant options. Furthermore, by personalizing details such as cuisine type, price range, location, and travel time, it expands the user's choices and helps them make the best selection for their purpose.

[0040] Thus, the system of the present invention provides information tailored to the individual needs of users, thereby improving the convenience of life in smart cities.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users input questions and requests using natural language via their devices. For example, they might type, "Tell me about nearby cafes."

[0044] Step 2:

[0045] The terminal converts user input into digital data and sends this data to the server.

[0046] Step 3:

[0047] The server passes the received data to a natural language processing engine, which analyzes the input to extract the user's intent and entities. At this point, it recognizes that the user needs information about cafes.

[0048] Step 4:

[0049] Based on the analysis results, the server sends information retrieval requests to the relevant database or external API. In this case, it sends a query to an external service to retrieve cafe data based on geographical information.

[0050] Step 5:

[0051] The server receives cafe information obtained from an external service and references user profiles in its own database. This allows it to personalize information based on the user's past behavior and preferences.

[0052] Step 6:

[0053] Based on the data it receives, the server generates information tailored to the user's needs. For example, it might summarize specific recommendations such as, "Cafes A and B, located a 5-minute walk apart, are now open."

[0054] Step 7:

[0055] The server compiles the final response and sends it to the terminal.

[0056] Step 8:

[0057] The device presents received information to the user in an easy-to-understand manner. This allows the user to intuitively understand the information and make decisions about their actions.

[0058] (Example 1)

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

[0060] In today's information and communication environment, users have access to vast amounts of information, but they face the challenge of obtaining accurate and personalized information from that vast amount. Furthermore, there is a demand for providing information that matches users' needs and preferences, but conventional systems are not adequately able to do so. To solve this problem, a method is needed that appropriately understands user intent and quickly and efficiently acquires and provides information based on that intent.

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

[0062] In this invention, the server includes means for receiving input from an information processing terminal, means for analyzing the received input using natural language processing technology to identify the user's intent, and means for sending an information retrieval request to a relevant recording medium or external information source based on the analysis results. This enables a rapid response to user requests and the provision of personalized information tailored to their needs.

[0063] An "information processing terminal" is an electronic device that receives input from a user and transmits it to a server as digital data.

[0064] "Natural language processing technology" is a technology that enables computers to understand and analyze natural language used by humans.

[0065] A "generative technology model" is an algorithm or system that generates content tailored to the user's profile based on acquired information, thereby personalizing the information.

[0066] A "recording medium" refers to a recording device or storage system that stores data and allows it to be accessed as needed.

[0067] "External information sources" refer to sources of information that are referenced to obtain necessary data, such as data provision services or APIs that exist outside the system.

[0068] "User profile data" refers to individual data that records a user's behavioral history and preferences, and is used to provide personalized information.

[0069] This invention is an information provision system for smart cities, aiming to provide information quickly and accurately tailored to the individual needs of users. The embodiments thereof will be described below.

[0070] Users input questions and requests in natural language via their information processing terminals. These terminals support text and voice input and feature a user-friendly interface. For example, a user might ask, "Can you tell me about a nearby cafe?"

[0071] The terminal converts user input into a digital format and sends it to the server. This terminal uses wireless communication technology to transmit data quickly, and security is also considered.

[0072] The server analyzes the received digital data using natural language processing techniques to identify the user's intent. Open-source natural language processing libraries and commercial natural language processing engines can be used here. Based on the analysis results, the server sends requests for information retrieval to relevant storage media or external information sources. For example, it might use an external geographic information API to obtain information about nearby cafes.

[0073] The acquired data is integrated with user profile data using a generative technology model and then personalized. This profile data includes past access history and user preferences. The server uses this information to generate relevant information and suggestions.

[0074] Personalized information is sent to the device and presented to the user in a visually easy-to-understand format. For example, the location of a cafe might be displayed on a map application, and route guidance could be provided.

[0075] For example, if a user wants to search for a restaurant based on specific criteria, they can input "Tell me the nearest restaurant that serves vegetarian food." Another example of a prompt message is, "I want to find a new restaurant. Please generate recommendations based on my current location, preferred cuisine, and budget." This system improves user convenience and supports efficient information retrieval.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user inputs the desired information in natural language using an information processing terminal. For example, they might input a question like, "Where's a good pizza place?" This input is passed to the terminal as data and then directly to the next processing step.

[0079] Step 2:

[0080] The terminal sends the received natural language input as text data to the server. In the case of voice input, speech recognition technology is used to convert it into text. The converted text data is then sent to the server.

[0081] Step 3:

[0082] The server analyzes the transmitted text data using natural language processing (NLP) techniques. A generative AI model is used for this analysis. This process grasps the intent of the input query and extracts keywords and phrases necessary to obtain specific information. As a result, data is obtained that clarifies the user's request.

[0083] Step 4:

[0084] Based on the analysis results, the server sends an information retrieval request to an appropriate storage medium or external information source. For example, it might use a geographic information API to query information about pizza restaurants near the user's current location. The output here is a dataset of the queried pizza restaurant information.

[0085] Step 5:

[0086] The server generates personalized information using a generative technology model based on the acquired information. In this process, it considers the user's profile data—that is, their preferences and past behavioral history—to derive the most relevant information for the user. The output of this process is customized information presented to the user.

[0087] Step 6:

[0088] On the device, personalized information sent from the server is received and displayed to the user in a visually easy-to-understand format. For example, a map application might display the location and rating of the nearest pizza restaurant and provide navigation. In this final step, the user can efficiently obtain the desired information.

[0089] (Application Example 1)

[0090] 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."

[0091] In smart cities, residents and visitors need diverse information, but obtaining that information quickly and accurately is challenging. In particular, visitors need real-time personalized information based on their location and personal interests when sightseeing. Current systems fail to adequately meet these requirements.

[0092] 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.

[0093] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information acquisition requests to relevant data storage media or external services based on the analysis results, means for personalizing the acquired information using the user's personal information data, means for presenting the personalized information to the user, means for acquiring relevant information from external services based on location information, and means for organizing the acquired information based on the user's interests. This improves the efficiency and personalization of information provision in smart cities and makes it possible to quickly provide users with the most relevant information.

[0094] An "information processing device" is a device that receives input from a user and processes and analyzes that data.

[0095] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0096] A "data storage medium" is a device or system used to record and store information over a long period of time.

[0097] "External services" are resources that exist outside the system and provide data or functionality online or via a network.

[0098] "Personal information data" refers to data that includes information related to a specific user, such as details of their past behavior and preferences.

[0099] "Personalizing" means customizing information to suit the individual user's preferences and needs.

[0100] "Location information" refers to information about a user's current location or a specific point, and is data used to determine a geographical location.

[0101] "User interests" refer to the themes and topics that individual users are particularly interested in.

[0102] The system for carrying out this invention comprises a terminal directly operated by the user, a server for data processing, a database for storing related information, and a service that serves as an external information source. The user inputs their request into the terminal in natural language. The input information is transmitted to the server via the terminal. The server analyzes this input using natural language processing technology to clarify the user's intent. Based on the results of the analysis, it sends requests for information retrieval to the relevant database and external services.

[0103] The server retrieves the necessary information, integrates it with the user's personal data, and personalizes the information. The personalized information is organized in a way that is most relevant to the user. Furthermore, the server retrieves relevant information from the surrounding area based on the user's location and displays it according to the user's interests.

[0104] As a concrete example, when a user visits a historical tourist site using their smartphone, they can obtain detailed historical information and recommended spots in real time. Based on the user's location information and past interest data, the server selects and provides appropriate information to the user.

[0105] An example of a prompt is, "I want to create a tourist information guide app for smart city visitors. Please give me design ideas for an app that provides real-time historical information and recommended spots based on the visitor's current location." By inputting such prompts into the generating AI model, it is possible to obtain design references for the system.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user uses a terminal to input a question or request in natural language. This input information is recorded on the terminal as text data and sent directly to the server. The terminal's role is to convert the user's input into a digital format and send it to the server.

[0109] Step 2:

[0110] The server receives text data sent from the terminal. Next, the server uses natural language processing technology to analyze the input and clarify the user's intent. The text data, as input, undergoes grammatical and semantic analysis, and the user's question and information request are output as explicit target data.

[0111] Step 3:

[0112] Based on the analysis results, the server sends information retrieval requests to relevant databases or external services. For example, based on the topics identified in the analysis results, it calls the appropriate external API to retrieve data. The target data, as input, is converted into the format of an API request, and the appropriate information is collected from the external source.

[0113] Step 4:

[0114] The server integrates the acquired information with the user's personal information data. During this process, past behavioral history and preferences are taken into consideration to personalize the information in a way that is beneficial to the user. The input external information data and individual profile data are processed together and output as customized information.

[0115] Step 5:

[0116] The server uses the user's location information to acquire further relevant information and organize it according to the user's interests. Location-dependent information is retrieved from external services, filtered according to interests, and presented to the user in the most optimal format. Location information as input is crucial as the foundation for the filtered output information.

[0117] Step 6:

[0118] The server sends personalized and organized information to the terminal. The terminal displays this information to the user in a visually understandable format. The terminal's role is to receive the formatted output information and present it appropriately in the user interface.

[0119] 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.

[0120] The present invention aims to recognize a user's emotions in real time and provide personalized information based on those emotions. This system consists of a user's terminal, a server, an emotion engine, a database, and external services.

[0121] First, the user inputs questions or requests for information into the system using natural language via a terminal. For example, they might request, "Tell me today's recommended news." The terminal receives the input information, converts it into a digital format, and sends it to the server.

[0122] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to appropriate databases or external APIs to collect relevant information.

[0123] The acquired information is integrated with the user profile, and the emotion engine simultaneously evaluates the user's emotional state. The emotion engine can read emotions from the user's tone of voice, the content of the input text, and even the user's eye movements and facial expressions. For example, if the user is feeling stressed, the information is personalized to provide more relaxing content.

[0124] The server considers data from the emotion engine and the user profile to generate an optimized information set. Because the information set is adjusted to reflect the user's current emotional state, users can receive information that is more comfortable and relevant to their purpose.

[0125] For example, if the system detects that a user is feeling tired, it will suggest light reading material or relaxation-related content. Conversely, if the user is focused, it can provide detailed, technical articles.

[0126] Ultimately, the server sends this individually tailored information to the terminal, which then displays it to the user in a visually appealing and easy-to-understand format. This allows users to receive information that best suits their emotional state, significantly improving the information experience in smart cities.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users input questions and information requests in natural language into their devices. For example, they might make a request like, "Tell me the latest news."

[0130] Step 2:

[0131] The terminal prepares to digitize user input as voice or text data and send it to the server.

[0132] Step 3:

[0133] The server passes the received digital data to a natural language processing engine, which analyzes the input and extracts the user's intent. It then determines that news information is needed.

[0134] Step 4:

[0135] The emotion engine analyzes user input and voice data, evaluating the emotional state from voice tone and text. For example, it can determine whether the user is in a happy mood or an anxious state.

[0136] Step 5:

[0137] Based on the analysis results, the server sends information retrieval requests to relevant databases or external news services. It collects data on news headlines and content.

[0138] Step 6:

[0139] The server integrates the acquired information with the user profile and sentiment engine results to personalize it. If the user is feeling stressed, the information is adjusted to prioritize relaxing news.

[0140] Step 7:

[0141] The server generates news sets optimized for the user's emotional state and organizes them for presentation.

[0142] Step 8:

[0143] The server sends the final set of information to the terminal.

[0144] Step 9:

[0145] The device presents the received information to the user in an easy-to-understand manner. This allows the user to intuitively obtain information that is relevant to their emotional state.

[0146] (Example 2)

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

[0148] In modern information processing systems, personalizing information based on user emotions is not sufficiently implemented, and providing information based on real-time emotion assessment is particularly difficult. Conventional methods fail to provide information that deeply understands the user's intentions and emotions, resulting in a problem of unoptimized user experience.

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

[0150] In this invention, the server includes means for converting natural language input acquired from an information processing terminal into a digital format, means for analyzing the input using speech recognition technology and natural language processing technology to clarify the user's intent, and means for personalizing the acquired information based on the user's profile and emotional state. This makes it possible to provide appropriate information in real time according to the user's emotional state.

[0151] An "information processing terminal" is a device that receives input from a user and converts it into digital data for processing.

[0152] "Natural language input" refers to instructions or questions in everyday language that a user provides to a system, either by voice or text.

[0153] A "digital format" is a data format that converts analog data into a structure that can be processed by a computer.

[0154] "Speech recognition technology" is a technology that analyzes speech data and converts it into text.

[0155] "Natural language processing technology" refers to the technology used to understand, interpret, and generate human language using computers.

[0156] "User intent" refers to the specific results or objectives that a user expects from the system.

[0157] "Emotional state" refers to the user's psychological state, evaluated based on various indicators that show the user's emotions.

[0158] Personalization is the process of providing information and services in a way that is optimized for a specific user.

[0159] The embodiment of the invention is a system that recognizes the user's intentions and emotional state in real time in response to their information requests and provides optimized information. This system mainly consists of an information processing terminal, a server, an emotion analysis engine, a database, and an external information source.

[0160] The user provides input in natural language to an information processing terminal. In the case of voice input, the terminal converts the data into digital format using speech recognition technology (for example, general speech conversion software) and transfers it to the server.

[0161] The server uses natural language processing techniques to analyze the received digital data. For example, it employs text analysis software and natural language processing libraries. It also utilizes an emotion analysis engine to evaluate the user's emotional state based on their voice tone and input text. This evaluation is performed by combining image recognition techniques for facial expression analysis with voice analysis techniques.

[0162] The server further integrates the acquired information with the user's profile information and personalizes the information based on their emotional state. This personalization uses generative AI technology to summarize and organize the information so that it is presented to the user in the most relevant way.

[0163] For example, a possible prompt might be "Tell me some movies to help me relax today," and the system would respond to such a user request by suggesting comedies or animated films, providing information that is suitable for the user.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] Users request information from the information processing terminal via voice or text. For example, they might type, "Tell me some recommended news." This request is converted into digital data by the terminal and sent to the server. In the case of voice input, speech recognition technology is used to convert it into text. The input is voice / text data, and the output is digital text data.

[0167] Step 2:

[0168] The server analyzes the received digital text data using natural language processing techniques to identify the user's intent. This analysis utilizes morphological analysis and intent classification algorithms. The input is digital text data, and the output is a data structure that indicates the user's intent. Specifically, it performs sentence structure analysis and keyword extraction.

[0169] Step 3:

[0170] Based on the analysis results, the server sends requests to appropriate databases and external information sources to retrieve information. In this process, it collects relevant data via APIs. The input is a data structure representing the user's intent, and the output is a dataset of the retrieved information. The specific operation involves API calls.

[0171] Step 4:

[0172] The server performs sentiment analysis so that the sentiment analysis engine can evaluate the user's emotional state. This analysis uses the content of the input text and voice samples. The input is voice tone and text data, and the output is data indicating the user's emotional state. Specific techniques include voice tone analysis and text sentiment analysis.

[0173] Step 5:

[0174] The server personalizes information based on acquired data, the user's profile, and their emotional state. In this process, a generative AI model is used to organize and summarize the data, creating an optimized information set. The input consists of acquired data sets and emotional state data, while the output is a personalized information set.

[0175] Step 6:

[0176] Ultimately, the server sends personalized information to the device. The device then displays the received information to the user in a visually easy-to-understand format. Specific examples of this could include infographics or list-based displays. The input is a personalized set of information, and the output is integrated information presented to the user.

[0177] (Application Example 2)

[0178] 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".

[0179] In today's information society, users are provided with a vast amount of information, but this information often does not match their current emotions or psychological state. This can lead to problems such as users experiencing stress due to information overload or being unable to obtain necessary information in a timely manner. In particular, the lack of optimization of information delivery based on emotions makes it difficult to provide effective information that is relevant to the situation.

[0180] 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.

[0181] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information retrieval requests to relevant databases or external services based on the analysis results, means for personalizing the acquired information using the user's profile data and emotional state, and means for dynamically adjusting and presenting the personalized information according to the user's emotional state. This makes it possible to provide information optimized according to the user's emotions in real time and improve the information experience.

[0182] An "information processing device" is a device used for inputting, processing, and outputting information. It is a device that receives requests and data from users, analyzes their content, and provides results.

[0183] "Natural language processing technology" is a technology that enables computers to understand and analyze the language that humans use on a daily basis, and it is a technology that has the ability to process text and speech and interpret their meaning.

[0184] "Emotional state" refers to information that indicates the user's current psychological and physiological state, and involves detecting and classifying specific emotions (e.g., happiness, anger, stress).

[0185] Personalization is a method of tailoring and providing information and services to individual users, optimizing content based on the user's preferences and emotions.

[0186] A "database" is a system for systematically collecting and storing information, and is a structured record for efficiently searching, updating, and managing that information.

[0187] "External services" refer to external information sources or function providers on which a system depends, enabling it to obtain or provide information through a network.

[0188] The embodiments for carrying out this invention will now be described. In one embodiment of the invention, a system is constructed as follows, and by executing a program, information is personalized and provided based on the user's emotional state.

[0189] The server receives input from the information processing device and analyzes the input data using natural language processing (NLP) technology. Specifically, it uses the Google® Cloud Natural Language API to effectively analyze the input natural language data and clarify the user's intentions and requests. In addition, the IBM Watson® Tone Analyzer is used as the emotion engine to analyze the user's emotional state. This allows the server to analyze the user's emotional state, such as whether they are "feeling stressed," and use that information in the next step.

[0190] The server then integrates the analyzed data with the user profile and retrieves relevant information using the Google Maps API and other event information APIs. By retrieving and integrating information that resonates with the user's emotions, it generates an optimal set of information for the user. This personalized information is then sent to the user's device.

[0191] On the user's device, the acquired information is displayed in an easy-to-read format. The device operates on platforms such as iOS and Android® and provides a user interface that enhances the user experience. Users can use this system to receive information optimized to their current emotions and needs.

[0192] A concrete example is a feature where, when a user enters a request such as, "I'm tired and would like to know where I can relax. Could you recommend some good places nearby?", the system provides information on nearby parks and relaxation spots. This makes it easier for users to access optimal environments and supports their choices in daily life.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user uses a terminal to input information requests in natural language. This input includes text prompts such as "I'm tired and would like to know where I can relax." The terminal converts this input into digital data and sends it to the server.

[0196] Step 2:

[0197] The server analyzes the received digital data using natural language processing (NLP) techniques. During this process, the Google Cloud Natural Language API is used to clarify the user's intent and requests. Based on the analysis of the input data's meaning, the types and elements of information the user is seeking are extracted.

[0198] Step 3:

[0199] Next, the server analyzes the user's emotional state. It uses IBM Watson Tone Analyzer to analyze the data and identify the user's emotional state (e.g., stress, reassurance). The input is text analyzed using NLP, and the output is the user's emotional state.

[0200] Step 4:

[0201] Based on the analyzed user requests and emotional state, the server sends information retrieval requests to databases and external services. It utilizes the Google Maps API and event information API to obtain information on relaxing places and events that match the user's requests. In this process, relevant information is collected by querying external resources based on user input.

[0202] Step 5:

[0203] The server personalizes the acquired information based on the user's profile data and emotional state. It processes the data to match the information the user currently desires, resulting in an optimized information set. This enables the provision of information tailored to the user's emotional state.

[0204] Step 6:

[0205] Ultimately, the server sends personalized information to the device. The device receives this information and displays it in a format that is easy for the user to see and understand. As a result of this step, the user can obtain the information that best suits their needs.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] The system of this invention aims to provide residents and visitors within a smart city with information that meets their needs quickly and efficiently. This system consists of user terminals, servers, a database, and external services.

[0223] First, the user uses the system to input questions or requests into the terminal in natural language. For example, they might make a request such as, "Please tell me today's weather." The terminal receives this input and sends it to the server as digital data.

[0224] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to relevant databases or external services to obtain the necessary information. For example, it might send a request to an external API that provides weather information to obtain weather data for the current location.

[0225] The acquired data is integrated with user profile data on the server. This profile data records the user's past behavior history and preferences, and is used to personalize the information. For example, if a user prefers information about a particular event, the system will adjust to prioritize providing that information.

[0226] Furthermore, the server generates specific answers for the user based on personalized information and sends them to the device. The device then displays the received information to the user in a visually easy-to-understand format.

[0227] For example, if a user wants to find a new restaurant, the system provides a list of restaurant options. Furthermore, by personalizing details such as cuisine type, price range, location, and travel time, it expands the user's choices and helps them make the best selection for their purpose.

[0228] Thus, the system of the present invention provides information tailored to the individual needs of users, thereby improving the convenience of life in smart cities.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] Users input questions and requests using natural language via their devices. For example, they might type, "Tell me about nearby cafes."

[0232] Step 2:

[0233] The terminal converts user input into digital data and sends this data to the server.

[0234] Step 3:

[0235] The server passes the received data to a natural language processing engine, which analyzes the input to extract the user's intent and entities. At this point, it recognizes that the user needs information about cafes.

[0236] Step 4:

[0237] Based on the analysis results, the server sends information retrieval requests to the relevant database or external API. In this case, it sends a query to an external service to retrieve cafe data based on geographical information.

[0238] Step 5:

[0239] The server receives cafe information obtained from an external service and references user profiles in its own database. This allows it to personalize information based on the user's past behavior and preferences.

[0240] Step 6:

[0241] Based on the data it receives, the server generates information tailored to the user's needs. For example, it might summarize specific recommendations such as, "Cafes A and B, located a 5-minute walk apart, are now open."

[0242] Step 7:

[0243] The server compiles the final response and sends it to the terminal.

[0244] Step 8:

[0245] The device presents received information to the user in an easy-to-understand manner. This allows the user to intuitively understand the information and make decisions about their actions.

[0246] (Example 1)

[0247] 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."

[0248] In today's information and communication environment, users have access to vast amounts of information, but they face the challenge of obtaining accurate and personalized information from that vast amount. Furthermore, there is a demand for providing information that matches users' needs and preferences, but conventional systems are not adequately able to do so. To solve this problem, a method is needed that appropriately understands user intent and quickly and efficiently acquires and provides information based on that intent.

[0249] 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.

[0250] In this invention, the server includes means for receiving input from an information processing terminal, means for analyzing the received input using natural language processing technology to identify the user's intent, and means for sending an information retrieval request to a relevant recording medium or external information source based on the analysis results. This enables a rapid response to user requests and the provision of personalized information tailored to their needs.

[0251] An "information processing terminal" is an electronic device that receives input from a user and transmits it to a server as digital data.

[0252] "Natural language processing technology" is a technology that enables computers to understand and analyze natural language used by humans.

[0253] A "generative technology model" is an algorithm or system that generates content tailored to the user's profile based on acquired information, thereby personalizing the information.

[0254] A "recording medium" refers to a recording device or storage system that stores data and allows it to be accessed as needed.

[0255] "External information sources" refer to sources of information that are referenced to obtain necessary data, such as data provision services or APIs that exist outside the system.

[0256] "User profile data" refers to individual data that records a user's behavioral history and preferences, and is used to provide personalized information.

[0257] This invention is an information provision system for smart cities, aiming to provide information quickly and accurately tailored to the individual needs of users. The embodiments thereof will be described below.

[0258] Users input questions and requests in natural language via their information processing terminals. These terminals support text and voice input and feature a user-friendly interface. For example, a user might ask, "Can you tell me about a nearby cafe?"

[0259] The terminal converts user input into a digital format and sends it to the server. This terminal uses wireless communication technology to transmit data quickly, and security is also considered.

[0260] The server analyzes the received digital data using natural language processing techniques to identify the user's intent. Open-source natural language processing libraries and commercial natural language processing engines can be used here. Based on the analysis results, the server sends requests for information retrieval to relevant storage media or external information sources. For example, it might use an external geographic information API to obtain information about nearby cafes.

[0261] The acquired data is integrated with user profile data using a generative technology model and then personalized. This profile data includes past access history and user preferences. The server uses this information to generate relevant information and suggestions.

[0262] Personalized information is sent to the device and presented to the user in a visually easy-to-understand format. For example, the location of a cafe might be displayed on a map application, and route guidance could be provided.

[0263] For example, if a user wants to search for a restaurant based on specific criteria, they can input "Tell me the nearest restaurant that serves vegetarian food." Another example of a prompt message is, "I want to find a new restaurant. Please generate recommendations based on my current location, preferred cuisine, and budget." This system improves user convenience and supports efficient information retrieval.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user inputs the desired information in natural language using an information processing terminal. For example, they might input a question like, "Where's a good pizza place?" This input is passed to the terminal as data and then directly to the next processing step.

[0267] Step 2:

[0268] The terminal sends the received natural language input as text data to the server. In the case of voice input, speech recognition technology is used to convert it into text. The converted text data is then sent to the server.

[0269] Step 3:

[0270] The server analyzes the transmitted text data using natural language processing (NLP) techniques. A generative AI model is used for this analysis. This process grasps the intent of the input query and extracts keywords and phrases necessary to obtain specific information. As a result, data is obtained that clarifies the user's request.

[0271] Step 4:

[0272] Based on the analysis results, the server sends an information retrieval request to an appropriate storage medium or external information source. For example, it might use a geographic information API to query information about pizza restaurants near the user's current location. The output here is a dataset of the queried pizza restaurant information.

[0273] Step 5:

[0274] The server generates personalized information using a generative technology model based on the acquired information. In this process, it considers the user's profile data—that is, their preferences and past behavioral history—to derive the most relevant information for the user. The output of this process is customized information presented to the user.

[0275] Step 6:

[0276] On the device, personalized information sent from the server is received and displayed to the user in a visually easy-to-understand format. For example, a map application might display the location and rating of the nearest pizza restaurant and provide navigation. In this final step, the user can efficiently obtain the desired information.

[0277] (Application Example 1)

[0278] 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."

[0279] In a smart city, residents and visitors need various types of information, but there is a problem that it is difficult to quickly and accurately obtain information that meets their needs. In particular, when visitors are sightseeing, there is a demand to provide personalized information in real time based on location information and personal interests. The current system cannot fully meet these requirements.

[0280] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for receiving an input from an information processing apparatus, means for analyzing the received input using natural language processing technology, means for sending an information acquisition request to a related data storage medium or an external service based on the analysis result, means for personalizing the information obtained using the user's personal information data, means for presenting the personalized information to the user, means for obtaining related information from an external service based on location information, and means for organizing the obtained information based on the user's interests. As a result, the efficiency and personalization of information provision in a smart city are improved, and it becomes possible to quickly provide the most optimal information for the user.

[0282] An "information processing apparatus" is a device for receiving an input from a user and performing data processing and analysis.

[0283] "Natural language processing technology" is a technology for a computer to understand and analyze human language.

[0284] A "data storage medium" is a device or system for recording and storing information over a long period of time.

[0285] An "external service" is an online or network-based resource that exists outside the system and provides data and functions.

[0286] "Personal information data" refers to data that includes information related to a specific user and refers to details such as past behaviors and preferences.

[0287] "To personalize" means to customize information according to the preferences and needs of individual users.

[0288] "Location information" refers to information related to the user's current location or a specific location and is data for identifying a geographical location.

[0289] "User interests" refer to themes and content that individual users are particularly interested in.

[0290] The system for implementing this invention includes a terminal directly operated by the user, a server for data processing, a database for storing related information, and a service serving as an external information source. The user inputs their requests to the terminal in natural language. The input information is transmitted to the server via the terminal. The server analyzes this input using natural language processing technology to clarify the user's intention. Based on the results of the analysis, a request for information acquisition is sent to the relevant database or external service.

[0291] When the server obtains the necessary information, it integrates the information with the user's personal information data and personalizes the information. The personalized information is organized in the most relevant form for the user. The server further obtains related information in the vicinity based on the user's location information and displays the information according to the user's interests.

[0292] As a specific example, when a user visits a historical tourist destination using a smartphone, detailed historical information and recommended spots can be obtained in real time on the spot. It is a mechanism in which the server selects appropriate information based on the user's location information and past interest data and provides it to the user.

[0293] An example of a prompt is, "I want to create a tourist information guide app for smart city visitors. Please give me design ideas for an app that provides real-time historical information and recommended spots based on the visitor's current location." By inputting such prompts into the generating AI model, it is possible to obtain design references for the system.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user uses a terminal to input a question or request in natural language. This input information is recorded on the terminal as text data and sent directly to the server. The terminal's role is to convert the user's input into a digital format and send it to the server.

[0297] Step 2:

[0298] The server receives text data sent from the terminal. Next, the server uses natural language processing technology to analyze the input and clarify the user's intent. The text data, as input, undergoes grammatical and semantic analysis, and the user's question and information request are output as explicit target data.

[0299] Step 3:

[0300] Based on the analysis results, the server sends information retrieval requests to relevant databases or external services. For example, based on the topics identified in the analysis results, it calls the appropriate external API to retrieve data. The target data, as input, is converted into the format of an API request, and the appropriate information is collected from the external source.

[0301] Step 4:

[0302] The server integrates the information it has acquired with the user's personal information data. At this time, taking into account the user's past behavior history and preferences, the information is personalized in a form that is beneficial to the user. The input external information data and individual profile data are processed in a batch and output as customized information.

[0303] Step 5:

[0304] Based on the user's location information, the server further acquires relevant information and organizes it according to the user's interests. Information dependent on location is obtained from external services, filtered according to interests, and provided to the user in an optimal form. The location information as input is important as the basis for the output filtered information.

[0305] Step 6:

[0306] The server transmits the personalized and organized information to the terminal. The terminal displays this information to the user in a visually easy-to-understand form. The role of the terminal is to receive the formatted output information and appropriately present it through the user interface.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0308] The system of the present invention aims to recognize the user's emotions in real time and provide personalized information based on those emotions. This system is composed of the user's terminal, server, emotion engine, database, and external services.

[0309] First, the user inputs questions or information requests in natural language to the system via the terminal. For example, a request such as "Tell me the recommended news for today" is made. The terminal receives the input information, converts it into a digital format, and transmits it to the server.

[0310] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to appropriate databases or external APIs to collect relevant information.

[0311] The acquired information is integrated with the user profile, and the emotion engine simultaneously evaluates the user's emotional state. The emotion engine can read emotions from the user's tone of voice, the content of the input text, and even the user's eye movements and facial expressions. For example, if the user is feeling stressed, the information is personalized to provide more relaxing content.

[0312] The server considers data from the emotion engine and the user profile to generate an optimized information set. Because the information set is adjusted to reflect the user's current emotional state, users can receive information that is more comfortable and relevant to their purpose.

[0313] For example, if the system detects that a user is feeling tired, it will suggest light reading material or relaxation-related content. Conversely, if the user is focused, it can provide detailed, technical articles.

[0314] Ultimately, the server sends this individually tailored information to the terminal, which then displays it to the user in a visually appealing and easy-to-understand format. This allows users to receive information that best suits their emotional state, significantly improving the information experience in smart cities.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] Users input questions and information requests in natural language into their devices. For example, they might make a request like, "Tell me the latest news."

[0318] Step 2:

[0319] The terminal prepares to digitize user input as voice or text data and send it to the server.

[0320] Step 3:

[0321] The server passes the received digital data to a natural language processing engine, which analyzes the input and extracts the user's intent. It then determines that news information is needed.

[0322] Step 4:

[0323] The emotion engine analyzes user input and voice data, evaluating the emotional state from voice tone and text. For example, it can determine whether the user is in a happy mood or an anxious state.

[0324] Step 5:

[0325] Based on the analysis results, the server sends information retrieval requests to relevant databases or external news services. It collects data on news headlines and content.

[0326] Step 6:

[0327] The server integrates the acquired information with the user profile and sentiment engine results to personalize it. If the user is feeling stressed, the information is adjusted to prioritize relaxing news.

[0328] Step 7:

[0329] The server generates news sets optimized for the user's emotional state and organizes them for presentation.

[0330] Step 8:

[0331] The server sends the final set of information to the terminal.

[0332] Step 9:

[0333] The device presents the received information to the user in an easy-to-understand manner. This allows the user to intuitively obtain information that is relevant to their emotional state.

[0334] (Example 2)

[0335] 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".

[0336] In modern information processing systems, personalizing information based on user emotions is not sufficiently implemented, and providing information based on real-time emotion assessment is particularly difficult. Conventional methods fail to provide information that deeply understands the user's intentions and emotions, resulting in a problem of unoptimized user experience.

[0337] 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.

[0338] In this invention, the server includes means for converting natural language input acquired from an information processing terminal into a digital format, means for analyzing the input using speech recognition technology and natural language processing technology to clarify the user's intent, and means for personalizing the acquired information based on the user's profile and emotional state. This makes it possible to provide appropriate information in real time according to the user's emotional state.

[0339] An "information processing terminal" is a device that receives input from a user and converts it into digital data for processing.

[0340] "Natural language input" refers to instructions or questions in everyday language that a user provides to a system, either by voice or text.

[0341] A "digital format" is a data format that converts analog data into a structure that can be processed by a computer.

[0342] "Speech recognition technology" is a technology that analyzes speech data and converts it into text.

[0343] "Natural language processing technology" refers to the technology used to understand, interpret, and generate human language using computers.

[0344] "User intent" refers to the specific results or objectives that a user expects from the system.

[0345] "Emotional state" refers to the user's psychological state, evaluated based on various indicators that show the user's emotions.

[0346] Personalization is the process of providing information and services in a way that is optimized for a specific user.

[0347] The embodiment of the invention is a system that recognizes the user's intentions and emotional state in real time in response to their information requests and provides optimized information. This system mainly consists of an information processing terminal, a server, an emotion analysis engine, a database, and an external information source.

[0348] The user provides input in natural language to an information processing terminal. In the case of voice input, the terminal converts the data into digital format using speech recognition technology (for example, general speech conversion software) and transfers it to the server.

[0349] The server uses natural language processing techniques to analyze the received digital data. For example, it employs text analysis software and natural language processing libraries. It also utilizes an emotion analysis engine to evaluate the user's emotional state based on their voice tone and input text. This evaluation is performed by combining image recognition techniques for facial expression analysis with voice analysis techniques.

[0350] The server further integrates the acquired information with the user's profile information and personalizes the information based on their emotional state. This personalization uses generative AI technology to summarize and organize the information so that it is presented to the user in the most relevant way.

[0351] For example, a possible prompt might be "Tell me some movies to help me relax today," and the system would respond to such a user request by suggesting comedies or animated films, providing information that is suitable for the user.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] Users request information from the information processing terminal via voice or text. For example, they might type, "Tell me some recommended news." This request is converted into digital data by the terminal and sent to the server. In the case of voice input, speech recognition technology is used to convert it into text. The input is voice / text data, and the output is digital text data.

[0355] Step 2:

[0356] The server analyzes the received digital text data using natural language processing techniques to identify the user's intent. This analysis utilizes morphological analysis and intent classification algorithms. The input is digital text data, and the output is a data structure that indicates the user's intent. Specifically, it performs sentence structure analysis and keyword extraction.

[0357] Step 3:

[0358] Based on the analysis results, the server sends requests to appropriate databases and external information sources to retrieve information. In this process, it collects relevant data via APIs. The input is a data structure representing the user's intent, and the output is a dataset of the retrieved information. The specific operation involves API calls.

[0359] Step 4:

[0360] The server performs sentiment analysis so that the sentiment analysis engine can evaluate the user's emotional state. This analysis uses the content of the input text and voice samples. The input is voice tone and text data, and the output is data indicating the user's emotional state. Specific techniques include voice tone analysis and text sentiment analysis.

[0361] Step 5:

[0362] The server personalizes information based on acquired data, the user's profile, and their emotional state. In this process, a generative AI model is used to organize and summarize the data, creating an optimized information set. The input consists of acquired data sets and emotional state data, while the output is a personalized information set.

[0363] Step 6:

[0364] Ultimately, the server sends personalized information to the device. The device then displays the received information to the user in a visually easy-to-understand format. Specific examples of this could include infographics or list-based displays. The input is a personalized set of information, and the output is integrated information presented to the user.

[0365] (Application Example 2)

[0366] 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."

[0367] In today's information society, users are provided with a vast amount of information, but this information often does not match their current emotions or psychological state. This can lead to problems such as users experiencing stress due to information overload or being unable to obtain necessary information in a timely manner. In particular, the lack of optimization of information delivery based on emotions makes it difficult to provide effective information that is relevant to the situation.

[0368] 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.

[0369] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information retrieval requests to relevant databases or external services based on the analysis results, means for personalizing the acquired information using the user's profile data and emotional state, and means for dynamically adjusting and presenting the personalized information according to the user's emotional state. This makes it possible to provide information optimized according to the user's emotions in real time and improve the information experience.

[0370] An "information processing device" is a device used for inputting, processing, and outputting information. It is a device that receives requests and data from users, analyzes their content, and provides results.

[0371] "Natural language processing technology" is a technology that enables computers to understand and analyze the language that humans use on a daily basis, and it is a technology that has the ability to process text and speech and interpret their meaning.

[0372] "Emotional state" refers to information that indicates the user's current psychological and physiological state, and involves detecting and classifying specific emotions (e.g., happiness, anger, stress).

[0373] Personalization is a method of tailoring and providing information and services to individual users, optimizing content based on the user's preferences and emotions.

[0374] A "database" is a system for systematically collecting and storing information, and is a structured record for efficiently searching, updating, and managing that information.

[0375] "External services" refer to external information sources or function providers on which a system depends, enabling it to obtain or provide information through a network.

[0376] The embodiments for carrying out this invention will now be described. In one embodiment of the invention, a system is constructed as follows, and by executing a program, information is personalized and provided based on the user's emotional state.

[0377] The server receives input from the information processing device and analyzes the input data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to effectively analyze the input natural language data and clarify the user's intentions and requests. In addition, the IBM Watson Tone Analyzer is used as the emotion engine to analyze the user's emotional state. This allows the server to analyze the user's emotional state, such as whether they are "feeling stressed," and use that information in the next step.

[0378] The server then integrates the analyzed data with the user profile and retrieves relevant information using the Google Maps API and other event information APIs. By retrieving and integrating information that resonates with the user's emotions, it generates an optimal set of information for the user. This personalized information is then sent to the user's device.

[0379] On the user's device, the acquired information is displayed in an easy-to-read format. The device operates on platforms such as iOS and Android, providing a user interface that enhances the user experience. Users can utilize this system to receive information optimized to their current emotions and needs.

[0380] A concrete example is a feature where, when a user enters a request such as, "I'm tired and would like to know where I can relax. Could you recommend some good places nearby?", the system provides information on nearby parks and relaxation spots. This makes it easier for users to access optimal environments and supports their choices in daily life.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The user uses a terminal to input information requests in natural language. This input includes text prompts such as "I'm tired and would like to know where I can relax." The terminal converts this input into digital data and sends it to the server.

[0384] Step 2:

[0385] The server analyzes the received digital data using natural language processing (NLP) techniques. During this process, the Google Cloud Natural Language API is used to clarify the user's intent and requests. Based on the analysis of the input data's meaning, the types and elements of information the user is seeking are extracted.

[0386] Step 3:

[0387] Next, the server analyzes the user's emotional state. It uses IBM Watson Tone Analyzer to analyze the data and identify the user's emotional state (e.g., stress, reassurance). The input is text analyzed using NLP, and the output is the user's emotional state.

[0388] Step 4:

[0389] Based on the analyzed user requests and emotional state, the server sends information retrieval requests to databases and external services. It utilizes the Google Maps API and event information API to obtain information on relaxing places and events that match the user's requests. In this process, relevant information is collected by querying external resources based on user input.

[0390] Step 5:

[0391] The server personalizes the acquired information based on the user's profile data and emotional state. It processes the data to match the information the user currently desires, resulting in an optimized information set. This enables the provision of information tailored to the user's emotional state.

[0392] Step 6:

[0393] Ultimately, the server sends personalized information to the device. The device receives this information and displays it in a format that is easy for the user to see and understand. As a result of this step, the user can obtain the information that best suits their needs.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] The system of this invention aims to provide residents and visitors within a smart city with information that meets their needs quickly and efficiently. This system consists of user terminals, servers, a database, and external services.

[0411] First, the user uses the system to input questions or requests into the terminal in natural language. For example, they might make a request such as, "Please tell me today's weather." The terminal receives this input and sends it to the server as digital data.

[0412] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to relevant databases or external services to obtain the necessary information. For example, it might send a request to an external API that provides weather information to obtain weather data for the current location.

[0413] The acquired data is integrated with user profile data on the server. This profile data records the user's past behavior history and preferences, and is used to personalize the information. For example, if a user prefers information about a particular event, the system will adjust to prioritize providing that information.

[0414] Furthermore, the server generates specific answers for the user based on personalized information and sends them to the device. The device then displays the received information to the user in a visually easy-to-understand format.

[0415] For example, if a user wants to find a new restaurant, the system provides a list of restaurant options. Furthermore, by personalizing details such as cuisine type, price range, location, and travel time, it expands the user's choices and helps them make the best selection for their purpose.

[0416] Thus, the system of the present invention provides information tailored to the individual needs of users, thereby improving the convenience of life in smart cities.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] Users input questions and requests using natural language via their devices. For example, they might type, "Tell me about nearby cafes."

[0420] Step 2:

[0421] The terminal converts user input into digital data and sends this data to the server.

[0422] Step 3:

[0423] The server passes the received data to a natural language processing engine, which analyzes the input to extract the user's intent and entities. At this point, it recognizes that the user needs information about cafes.

[0424] Step 4:

[0425] Based on the analysis results, the server sends information retrieval requests to the relevant database or external API. In this case, it sends a query to an external service to retrieve cafe data based on geographical information.

[0426] Step 5:

[0427] The server receives cafe information obtained from an external service and references user profiles in its own database. This allows it to personalize information based on the user's past behavior and preferences.

[0428] Step 6:

[0429] Based on the data it receives, the server generates information tailored to the user's needs. For example, it might summarize specific recommendations such as, "Cafes A and B, located a 5-minute walk apart, are now open."

[0430] Step 7:

[0431] The server compiles the final response and sends it to the terminal.

[0432] Step 8:

[0433] The device presents received information to the user in an easy-to-understand manner. This allows the user to intuitively understand the information and make decisions about their actions.

[0434] (Example 1)

[0435] 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."

[0436] In today's information and communication environment, users have access to vast amounts of information, but they face the challenge of obtaining accurate and personalized information from that vast amount. Furthermore, there is a demand for providing information that matches users' needs and preferences, but conventional systems are not adequately able to do so. To solve this problem, a method is needed that appropriately understands user intent and quickly and efficiently acquires and provides information based on that intent.

[0437] 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.

[0438] In this invention, the server includes means for receiving input from an information processing terminal, means for analyzing the received input using natural language processing technology to identify the user's intent, and means for sending an information retrieval request to a relevant recording medium or external information source based on the analysis results. This enables a rapid response to user requests and the provision of personalized information tailored to their needs.

[0439] An "information processing terminal" is an electronic device that receives input from a user and transmits it to a server as digital data.

[0440] "Natural language processing technology" is a technology that enables computers to understand and analyze natural language used by humans.

[0441] A "generative technology model" is an algorithm or system that generates content tailored to the user's profile based on acquired information, thereby personalizing the information.

[0442] A "recording medium" refers to a recording device or storage system that stores data and allows it to be accessed as needed.

[0443] "External information sources" refer to sources of information that are referenced to obtain necessary data, such as data provision services or APIs that exist outside the system.

[0444] "User profile data" refers to individual data that records a user's behavioral history and preferences, and is used to provide personalized information.

[0445] This invention is an information provision system for smart cities, aiming to provide information quickly and accurately tailored to the individual needs of users. The embodiments thereof will be described below.

[0446] Users input questions and requests in natural language via their information processing terminals. These terminals support text and voice input and feature a user-friendly interface. For example, a user might ask, "Can you tell me about a nearby cafe?"

[0447] The terminal converts user input into a digital format and sends it to the server. This terminal uses wireless communication technology to transmit data quickly, and security is also considered.

[0448] The server analyzes the received digital data using natural language processing techniques to identify the user's intent. Open-source natural language processing libraries and commercial natural language processing engines can be used here. Based on the analysis results, the server sends requests for information retrieval to relevant storage media or external information sources. For example, it might use an external geographic information API to obtain information about nearby cafes.

[0449] The acquired data is integrated with user profile data using a generative technology model and then personalized. This profile data includes past access history and user preferences. The server uses this information to generate relevant information and suggestions.

[0450] Personalized information is sent to the device and presented to the user in a visually easy-to-understand format. For example, the location of a cafe might be displayed on a map application, and route guidance could be provided.

[0451] For example, if a user wants to search for a restaurant based on specific criteria, they can input "Tell me the nearest restaurant that serves vegetarian food." Another example of a prompt message is, "I want to find a new restaurant. Please generate recommendations based on my current location, preferred cuisine, and budget." This system improves user convenience and supports efficient information retrieval.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The user inputs the desired information in natural language using an information processing terminal. For example, they might input a question like, "Where's a good pizza place?" This input is passed to the terminal as data and then directly to the next processing step.

[0455] Step 2:

[0456] The terminal sends the received natural language input as text data to the server. In the case of voice input, speech recognition technology is used to convert it into text. The converted text data is then sent to the server.

[0457] Step 3:

[0458] The server analyzes the transmitted text data using natural language processing (NLP) techniques. A generative AI model is used for this analysis. This process grasps the intent of the input query and extracts keywords and phrases necessary to obtain specific information. As a result, data is obtained that clarifies the user's request.

[0459] Step 4:

[0460] Based on the analysis results, the server sends an information retrieval request to an appropriate storage medium or external information source. For example, it might use a geographic information API to query information about pizza restaurants near the user's current location. The output here is a dataset of the queried pizza restaurant information.

[0461] Step 5:

[0462] The server generates personalized information using a generative technology model based on the acquired information. In this process, it considers the user's profile data—that is, their preferences and past behavioral history—to derive the most relevant information for the user. The output of this process is customized information presented to the user.

[0463] Step 6:

[0464] On the device, personalized information sent from the server is received and displayed to the user in a visually easy-to-understand format. For example, a map application might display the location and rating of the nearest pizza restaurant and provide navigation. In this final step, the user can efficiently obtain the desired information.

[0465] (Application Example 1)

[0466] 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."

[0467] In smart cities, residents and visitors need diverse information, but obtaining that information quickly and accurately is challenging. In particular, visitors need real-time personalized information based on their location and personal interests when sightseeing. Current systems fail to adequately meet these requirements.

[0468] 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.

[0469] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information acquisition requests to relevant data storage media or external services based on the analysis results, means for personalizing the acquired information using the user's personal information data, means for presenting the personalized information to the user, means for acquiring relevant information from external services based on location information, and means for organizing the acquired information based on the user's interests. This improves the efficiency and personalization of information provision in smart cities and makes it possible to quickly provide users with the most relevant information.

[0470] An "information processing device" is a device that receives input from a user and processes and analyzes that data.

[0471] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0472] A "data storage medium" is a device or system used to record and store information over a long period of time.

[0473] "External services" are resources that exist outside the system and provide data or functionality online or via a network.

[0474] "Personal information data" refers to data that includes information related to a specific user, such as details of their past behavior and preferences.

[0475] "Personalizing" means customizing information to suit the individual user's preferences and needs.

[0476] "Location information" refers to information about a user's current location or a specific point, and is data used to determine a geographical location.

[0477] "User interests" refer to the themes and topics that individual users are particularly interested in.

[0478] The system for carrying out this invention comprises a terminal directly operated by the user, a server for data processing, a database for storing related information, and a service that serves as an external information source. The user inputs their request into the terminal in natural language. The input information is transmitted to the server via the terminal. The server analyzes this input using natural language processing technology to clarify the user's intent. Based on the results of the analysis, it sends requests for information retrieval to the relevant database and external services.

[0479] The server retrieves the necessary information, integrates it with the user's personal data, and personalizes the information. The personalized information is organized in a way that is most relevant to the user. Furthermore, the server retrieves relevant information from the surrounding area based on the user's location and displays it according to the user's interests.

[0480] As a concrete example, when a user visits a historical tourist site using their smartphone, they can obtain detailed historical information and recommended spots in real time. Based on the user's location information and past interest data, the server selects and provides appropriate information to the user.

[0481] An example of a prompt is, "I want to create a tourist information guide app for smart city visitors. Please give me design ideas for an app that provides real-time historical information and recommended spots based on the visitor's current location." By inputting such prompts into the generating AI model, it is possible to obtain design references for the system.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The user uses a terminal to input a question or request in natural language. This input information is recorded on the terminal as text data and sent directly to the server. The terminal's role is to convert the user's input into a digital format and send it to the server.

[0485] Step 2:

[0486] The server receives text data sent from the terminal. Next, the server uses natural language processing technology to analyze the input and clarify the user's intent. The text data, as input, undergoes grammatical and semantic analysis, and the user's question and information request are output as explicit target data.

[0487] Step 3:

[0488] Based on the analysis results, the server sends information retrieval requests to relevant databases or external services. For example, based on the topics identified in the analysis results, it calls the appropriate external API to retrieve data. The target data, as input, is converted into the format of an API request, and the appropriate information is collected from the external source.

[0489] Step 4:

[0490] The server integrates the acquired information with the user's personal information data. During this process, past behavioral history and preferences are taken into consideration to personalize the information in a way that is beneficial to the user. The input external information data and individual profile data are processed together and output as customized information.

[0491] Step 5:

[0492] The server uses the user's location information to acquire further relevant information and organize it according to the user's interests. Location-dependent information is retrieved from external services, filtered according to interests, and presented to the user in the most optimal format. Location information as input is crucial as the foundation for the filtered output information.

[0493] Step 6:

[0494] The server sends personalized and organized information to the terminal. The terminal displays this information to the user in a visually understandable format. The terminal's role is to receive the formatted output information and present it appropriately in the user interface.

[0495] 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.

[0496] The present invention aims to recognize a user's emotions in real time and provide personalized information based on those emotions. This system consists of a user's terminal, a server, an emotion engine, a database, and external services.

[0497] First, the user inputs questions or requests for information into the system using natural language via a terminal. For example, they might request, "Tell me today's recommended news." The terminal receives the input information, converts it into a digital format, and sends it to the server.

[0498] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to appropriate databases or external APIs to collect relevant information.

[0499] The acquired information is integrated with the user profile, and the emotion engine simultaneously evaluates the user's emotional state. The emotion engine can read emotions from the user's tone of voice, the content of the input text, and even the user's eye movements and facial expressions. For example, if the user is feeling stressed, the information is personalized to provide more relaxing content.

[0500] The server considers data from the emotion engine and the user profile to generate an optimized information set. Because the information set is adjusted to reflect the user's current emotional state, users can receive information that is more comfortable and relevant to their purpose.

[0501] For example, if the system detects that a user is feeling tired, it will suggest light reading material or relaxation-related content. Conversely, if the user is focused, it can provide detailed, technical articles.

[0502] Ultimately, the server sends this individually tailored information to the terminal, which then displays it to the user in a visually appealing and easy-to-understand format. This allows users to receive information that best suits their emotional state, significantly improving the information experience in smart cities.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] Users input questions and information requests in natural language into their devices. For example, they might make a request like, "Tell me the latest news."

[0506] Step 2:

[0507] The terminal prepares to digitize user input as voice or text data and send it to the server.

[0508] Step 3:

[0509] The server passes the received digital data to a natural language processing engine, which analyzes the input and extracts the user's intent. It then determines that news information is needed.

[0510] Step 4:

[0511] The emotion engine analyzes user input and voice data, evaluating the emotional state from voice tone and text. For example, it can determine whether the user is in a happy mood or an anxious state.

[0512] Step 5:

[0513] Based on the analysis results, the server sends information retrieval requests to relevant databases or external news services. It collects data on news headlines and content.

[0514] Step 6:

[0515] The server integrates the acquired information with the user profile and sentiment engine results to personalize it. If the user is feeling stressed, the information is adjusted to prioritize relaxing news.

[0516] Step 7:

[0517] The server generates news sets optimized for the user's emotional state and organizes them for presentation.

[0518] Step 8:

[0519] The server sends the final set of information to the terminal.

[0520] Step 9:

[0521] The device presents the received information to the user in an easy-to-understand manner. This allows the user to intuitively obtain information that is relevant to their emotional state.

[0522] (Example 2)

[0523] 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."

[0524] In modern information processing systems, personalizing information based on user emotions is not sufficiently implemented, and providing information based on real-time emotion assessment is particularly difficult. Conventional methods fail to provide information that deeply understands the user's intentions and emotions, resulting in a problem of unoptimized user experience.

[0525] 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.

[0526] In this invention, the server includes means for converting natural language input acquired from an information processing terminal into a digital format, means for analyzing the input using speech recognition technology and natural language processing technology to clarify the user's intent, and means for personalizing the acquired information based on the user's profile and emotional state. This makes it possible to provide appropriate information in real time according to the user's emotional state.

[0527] An "information processing terminal" is a device that receives input from a user and converts it into digital data for processing.

[0528] "Natural language input" refers to instructions or questions in everyday language that a user provides to a system, either by voice or text.

[0529] A "digital format" is a data format that converts analog data into a structure that can be processed by a computer.

[0530] "Speech recognition technology" is a technology that analyzes speech data and converts it into text.

[0531] "Natural language processing technology" refers to the technology used to understand, interpret, and generate human language using computers.

[0532] "User intent" refers to the specific results or objectives that a user expects from the system.

[0533] "Emotional state" refers to the user's psychological state, evaluated based on various indicators that show the user's emotions.

[0534] Personalization is the process of providing information and services in a way that is optimized for a specific user.

[0535] The embodiment of the invention is a system that recognizes the user's intentions and emotional state in real time in response to their information requests and provides optimized information. This system mainly consists of an information processing terminal, a server, an emotion analysis engine, a database, and an external information source.

[0536] The user provides input in natural language to an information processing terminal. In the case of voice input, the terminal converts the data into digital format using speech recognition technology (for example, general speech conversion software) and transfers it to the server.

[0537] The server uses natural language processing techniques to analyze the received digital data. For example, it employs text analysis software and natural language processing libraries. It also utilizes an emotion analysis engine to evaluate the user's emotional state based on their voice tone and input text. This evaluation is performed by combining image recognition techniques for facial expression analysis with voice analysis techniques.

[0538] The server further integrates the acquired information with the user's profile information and personalizes the information based on their emotional state. This personalization uses generative AI technology to summarize and organize the information so that it is presented to the user in the most relevant way.

[0539] For example, a possible prompt might be "Tell me some movies to help me relax today," and the system would respond to such a user request by suggesting comedies or animated films, providing information that is suitable for the user.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] Users request information from the information processing terminal via voice or text. For example, they might type, "Tell me some recommended news." This request is converted into digital data by the terminal and sent to the server. In the case of voice input, speech recognition technology is used to convert it into text. The input is voice / text data, and the output is digital text data.

[0543] Step 2:

[0544] The server analyzes the received digital text data using natural language processing techniques to identify the user's intent. This analysis utilizes morphological analysis and intent classification algorithms. The input is digital text data, and the output is a data structure that indicates the user's intent. Specifically, it performs sentence structure analysis and keyword extraction.

[0545] Step 3:

[0546] Based on the analysis results, the server sends requests to appropriate databases and external information sources to retrieve information. In this process, it collects relevant data via APIs. The input is a data structure representing the user's intent, and the output is a dataset of the retrieved information. The specific operation involves API calls.

[0547] Step 4:

[0548] The server performs sentiment analysis so that the sentiment analysis engine can evaluate the user's emotional state. This analysis uses the content of the input text and voice samples. The input is voice tone and text data, and the output is data indicating the user's emotional state. Specific techniques include voice tone analysis and text sentiment analysis.

[0549] Step 5:

[0550] The server personalizes information based on acquired data, the user's profile, and their emotional state. In this process, a generative AI model is used to organize and summarize the data, creating an optimized information set. The input consists of acquired data sets and emotional state data, while the output is a personalized information set.

[0551] Step 6:

[0552] Ultimately, the server sends personalized information to the device. The device then displays the received information to the user in a visually easy-to-understand format. Specific examples of this could include infographics or list-based displays. The input is a personalized set of information, and the output is integrated information presented to the user.

[0553] (Application Example 2)

[0554] 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."

[0555] In today's information society, users are provided with a vast amount of information, but this information often does not match their current emotions or psychological state. This can lead to problems such as users experiencing stress due to information overload or being unable to obtain necessary information in a timely manner. In particular, the lack of optimization of information delivery based on emotions makes it difficult to provide effective information that is relevant to the situation.

[0556] 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.

[0557] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information retrieval requests to relevant databases or external services based on the analysis results, means for personalizing the acquired information using the user's profile data and emotional state, and means for dynamically adjusting and presenting the personalized information according to the user's emotional state. This makes it possible to provide information optimized according to the user's emotions in real time and improve the information experience.

[0558] An "information processing device" is a device used for inputting, processing, and outputting information. It is a device that receives requests and data from users, analyzes their content, and provides results.

[0559] "Natural language processing technology" is a technology that enables computers to understand and analyze the language that humans use on a daily basis, and it is a technology that has the ability to process text and speech and interpret their meaning.

[0560] "Emotional state" refers to information that indicates the user's current psychological and physiological state, and involves detecting and classifying specific emotions (e.g., happiness, anger, stress).

[0561] Personalization is a method of tailoring and providing information and services to individual users, optimizing content based on the user's preferences and emotions.

[0562] A "database" is a system for systematically collecting and storing information, and is a structured record for efficiently searching, updating, and managing that information.

[0563] "External services" refer to external information sources or function providers on which a system depends, enabling it to obtain or provide information through a network.

[0564] The embodiments for carrying out this invention will now be described. In one embodiment of the invention, a system is constructed as follows, and by executing a program, information is personalized and provided based on the user's emotional state.

[0565] The server receives input from the information processing device and analyzes the input data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to effectively analyze the input natural language data and clarify the user's intentions and requests. In addition, the IBM Watson Tone Analyzer is used as the emotion engine to analyze the user's emotional state. This allows the server to analyze the user's emotional state, such as whether they are "feeling stressed," and use that information in the next step.

[0566] The server then integrates the analyzed data with the user profile and retrieves relevant information using the Google Maps API and other event information APIs. By retrieving and integrating information that resonates with the user's emotions, it generates an optimal set of information for the user. This personalized information is then sent to the user's device.

[0567] On the user's device, the acquired information is displayed in an easy-to-read format. The device operates on platforms such as iOS and Android, providing a user interface that enhances the user experience. Users can utilize this system to receive information optimized to their current emotions and needs.

[0568] A concrete example is a feature where, when a user enters a request such as, "I'm tired and would like to know where I can relax. Could you recommend some good places nearby?", the system provides information on nearby parks and relaxation spots. This makes it easier for users to access optimal environments and supports their choices in daily life.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The user uses a terminal to input information requests in natural language. This input includes text prompts such as "I'm tired and would like to know where I can relax." The terminal converts this input into digital data and sends it to the server.

[0572] Step 2:

[0573] The server analyzes the received digital data using natural language processing (NLP) techniques. During this process, the Google Cloud Natural Language API is used to clarify the user's intent and requests. Based on the analysis of the input data's meaning, the types and elements of information the user is seeking are extracted.

[0574] Step 3:

[0575] Next, the server analyzes the user's emotional state. It uses IBM Watson Tone Analyzer to analyze the data and identify the user's emotional state (e.g., stress, reassurance). The input is text analyzed using NLP, and the output is the user's emotional state.

[0576] Step 4:

[0577] Based on the analyzed user requests and emotional state, the server sends information retrieval requests to databases and external services. It utilizes the Google Maps API and event information API to obtain information on relaxing places and events that match the user's requests. In this process, relevant information is collected by querying external resources based on user input.

[0578] Step 5:

[0579] The server personalizes the acquired information based on the user's profile data and emotional state. It processes the data to match the information the user currently desires, resulting in an optimized information set. This enables the provision of information tailored to the user's emotional state.

[0580] Step 6:

[0581] Ultimately, the server sends personalized information to the device. The device receives this information and displays it in a format that is easy for the user to see and understand. As a result of this step, the user can obtain the information that best suits their needs.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] The system of this invention aims to provide residents and visitors within a smart city with information that meets their needs quickly and efficiently. This system consists of user terminals, servers, a database, and external services.

[0600] First, the user uses the system to input questions or requests into the terminal in natural language. For example, they might make a request such as, "Please tell me today's weather." The terminal receives this input and sends it to the server as digital data.

[0601] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to relevant databases or external services to obtain the necessary information. For example, it might send a request to an external API that provides weather information to obtain weather data for the current location.

[0602] The acquired data is integrated with user profile data on the server. This profile data records the user's past behavior history and preferences, and is used to personalize the information. For example, if a user prefers information about a particular event, the system will adjust to prioritize providing that information.

[0603] Furthermore, the server generates specific answers for the user based on personalized information and sends them to the device. The device then displays the received information to the user in a visually easy-to-understand format.

[0604] For example, if a user wants to find a new restaurant, the system provides a list of restaurant options. Furthermore, by personalizing details such as cuisine type, price range, location, and travel time, it expands the user's choices and helps them make the best selection for their purpose.

[0605] Thus, the system of the present invention provides information tailored to the individual needs of users, thereby improving the convenience of life in smart cities.

[0606] The following describes the processing flow.

[0607] Step 1:

[0608] Users input questions and requests using natural language via their devices. For example, they might type, "Tell me about nearby cafes."

[0609] Step 2:

[0610] The terminal converts user input into digital data and sends this data to the server.

[0611] Step 3:

[0612] The server passes the received data to a natural language processing engine, which analyzes the input to extract the user's intent and entities. At this point, it recognizes that the user needs information about cafes.

[0613] Step 4:

[0614] Based on the analysis results, the server sends information retrieval requests to the relevant database or external API. In this case, it sends a query to an external service to retrieve cafe data based on geographical information.

[0615] Step 5:

[0616] The server receives cafe information obtained from an external service and references user profiles in its own database. This allows it to personalize information based on the user's past behavior and preferences.

[0617] Step 6:

[0618] Based on the data it receives, the server generates information tailored to the user's needs. For example, it might summarize specific recommendations such as, "Cafes A and B, located a 5-minute walk apart, are now open."

[0619] Step 7:

[0620] The server compiles the final response and sends it to the terminal.

[0621] Step 8:

[0622] The device presents received information to the user in an easy-to-understand manner. This allows the user to intuitively understand the information and make decisions about their actions.

[0623] (Example 1)

[0624] 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".

[0625] In today's information and communication environment, users have access to vast amounts of information, but they face the challenge of obtaining accurate and personalized information from that vast amount. Furthermore, there is a demand for providing information that matches users' needs and preferences, but conventional systems are not adequately able to do so. To solve this problem, a method is needed that appropriately understands user intent and quickly and efficiently acquires and provides information based on that intent.

[0626] 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.

[0627] In this invention, the server includes means for receiving input from an information processing terminal, means for analyzing the received input using natural language processing technology to identify the user's intent, and means for sending an information retrieval request to a relevant recording medium or external information source based on the analysis results. This enables a rapid response to user requests and the provision of personalized information tailored to their needs.

[0628] An "information processing terminal" is an electronic device that receives input from a user and transmits it to a server as digital data.

[0629] "Natural language processing technology" is a technology that enables computers to understand and analyze natural language used by humans.

[0630] A "generative technology model" is an algorithm or system that generates content tailored to the user's profile based on acquired information, thereby personalizing the information.

[0631] A "recording medium" refers to a recording device or storage system that stores data and allows it to be accessed as needed.

[0632] "External information sources" refer to sources of information that are referenced to obtain necessary data, such as data provision services or APIs that exist outside the system.

[0633] "User profile data" refers to individual data that records a user's behavioral history and preferences, and is used to provide personalized information.

[0634] This invention is an information provision system for smart cities, aiming to provide information quickly and accurately tailored to the individual needs of users. The embodiments thereof will be described below.

[0635] Users input questions and requests in natural language via their information processing terminals. These terminals support text and voice input and feature a user-friendly interface. For example, a user might ask, "Can you tell me about a nearby cafe?"

[0636] The terminal converts user input into a digital format and sends it to the server. This terminal uses wireless communication technology to transmit data quickly, and security is also considered.

[0637] The server analyzes the received digital data using natural language processing techniques to identify the user's intent. Open-source natural language processing libraries and commercial natural language processing engines can be used here. Based on the analysis results, the server sends requests for information retrieval to relevant storage media or external information sources. For example, it might use an external geographic information API to obtain information about nearby cafes.

[0638] The acquired data is integrated with user profile data using a generative technology model and then personalized. This profile data includes past access history and user preferences. The server uses this information to generate relevant information and suggestions.

[0639] Personalized information is sent to the device and presented to the user in a visually easy-to-understand format. For example, the location of a cafe might be displayed on a map application, and route guidance could be provided.

[0640] For example, if a user wants to search for a restaurant based on specific criteria, they can input "Tell me the nearest restaurant that serves vegetarian food." Another example of a prompt message is, "I want to find a new restaurant. Please generate recommendations based on my current location, preferred cuisine, and budget." This system improves user convenience and supports efficient information retrieval.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] The user inputs the desired information in natural language using an information processing terminal. For example, they might input a question like, "Where's a good pizza place?" This input is passed to the terminal as data and then directly to the next processing step.

[0644] Step 2:

[0645] The terminal sends the received natural language input as text data to the server. In the case of voice input, speech recognition technology is used to convert it into text. The converted text data is then sent to the server.

[0646] Step 3:

[0647] The server analyzes the transmitted text data using natural language processing (NLP) techniques. A generative AI model is used for this analysis. This process grasps the intent of the input query and extracts keywords and phrases necessary to obtain specific information. As a result, data is obtained that clarifies the user's request.

[0648] Step 4:

[0649] Based on the analysis results, the server sends an information retrieval request to an appropriate storage medium or external information source. For example, it might use a geographic information API to query information about pizza restaurants near the user's current location. The output here is a dataset of the queried pizza restaurant information.

[0650] Step 5:

[0651] The server generates personalized information using a generative technology model based on the acquired information. In this process, it considers the user's profile data—that is, their preferences and past behavioral history—to derive the most relevant information for the user. The output of this process is customized information presented to the user.

[0652] Step 6:

[0653] On the device, personalized information sent from the server is received and displayed to the user in a visually easy-to-understand format. For example, a map application might display the location and rating of the nearest pizza restaurant and provide navigation. In this final step, the user can efficiently obtain the desired information.

[0654] (Application Example 1)

[0655] 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".

[0656] In smart cities, residents and visitors need diverse information, but obtaining that information quickly and accurately is challenging. In particular, visitors need real-time personalized information based on their location and personal interests when sightseeing. Current systems fail to adequately meet these requirements.

[0657] 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.

[0658] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information acquisition requests to relevant data storage media or external services based on the analysis results, means for personalizing the acquired information using the user's personal information data, means for presenting the personalized information to the user, means for acquiring relevant information from external services based on location information, and means for organizing the acquired information based on the user's interests. This improves the efficiency and personalization of information provision in smart cities and makes it possible to quickly provide users with the most relevant information.

[0659] An "information processing device" is a device that receives input from a user and processes and analyzes that data.

[0660] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0661] A "data storage medium" is a device or system used to record and store information over a long period of time.

[0662] "External services" are resources that exist outside the system and provide data or functionality online or via a network.

[0663] "Personal information data" refers to data that includes information related to a specific user, such as details of their past behavior and preferences.

[0664] "Personalizing" means customizing information to suit each user's individual preferences and needs.

[0665] "Location information" refers to information about a user's current location or a specific point, and is data used to determine a geographical location.

[0666] "User interests" refer to the themes and topics that individual users are particularly interested in.

[0667] The system for carrying out this invention comprises a terminal directly operated by the user, a server for data processing, a database for storing related information, and a service that serves as an external information source. The user inputs their request into the terminal in natural language. The input information is transmitted to the server via the terminal. The server analyzes this input using natural language processing technology to clarify the user's intent. Based on the results of the analysis, it sends requests for information retrieval to the relevant database and external services.

[0668] The server retrieves the necessary information, integrates it with the user's personal data, and personalizes the information. The personalized information is organized in a way that is most relevant to the user. Furthermore, the server retrieves relevant information from the surrounding area based on the user's location and displays it according to the user's interests.

[0669] As a concrete example, when a user visits a historical tourist site using their smartphone, they can obtain detailed historical information and recommended spots in real time. Based on the user's location information and past interest data, the server selects and provides appropriate information to the user.

[0670] An example of a prompt is, "I want to create a tourist information guide app for smart city visitors. Please give me design ideas for an app that provides real-time historical information and recommended spots based on the visitor's current location." By inputting such prompts into the generating AI model, it is possible to obtain design references for the system.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The user uses a terminal to input a question or request in natural language. This input information is recorded on the terminal as text data and sent directly to the server. The terminal's role is to convert the user's input into a digital format and send it to the server.

[0674] Step 2:

[0675] The server receives text data sent from the terminal. Next, the server uses natural language processing technology to analyze the input and clarify the user's intent. The text data, as input, undergoes grammatical and semantic analysis, and the user's question and information request are output as explicit target data.

[0676] Step 3:

[0677] Based on the analysis results, the server sends information retrieval requests to relevant databases or external services. For example, based on the topics identified in the analysis results, it calls the appropriate external API to retrieve data. The target data, as input, is converted into the format of an API request, and the appropriate information is collected from the external source.

[0678] Step 4:

[0679] The server integrates the acquired information with the user's personal information data. During this process, past behavioral history and preferences are taken into consideration to personalize the information in a way that is beneficial to the user. The input external information data and individual profile data are processed together and output as customized information.

[0680] Step 5:

[0681] The server uses the user's location information to acquire further relevant information and organize it according to the user's interests. Location-dependent information is retrieved from external services, filtered according to interests, and presented to the user in the most optimal format. Location information as input is crucial as the foundation for the filtered output information.

[0682] Step 6:

[0683] The server sends personalized and organized information to the terminal. The terminal displays this information to the user in a visually understandable format. The terminal's role is to receive the formatted output information and present it appropriately in the user interface.

[0684] 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.

[0685] The present invention aims to recognize a user's emotions in real time and provide personalized information based on those emotions. This system consists of a user's terminal, a server, an emotion engine, a database, and external services.

[0686] First, the user inputs questions or requests for information into the system using natural language via a terminal. For example, they might request, "Tell me today's recommended news." The terminal receives the input information, converts it into a digital format, and sends it to the server.

[0687] The server analyzes the received digital data using natural language processing (NLP) techniques to clarify the user's intent. Based on the analysis results, it sends information retrieval requests to appropriate databases or external APIs to collect relevant information.

[0688] The acquired information is integrated with the user profile, and the emotion engine simultaneously evaluates the user's emotional state. The emotion engine can read emotions from the user's tone of voice, the content of the input text, and even the user's eye movements and facial expressions. For example, if the user is feeling stressed, the information is personalized to provide more relaxing content.

[0689] The server considers data from the emotion engine and the user profile to generate an optimized information set. Because the information set is adjusted to reflect the user's current emotional state, users can receive information that is more comfortable and relevant to their purpose.

[0690] For example, if the system detects that a user is feeling tired, it will suggest light reading material or relaxation-related content. Conversely, if the user is focused, it can provide detailed, technical articles.

[0691] Ultimately, the server sends this individually tailored information to the terminal, which then displays it to the user in a visually appealing and easy-to-understand format. This allows users to receive information that best suits their emotional state, significantly improving the information experience in smart cities.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] Users input questions and information requests in natural language into their devices. For example, they might make a request like, "Tell me the latest news."

[0695] Step 2:

[0696] The terminal prepares to digitize user input as voice or text data and send it to the server.

[0697] Step 3:

[0698] The server passes the received digital data to a natural language processing engine, which analyzes the input and extracts the user's intent. It then determines that news information is needed.

[0699] Step 4:

[0700] The emotion engine analyzes user input and voice data, evaluating the emotional state from voice tone and text. For example, it can determine whether the user is in a happy mood or an anxious state.

[0701] Step 5:

[0702] Based on the analysis results, the server sends information retrieval requests to relevant databases or external news services. It collects data on news headlines and content.

[0703] Step 6:

[0704] The server integrates the acquired information with the user profile and sentiment engine results to personalize it. If the user is feeling stressed, the information is adjusted to prioritize relaxing news.

[0705] Step 7:

[0706] The server generates news sets optimized for the user's emotional state and organizes them for presentation.

[0707] Step 8:

[0708] The server sends the final set of information to the terminal.

[0709] Step 9:

[0710] The device presents the received information to the user in an easy-to-understand manner. This allows the user to intuitively obtain information that is relevant to their emotional state.

[0711] (Example 2)

[0712] 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".

[0713] In modern information processing systems, personalizing information based on user emotions is not sufficiently implemented, and providing information based on real-time emotion assessment is particularly difficult. Conventional methods fail to provide information that deeply understands the user's intentions and emotions, resulting in a problem of unoptimized user experience.

[0714] 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.

[0715] In this invention, the server includes means for converting natural language input acquired from an information processing terminal into a digital format, means for analyzing the input using speech recognition technology and natural language processing technology to clarify the user's intent, and means for personalizing the acquired information based on the user's profile and emotional state. This makes it possible to provide appropriate information in real time according to the user's emotional state.

[0716] An "information processing terminal" is a device that receives input from a user and converts it into digital data for processing.

[0717] "Natural language input" refers to instructions or questions in everyday language that a user provides to a system, either by voice or text.

[0718] A "digital format" is a data format that converts analog data into a structure that can be processed by a computer.

[0719] "Speech recognition technology" is a technology that analyzes speech data and converts it into text.

[0720] "Natural language processing technology" refers to the technology used to understand, interpret, and generate human language using computers.

[0721] "User intent" refers to the specific results or objectives that a user expects from the system.

[0722] "Emotional state" refers to the user's psychological state, evaluated based on various indicators that show the user's emotions.

[0723] Personalization is the process of providing information and services in a way that is optimized for a specific user.

[0724] The embodiment of the invention is a system that recognizes the user's intentions and emotional state in real time in response to their information requests and provides optimized information. This system mainly consists of an information processing terminal, a server, an emotion analysis engine, a database, and an external information source.

[0725] The user provides input in natural language to an information processing terminal. In the case of voice input, the terminal converts the data into digital format using speech recognition technology (for example, general speech conversion software) and transfers it to the server.

[0726] The server uses natural language processing techniques to analyze the received digital data. For example, it employs text analysis software and natural language processing libraries. It also utilizes an emotion analysis engine to evaluate the user's emotional state based on their voice tone and input text. This evaluation is performed by combining image recognition techniques for facial expression analysis with voice analysis techniques.

[0727] The server further integrates the acquired information with the user's profile information and personalizes the information based on their emotional state. This personalization uses generative AI technology to summarize and organize the information so that it is presented to the user in the most relevant way.

[0728] For example, a possible prompt might be "Tell me some movies to help me relax today," and the system would respond to such a user request by suggesting comedies or animated films, providing information that is suitable for the user.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] Users request information from the information processing terminal via voice or text. For example, they might type, "Tell me some recommended news." This request is converted into digital data by the terminal and sent to the server. In the case of voice input, speech recognition technology is used to convert it into text. The input is voice / text data, and the output is digital text data.

[0732] Step 2:

[0733] The server analyzes the received digital text data using natural language processing techniques to identify the user's intent. This analysis utilizes morphological analysis and intent classification algorithms. The input is digital text data, and the output is a data structure that indicates the user's intent. Specifically, it performs sentence structure analysis and keyword extraction.

[0734] Step 3:

[0735] Based on the analysis results, the server sends requests to appropriate databases and external information sources to retrieve information. In this process, it collects relevant data via APIs. The input is a data structure representing the user's intent, and the output is a dataset of the retrieved information. The specific operation involves API calls.

[0736] Step 4:

[0737] The server performs sentiment analysis so that the sentiment analysis engine can evaluate the user's emotional state. This analysis uses the content of the input text and voice samples. The input is voice tone and text data, and the output is data indicating the user's emotional state. Specific techniques include voice tone analysis and text sentiment analysis.

[0738] Step 5:

[0739] The server personalizes information based on acquired data, the user's profile, and their emotional state. In this process, a generative AI model is used to organize and summarize the data, creating an optimized information set. The input consists of acquired data sets and emotional state data, while the output is a personalized information set.

[0740] Step 6:

[0741] Ultimately, the server sends personalized information to the device. The device then displays the received information to the user in a visually easy-to-understand format. Specific examples of this could include infographics or list-based displays. The input is a personalized set of information, and the output is integrated information presented to the user.

[0742] (Application Example 2)

[0743] 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".

[0744] In today's information society, users are provided with a vast amount of information, but this information often does not match their current emotions or psychological state. This can lead to problems such as users experiencing stress due to information overload or being unable to obtain necessary information in a timely manner. In particular, the lack of optimization of information delivery based on emotions makes it difficult to provide effective information that is relevant to the situation.

[0745] 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.

[0746] In this invention, the server includes means for receiving input from an information processing device, means for analyzing the received input using natural language processing technology, means for sending information retrieval requests to relevant databases or external services based on the analysis results, means for personalizing the acquired information using the user's profile data and emotional state, and means for dynamically adjusting and presenting the personalized information according to the user's emotional state. This makes it possible to provide information optimized according to the user's emotions in real time and improve the information experience.

[0747] An "information processing device" is a device used for inputting, processing, and outputting information. It is a device that receives requests and data from users, analyzes their content, and provides results.

[0748] "Natural language processing technology" is a technology that enables computers to understand and analyze the language that humans use on a daily basis, and it is a technology that has the ability to process text and speech and interpret their meaning.

[0749] "Emotional state" refers to information that indicates the user's current psychological and physiological state, and involves detecting and classifying specific emotions (e.g., happiness, anger, stress).

[0750] Personalization is a method of tailoring and providing information and services to individual users, optimizing content based on the user's preferences and emotions.

[0751] A "database" is a system for systematically collecting and storing information, and is a structured record for efficiently searching, updating, and managing that information.

[0752] "External services" refer to external information sources or function providers on which a system depends, enabling it to obtain or provide information through a network.

[0753] The embodiments for carrying out this invention will now be described. In one embodiment of the invention, a system is constructed as follows, and by executing a program, information is personalized and provided based on the user's emotional state.

[0754] The server receives input from the information processing device and analyzes the input data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to effectively analyze the input natural language data and clarify the user's intentions and requests. In addition, the IBM Watson Tone Analyzer is used as the emotion engine to analyze the user's emotional state. This allows the server to analyze the user's emotional state, such as whether they are "feeling stressed," and use that information in the next step.

[0755] The server then integrates the analyzed data with the user profile and retrieves relevant information using the Google Maps API and other event information APIs. By retrieving and integrating information that resonates with the user's emotions, it generates an optimal set of information for the user. This personalized information is then sent to the user's device.

[0756] On the user's device, the acquired information is displayed in an easy-to-read format. The device operates on platforms such as iOS and Android, providing a user interface that enhances the user experience. Users can utilize this system to receive information optimized to their current emotions and needs.

[0757] A concrete example is a feature where, when a user enters a request such as, "I'm tired and would like to know where I can relax. Could you recommend some good places nearby?", the system provides information on nearby parks and relaxation spots. This makes it easier for users to access optimal environments and supports their choices in daily life.

[0758] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0759] Step 1:

[0760] The user uses a terminal to input information requests in natural language. This input includes text prompts such as "I'm tired and would like to know where I can relax." The terminal converts this input into digital data and sends it to the server.

[0761] Step 2:

[0762] The server analyzes the received digital data using natural language processing (NLP) techniques. During this process, the Google Cloud Natural Language API is used to clarify the user's intent and requests. Based on the analysis of the input data's meaning, the types and elements of information the user is seeking are extracted.

[0763] Step 3:

[0764] Next, the server analyzes the user's emotional state. It uses IBM Watson Tone Analyzer to analyze the data and identify the user's emotional state (e.g., stress, reassurance). The input is text analyzed using NLP, and the output is the user's emotional state.

[0765] Step 4:

[0766] Based on the analyzed user requests and emotional state, the server sends information retrieval requests to databases and external services. It utilizes the Google Maps API and event information API to obtain information on relaxing places and events that match the user's requests. In this process, relevant information is collected by querying external resources based on user input.

[0767] Step 5:

[0768] The server personalizes the acquired information based on the user's profile data and emotional state. It processes the data to match the information the user currently desires, resulting in an optimized information set. This enables the provision of information tailored to the user's emotional state.

[0769] Step 6:

[0770] Ultimately, the server sends personalized information to the device. The device receives this information and displays it in a format that is easy for the user to see and understand. As a result of this step, the user can obtain the information that best suits their needs.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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."

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A means of receiving input from an information processing device,

[0795] A means of analyzing the input received using natural language processing technology,

[0796] A means for sending information retrieval requests to relevant databases or external services based on the analysis results,

[0797] A means of personalizing information obtained using user profile data,

[0798] A means of presenting personalized information to users,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, which receives user feedback and uses the feedback to improve the system's operation.

[0802] (Claim 3)

[0803] The system according to claim 1, which uses a machine learning algorithm to optimize information based on the user's behavior patterns or preferences.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A means of receiving input from an information processing terminal,

[0807] A means of analyzing input received using natural language processing technology to identify the user's intent,

[0808] A means for sending an information retrieval request to a relevant recording medium or external information source based on the analysis results,

[0809] A means of personalizing acquired information based on user profile data using a generative technology model,

[0810] A means of presenting personalized information to the user via a device,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, which receives opinions from users and utilizes that feedback to improve the operation of the information system.

[0814] (Claim 3)

[0815] The system according to claim 1, which utilizes a machine learning method to optimize information based on the user's behavior patterns or preferences.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A means of receiving input from an information processing device,

[0819] A means of analyzing the input received using natural language processing technology,

[0820] A means for sending an information retrieval request to a relevant data storage medium or external service based on the analysis results,

[0821] A means of personalizing information obtained using users' personal information data,

[0822] A means of presenting personalized information to users,

[0823] A means of obtaining relevant information from external services based on location information,

[0824] A means of organizing acquired information based on user interests,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, which receives user feedback and uses that feedback to improve the system's operation.

[0828] (Claim 3)

[0829] The system according to claim 1, which uses machine learning techniques to optimize information based on the user's behavior patterns or preferences.

[0830] "Example 2 of combining an emotion engine"

[0831] (Claim 1)

[0832] A means of converting natural language input acquired from an information processing terminal into a digital format,

[0833] A means of analyzing input using speech recognition technology and natural language processing technology to clarify the user's intent,

[0834] A means for sending information retrieval requests to relevant data sets or external information sources based on the analysis results,

[0835] A means of personalizing acquired information based on the user's profile and emotional state,

[0836] A means of providing personalized information to the device in the most optimal format,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which evaluates the user's emotional state in real time and adjusts the system's output according to the emotion.

[0840] (Claim 3)

[0841] The system according to claim 1, which uses generative AI technology to structure and summarize information and optimize the user experience.

[0842] "Application example 2 when combining with an emotional engine"

[0843] (Claim 1)

[0844] A means of receiving input from an information processing device,

[0845] A means of analyzing the input received using natural language processing technology,

[0846] A means for sending information retrieval requests to relevant databases or external services based on the analysis results,

[0847] A means of personalizing information obtained using user profile data and emotional state,

[0848] A means of dynamically adjusting and presenting personalized information according to the user's emotional state,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which receives user feedback and emotional states and uses them to improve the system's operation.

[0852] (Claim 3)

[0853] The system according to claim 1, which uses a machine learning algorithm to optimize information based on the user's behavior patterns, preferences, and emotional state. [Explanation of Symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving input from an information processing device, A means of analyzing the input received using natural language processing technology, A means for sending an information retrieval request to a relevant data storage medium or external service based on the analysis results, A means of personalizing information obtained using users' personal information data, A means of presenting personalized information to users, A means of obtaining relevant information from external services based on location information, A means of organizing acquired information based on user interests, A system that includes this.

2. The system according to claim 1, which receives user feedback and uses that feedback to improve the system's operation.

3. The system according to claim 1, which uses machine learning techniques to optimize information based on the user's behavior patterns or preferences.

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