system

Abstract

We provide the system. [Solution] A natural language processing method for analyzing information requests from users, Information gathering means for collecting relevant information from external sources based on the analyzed requirements, Information processing means that compares collected information with user history data and selects and processes appropriate information, A means of presenting selected and processed information to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult for users to efficiently collect information related to specific hobbies or favorite activities and receive appropriate suggestions. In particular, it is difficult to find highly relevant information from a large number of information sources and take immediate actions as needed. Also, procedures such as ticket reservation or purchase for events that users wish to participate in have become complicated. This has led to a decrease in user satisfaction and contributed to a decline in the motivation for hobbies.

Means for Solving the Problems

[0005] This invention includes an information gathering means that analyzes user requests using natural language processing means and collects relevant information from external sources based on the analysis results. Furthermore, it includes an information processing means that compares the collected information with the user's historical data and selects and processes personalized information, thereby efficiently providing meaningful information to the user. It also includes a means for presenting the selected and processed information to the user, and further includes a reservation means that executes reservation or purchase procedures in cooperation with external services for events that the user has shown interest in, thereby improving the user experience. As a result, users can easily obtain useful information related to their hobbies and activities and can carry out necessary procedures without any problems.

[0006] "Natural language processing means" refers to technologies that analyze user input and understand its meaning and intent.

[0007] "Information gathering means" refers to systems and processes for obtaining information related to user requests from external sources.

[0008] "Information processing means" refers to a function that analyzes and selects collected data based on the user's interests and past history, and processes it into an appropriate form.

[0009] "Presentation methods" refer to interfaces and methods for providing processed data to users in an easily understandable way.

[0010] A "reservation method" is a function that automatically or semi-automatically performs the procedures necessary for users to participate in events or services.

[0011] "Historical data" refers to records that show a user's past behavior and interests, and serves as the foundational data for providing personalized suggestions.

[0012] "External services" refer to information sources or action platforms provided by other companies or organizations that are available via the internet or a network. [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] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described 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, and the like.

[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] This invention is implemented as a system that efficiently collects information related to users' hobbies and fan activities and provides it to users in an appropriate format. This system consists of a user terminal, a server, and an internal database, and functions as follows.

[0035] The user terminal provides an interface for users to input information they are interested in. If a user inputs "Tell me about idol events next week," the request is initially parsed within the terminal and sent to the server as data.

[0036] The server analyzes received user requests using natural language processing to understand the user's intent. Based on the analysis results, information gathering tools access external event information services and related websites to collect relevant data. For example, it might retrieve information about idol events held within a specified period.

[0037] The acquired information is compared with the user's past history data by information processing tools on the server, and information that matches the user's interests is selected and processed. This history data is an accumulation of information on events and artists that the user has shown interest in in the past, and forms the basis for providing personalized suggestions.

[0038] The selected information is sent from the server to the user's terminal and presented to the user. This information is displayed in a format tailored to the user's interests, allowing them to view details and request ticket reservations for events that interest them.

[0039] For example, if a user becomes interested in a particular idol's live performance from the event information presented, the user's device sends an additional request to the server, and the reservation process begins. The server collaborates with external services to check ticket availability and completes the reservation if necessary. Through this process, the user can participate in events they are interested in with minimal effort.

[0040] In this way, the present invention can smoothly support users' hobbies and fan activities, and provide them with a highly satisfying experience.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters "Tell me about next week's idol event information" into their terminal. The user's terminal performs an initial analysis of the input and sends the analyzed data to the server.

[0044] Step 2:

[0045] The server analyzes the received request data in detail using natural language processing techniques. As a result of the analysis, attribute information such as event type, date, and region is extracted.

[0046] Step 3:

[0047] The server uses information gathering methods based on the analysis results to obtain relevant event information from external event information providers. It collects idle event data for a specified period using an external API or web crawler.

[0048] Step 4:

[0049] The collected event information is evaluated within the server using information processing tools. By referring to the user's history database and considering information about past events attended and idols they have shown interest in, the most relevant information is selected.

[0050] Step 5:

[0051] The server formats the selected event information and transmits it to the user's terminal via a presentation device in order to provide it to the user.

[0052] Step 6:

[0053] The user terminal displays received event information and provides an interface that prompts for further information and subsequent actions (e.g., ticket booking). Users can review the details here and, if necessary, request a booking.

[0054] Step 7:

[0055] When a reservation request is received, the server uses the reservation mechanism to connect with an external reservation service, check ticket availability, and complete the reservation for the user. The process is completed when the reservation result is notified to the user's device.

[0056] (Example 1)

[0057] 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."

[0058] Conventional information systems have struggled to efficiently collect event information based on user interests and present it in an optimal format. Furthermore, users had to manually research and make reservations when they showed interest in a particular event, which was time-consuming. Moreover, the systems were not adequately equipped to dynamically understand user interests and provide personalized information.

[0059] 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.

[0060] In this invention, the server includes a natural language processing means that analyzes information requests from users and extracts keywords as necessary; an information gathering means that converts the analyzed requests into prompt sentences, accesses external information sources, and collects relevant information; and an information processing means that compares the collected information with the user's past history data, selects information considering the user's interests, and visualizes it. This enables the effective collection of event information based on the user's interests, provides it in a visually easy-to-understand format, and allows for the automated execution of reservation procedures.

[0061] "Natural language processing means" refers to a program or technology that analyzes information requests received from users to understand keywords and intent.

[0062] "Information gathering means" refers to a program or technology that accesses external information sources and obtains relevant data based on analyzed information requests.

[0063] "Information processing means" refers to a program or technology for comparing collected data with the user's past history data and selecting and processing it to match the user's interests.

[0064] "Presentation means" refers to a program or interface for displaying selected and processed information to the user in an easily understandable manner.

[0065] A "reservation method" is a program or technology that automatically handles reservation and purchase procedures for events that users are interested in, by linking with external reservation services.

[0066] A "database management means" is a program or technology for dynamically collecting user preferences through interaction with users and for building and updating a database.

[0067] This invention is implemented as a system for efficiently collecting and presenting information based on user interests. The system consists of a user terminal, a server, and a database.

[0068] The user terminal provides an interface for users to input event information of interest. Specifically, it includes text input fields and category selection options. This terminal performs basic parsing to send the received input as data to the server.

[0069] The server uses natural language processing (NLP) to analyze the information request received from the user. Here, natural language processing libraries (e.g., spaCy and the GPT model) are used to understand the user's intent. At this stage, the user's input is converted into a prompt. For example, a prompt such as "Please retrieve information about the idol live concert being held next week and inform the user." is generated.

[0070] Next, the server uses information gathering tools to collect necessary data from external sources. This data is typically obtained via APIs and received in JSON format. The retrieved information is then compared with the user's past history data by information processing tools within the server, and selected and processed to reflect the user's interests.

[0071] Finally, the selected information is sent from the server to the user's terminal, which then presents the information to the user in an intuitively viewable format. For example, list format or calendar display may be used.

[0072] This system allows users to efficiently obtain information on events of interest, view details, and make reservations with minimal effort. Furthermore, it provides personalized information based on user preferences, resulting in a highly satisfying user experience.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user inputs event information of interest to their terminal. The terminal receives the user's request via a text input field, performs a basic analysis of the entered text, and extracts key keywords. It then structures this extracted data and sends it to the server. In this step, the input is the user's text data, and the output is structured data containing the extracted keywords.

[0076] Step 2:

[0077] The server analyzes the received data in detail using natural language processing techniques. Using a generative AI model, it generates prompts to understand the user's intent. These prompts are used as data collection requests to external sources. The input for this step is the user's structured data, and the output is the generated prompts.

[0078] Step 3:

[0079] The server accesses external information sources using information gathering tools and retrieves relevant information based on prompt messages. At this stage, information is collected from an external event database via a RESTful API. The collected information is imported into the server in JSON format. The input for this step is a prompt message, and the output is raw data retrieved from the external information source.

[0080] Step 4:

[0081] The server compares the collected information with the user's past history data. Using information processing tools, data is selected based on the user's interests and preferences and processed into a visually easy-to-understand format. This process involves filtering and ranking the data to identify the most relevant event information. The input for this step is raw data and user history data, and the output is selected and processed event information.

[0082] Step 5:

[0083] The server sends the selected information to the user's terminal. The terminal displays the received information on its interface, allowing the user to view the details. Methods that allow the user to operate intuitively, such as list format or calendar display, are used. The input in this step is the selected and processed information, and the output is the visual display provided to the user.

[0084] Step 6:

[0085] If a user is interested in a displayed event and wishes to make a reservation, their device sends an additional request to the server. The server then interacts with an external reservation system to check ticket availability and complete the reservation process if necessary. The input for this step is the user's additional request, and the output is reservation confirmation information.

[0086] (Application Example 1)

[0087] 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."

[0088] Traditional information gathering systems lacked sufficient personalization capabilities to effectively provide information based on users' interests and preferences. Furthermore, the use of location information within physical stores was limited, making it difficult for users to obtain the most relevant information upon arrival. This often resulted in a limited user experience.

[0089] 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.

[0090] In this invention, the server includes a natural language processing means for analyzing information requests from users, an information gathering means for collecting relevant information from external sources based on the analyzed requests, an information processing means for comparing the collected information with the user's historical data and selecting and processing appropriate information, a presentation means for presenting the selected and processed information to the user and providing detailed information about products or services based on additional requests, and a location information analysis means for providing in-store information using local location information. This enables users to receive optimal information based on their personal tastes and interests in real time within the store.

[0091] "Natural language processing means" refers to technologies for understanding information requests entered by users and analyzing their intent.

[0092] "Information gathering means" refers to the means of efficiently obtaining relevant information from external sources based on analyzed requirements.

[0093] "Information processing means" refers to a means of comparing collected information with the user's past history data, selecting information suitable for the user, and processing it.

[0094] "Presentation methods" refer to means of providing selected and processed information to users in an easy-to-view format, and of presenting detailed information about products and services upon request.

[0095] "Location information analysis means" refers to technology that determines the user's current location and provides appropriate geographical information and services based on that location information.

[0096] A "reservation method" refers to a means of automatically executing reservation and purchase procedures for events specified by the user, in conjunction with external services.

[0097] A "database management system" is a management function that builds preference data collected through interaction with users and continuously updates it to provide information tailored to each individual user.

[0098] To realize this invention, the system consists of a program that integrates multiple technologies. The server interprets information requests from users using natural language processing algorithms. Natural language processing libraries such as Google's TENSORFLOW and Hugging Face's Transformers are used. Based on the analysis results, the server utilizes external information gathering channels (such as APIs and web crawlers) to collect information that meets the conditions specified by the user.

[0099] Subsequently, the information collected by the information processing system is compared with the user's historical data. Amazon Web Services (AWS®) DynamoDB is used as the database system here. Based on the user data, a process is carried out to select and process personalized information. In particular, data matching and filtering are used to select information that best matches the user's interests.

[0100] On the device, selected information is displayed while also considering the user's location. Location analysis combines the smartphone's built-in GPS and BLE beacon technology. This allows users to obtain relevant information in real time when visiting physical stores. Furthermore, support is provided for users to quickly register for events or purchase products through reservation methods.

[0101] As a concrete example, when a user scans product A with a smartphone app in a store, special event information and promotions related to that product are immediately displayed. In this process, by utilizing a generative AI model and employing prompts such as "Tell me what recommended information should be displayed while I'm in the store," rapid and accurate information provision is achieved.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The user enters the information request using an app installed on their smartphone. The entered request is temporarily stored on the device as text data and prepared to be sent to the server. In this step, the input is the user's information request, and the output is the request data sent to the server.

[0105] Step 2:

[0106] The server analyzes the received text data using natural language processing techniques. In this step, it understands the intent of the input request and identifies relevant information based on extracted keywords and elements. The technology used is a natural language processing library, and the output is a search query tailored to the user's request.

[0107] Step 3:

[0108] The server collects relevant data using external information gathering methods based on the analysis results. It obtains specified event and product information via API or web scraping. The input is a search query, and the output is a dataset of event details and product information. Within this process, it specifically accesses the selected information sources and collects the data.

[0109] Step 4:

[0110] The collected information data is cross-referenced with user history data by information processing tools on the server. User data stored in Amazon Web Services' DynamoDB is referenced, and highly relevant information is selected. The input for this step is an information dataset and a user database, and the output is a set of personalized information.

[0111] Step 5:

[0112] The device displays personalized information received from the server, linked to the user's location information. In this process, location information is obtained from the smartphone's GPS function or BLE beacons, providing information relevant to the user's specific location. Input consists of user location data and a set of personalized information, while output is the information displayed on the user interface.

[0113] Step 6:

[0114] Based on the information provided, the user makes further reservation or purchase requests through the terminal. Requests received from the terminal are sent to an external server via the reservation system, where ticket reservations or product reservations are made. The input in this step is a reservation request, and the output is a reservation confirmation notification. During this process, data communication between the server and the external service is specifically performed.

[0115] 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.

[0116] The present invention is implemented as a system that incorporates an emotion engine for recognizing user emotions into a conventional information provision system. This system consists of a user terminal, a server, an internal database, and the emotion engine.

[0117] The user terminal provides an interface for users to input information they are interested in and to engage in conversations. If a user inputs a message that includes an emotion, such as "I'm disappointed I couldn't go to the music event recently," the terminal sends that data to the server.

[0118] The server analyzes the received data using natural language processing and an emotion engine. The natural language processing analyzes the message content, and the emotion engine identifies the user's emotions and records them in a database. For example, if a message expressing disappointment is recognized, "sadness" will be recorded as the user's recent emotion.

[0119] The analyzed information is used to acquire event information from relevant external sources using information gathering methods. During this process, information relevant to the user's emotions is prioritized and selected based on the analysis results of the emotion engine. This process allows for the provision of event information that is highly likely to evoke feelings of joy and excitement in the user.

[0120] The acquired information is processed within the server using data processing tools and then cross-referenced with the user's individual history database along with sentiment data. This enables more personalized suggestions.

[0121] The selected and processed information is then sent to the user's terminal via a presentation device. For example, if a user is feeling emotionally exhausted, the system can suggest relaxing events or activities that are ideal for changing their mood.

[0122] Furthermore, if a user expresses interest in a particular event, the server utilizes reservation mechanisms to execute ticket reservations for the event through integration with external services. Through this series of processes, the present invention can realize flexible information provision and experiences based on the user's emotions and interests, thereby improving user satisfaction.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] A user enters "I'm disappointed I haven't been able to go to any music events lately" into their device. The device then sends this input to a server. It is assumed that this input will also include data related to the user's emotions.

[0126] Step 2:

[0127] The server analyzes the received data using natural language processing to understand the message content and the request regarding the event. It also uses an emotion engine to recognize the user's emotional state, in this case, "sadness."

[0128] Step 3:

[0129] Based on the analysis results, the server uses information gathering tools to access external music event information providers and retrieve relevant event information. Here, priority is given to collecting information on enjoyable events that might alleviate the user's "sadness," as well as information on artists of interest.

[0130] Step 4:

[0131] The acquired event information is processed within the server using information processing tools. This information is then compared with the user's history data to select information that is likely to be of particular interest. Sentimental data is also considered at this stage.

[0132] Step 5:

[0133] The server formats the selected information into a user-friendly format and transmits it to the user's terminal via a presentation device.

[0134] Step 6:

[0135] The user terminal displays the received information on the screen and guides the user in a way that captures their interest. It provides an interface that allows users to view event details and prompts them to take booking action as needed.

[0136] Step 7:

[0137] When a user expresses interest in an event and makes an additional request such as "I want to reserve tickets," the device sends that request to the server.

[0138] Step 8:

[0139] The server uses a reservation mechanism to coordinate with an external reservation service, check the availability of tickets for the specified event, and make a reservation. The process is completed by notifying the user's terminal of the reservation result.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] While modern information systems can provide information in response to user requests, they are not adequately capable of providing more personalized and appropriate information based on individual user emotions. Furthermore, there are challenges in flexibly selecting events and facilitating booking procedures that align with user sentiment.

[0143] 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.

[0144] In this invention, the server includes data analysis means for analyzing information requests from users, information gathering means for collecting relevant knowledge from external sources based on the analyzed requests and the user's emotional state, information processing means for comparing the collected knowledge with the user's historical data and processing it to make personalized suggestions, output means for presenting the selected and processed knowledge to the user, and emotion analysis means for identifying the user's emotional state and preferentially selecting event information appropriate to that emotion. This enables flexible and highly personalized information provision based on the user's emotions.

[0145] "Data analysis means" refers to technologies for analyzing information requests provided by users and understanding their content.

[0146] "Information gathering methods" refer to techniques for acquiring relevant knowledge from external sources based on analyzed information needs and emotional states.

[0147] "Information processing means" refers to technology that compares collected knowledge with the user's historical data, processes it as personalized information, and makes suggestions.

[0148] "Output method" refers to a method for presenting selected and processed knowledge to the user.

[0149] "Emotional analysis means" refers to a technology that identifies a user's emotional state during the process of analyzing information requests from users and selects information appropriate to that emotion.

[0150] This invention is a system that provides information while taking user emotions into consideration, and is realized through the following configuration and operation. The main components include a user terminal, a server, an emotion analysis engine, and a database.

[0151] The user terminal provides an interface for receiving messages from the user that include information requests and emotions. This interface is designed to support various input methods, such as voice input, text input, and touch operation. When a user enters a message that includes emotions, such as "I'm disappointed I couldn't go to the music event recently," the user terminal immediately sends that information to the server.

[0152] The server uses a natural language processing framework to analyze the received message. Specifically, an open-source natural language processing library is used. The server then uses a sentiment analysis engine to identify the emotions within the message. Through this process, it determines whether the user is expressing emotions such as "sadness" or "joy."

[0153] Based on the analyzed information, the server activates an information gathering module to acquire relevant knowledge from external data sources. Publicly available APIs on the internet may be used for data collection. The system is designed to prioritize event information that aligns with the user's emotions.

[0154] The acquired information is processed within the server using information processing technology. The processed information is then compared with the user's history database to generate personalized suggestions based on the user's past behavior and preferences.

[0155] Finally, the selected information is sent to the user's device and displayed on the screen. This allows the user to obtain information about events and activities that match their emotions.

[0156] As a concrete example, here is an example of a prompt: "Suggest ways for the user to spend their holidays in a way they will enjoy. However, their recent emotional state is recorded as 'fatigued'."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] Users input emotionally charged messages using their devices. The message input interface allows users to deliver messages via voice or text.

[0160] Input: A message that includes the user's emotions (e.g., "I'm sad I haven't been able to go to music events lately")

[0161] Specifically, the terminal sends this message to the server in the specified format.

[0162] Step 2:

[0163] The server receives the message and parses its content using a natural language processing framework.

[0164] Input: Message sent by the user

[0165] Data processing: Perform grammatical analysis and keyword extraction.

[0166] Output: Analyzed message content (e.g., hobby "music", emotion "disappointed")

[0167] In concrete terms, the server analyzes the grammatical structure and identifies important words and phrases.

[0168] Step 3:

[0169] The server uses an emotion analysis engine to identify the emotional components of the message.

[0170] Input: Analyzed message content

[0171] Data processing: Identify emotional states and classify them into emotional categories such as "sadness."

[0172] Output: User's emotional state (e.g., "sadness")

[0173] Specifically, the server executes the emotion engine's algorithm and generates emotion vectors.

[0174] Step 4:

[0175] The server activates the information gathering module and collects relevant information from external sources.

[0176] Input: Data on the user's emotional state and interests

[0177] Data processing: Prioritize collecting event information that is relevant to the emotions being expressed.

[0178] Output: Related information list (e.g., list of music events)

[0179] Specifically, the server calls an external API to retrieve event information from the database.

[0180] Step 5:

[0181] The server processes the collected information using information processing technology and compares it with the user's history data.

[0182] Input: Related information list, user history database

[0183] Data processing: Perform matching filtering to generate personalized suggestions.

[0184] Output: Personalized event suggestions (e.g., specific music events)

[0185] Specifically, the server executes a filtering algorithm to select events that match past user behavior.

[0186] Step 6:

[0187] The server sends the selected information to the user's terminal and displays it on the screen.

[0188] Input: Personalized event suggestions

[0189] Output: Event information presented to the user

[0190] Specifically, the device displays a notification, allowing the user to view the information.

[0191] (Application Example 2)

[0192] 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".

[0193] Modern information delivery systems often lack sufficient personalization that takes user emotions into account, making it difficult to immediately provide users with specific and appropriate information and content they seek. Furthermore, the inefficiency of emotion-based information recommendations and event bookings makes improving user satisfaction a challenge.

[0194] 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.

[0195] In this invention, the server includes natural language processing means for analyzing information requests from users and recognizing emotions; information gathering means for collecting relevant information from external sources based on the analyzed requests and emotions; and information processing means for comparing the collected information with the user's history database and selecting and processing appropriate information according to the user's emotions. This makes it possible to provide specific and attractive information and suggest content based on the user's emotions.

[0196] "Natural language processing means" refers to technologies that analyze text data entered by users and understand its content and sentiment.

[0197] "Information gathering means" refers to a mechanism for obtaining relevant information from external sources based on analyzed user requests and emotions.

[0198] "Information processing means" refers to the process of comparing collected information with the user's history database and selecting and processing necessary information according to the user's emotions.

[0199] "Presentation means" refers to an interface or mechanism for appropriately displaying selected and processed information to the user.

[0200] "Emotion" refers to elements that identify mental states such as joy, sadness, and stress by analyzing the user's emotional state expressed in their text input.

[0201] A "user history database" refers to a collection of information that accumulates past user behavior, preferences, and emotional data to support the provision of personalized services.

[0202] "External information sources" refer to sources of information that may exist outside the device, such as publicly available data on the internet or commercial databases.

[0203] This invention aims to construct an information provision and content suggestion system that takes user emotions into consideration. Implementation will utilize user terminals, servers, and the network environment connecting them.

[0204] The user terminal provides an interface for receiving text input that reflects the user's emotions. Through this interface, the user can input their emotions and information requests in natural language.

[0205] The server first receives data sent by the user and analyzes it using natural language processing techniques. This analysis process utilizes natural language processing libraries such as Hugging Face Transformers and the Google Cloud Natural Language API. Using these technologies, the server identifies the user's emotions and generates and records emotion data.

[0206] Based on the analyzed emotions and data, the server utilizes information gathering tools to acquire relevant videos and content from external sources. The collected data is then cross-referenced with the user's history database and processed by information processing tools to select and create the most relevant content.

[0207] The selected information is presented to the user. Furthermore, if the user shows interest in specific content, the system can integrate with external services to make reservations or recommendations. For example, if a user enters "I've been feeling stressed lately," the system can suggest relaxing videos and music. An example of a prompt to the generating AI model in this case would be, "The user is currently feeling 'stressed.' Please suggest three relaxing content options."

[0208] This system allows users to receive personalized information and content that responds to their emotions, leading to increased satisfaction.

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

[0210] Step 1:

[0211] The user terminal receives input from the user. The user inputs emotions and information requests in natural language. The entered text data is sent from the terminal to the server. At this stage, the input data may contain specific emotions or requests.

[0212] Step 2:

[0213] The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Hugging Face Transformers library to analyze the text content and the Google Cloud Natural Language API to determine emotions. Text data is provided as input, and emotion data and request data are generated as output. The server records this information in an internal database.

[0214] Step 3:

[0215] Based on the analyzed requests and sentiments, the server uses information gathering tools to acquire relevant information from external sources. It sends prompt sentences to a generating AI model to obtain a list of suitable content candidates. The input consists of sentiment data and request data, and the output is a list of relevant content candidates. This process includes specific actions such as accessing external APIs via the internet.

[0216] Step 4:

[0217] The server retrieves potential content and compares it with the user's history database, then uses information processing tools to select and process the content. By referring to the user's history in the database, the most appropriate content is selected as output. This process involves specific actions that predict user preferences using machine learning algorithms.

[0218] Step 5:

[0219] The server presents selected information to the user's terminal. This information includes personalized content tailored to the user's current mood. The output information is displayed on the terminal in a format easily accessible to the user.

[0220] Step 6:

[0221] The server collaborates with external services to reserve or recommend content that the user has shown interest in. If necessary, it can trigger calls to external service APIs based on user actions, such as ticket reservations. Input includes the user's selected actions, and output includes reservation confirmation information.

[0222] Throughout this entire process, users can receive emotion-responsive information in real time and be prompted to take relevant actions.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is implemented as a system that efficiently collects information related to users' hobbies and fan activities and provides it to users in an appropriate format. This system consists of a user terminal, a server, and an internal database, and functions as follows.

[0240] The user terminal provides an interface for users to input information they are interested in. If a user inputs "Tell me about idol events next week," the request is initially parsed within the terminal and sent to the server as data.

[0241] The server analyzes received user requests using natural language processing to understand the user's intent. Based on the analysis results, information gathering tools access external event information services and related websites to collect relevant data. For example, it might retrieve information about idol events held within a specified period.

[0242] The acquired information is compared with the user's past history data by information processing tools on the server, and information that matches the user's interests is selected and processed. This history data is an accumulation of information on events and artists that the user has shown interest in in the past, and forms the basis for providing personalized suggestions.

[0243] The selected information is sent from the server to the user's terminal and presented to the user. This information is displayed in a format tailored to the user's interests, allowing them to view details and request ticket reservations for events that interest them.

[0244] For example, if a user becomes interested in a particular idol's live performance from the event information presented, the user's device sends an additional request to the server, and the reservation process begins. The server collaborates with external services to check ticket availability and completes the reservation if necessary. Through this process, the user can participate in events they are interested in with minimal effort.

[0245] In this way, the present invention can smoothly support users' hobbies and fan activities, and provide them with a highly satisfying experience.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The user enters "Tell me about next week's idol event information" into their terminal. The user's terminal performs an initial analysis of the input and sends the analyzed data to the server.

[0249] Step 2:

[0250] The server analyzes the received request data in detail using natural language processing techniques. As a result of the analysis, attribute information such as event type, date, and region is extracted.

[0251] Step 3:

[0252] The server uses information gathering methods based on the analysis results to obtain relevant event information from external event information providers. It collects idle event data for a specified period using an external API or web crawler.

[0253] Step 4:

[0254] The collected event information is evaluated within the server using information processing tools. By referring to the user's history database and considering information about past events attended and idols they have shown interest in, the most relevant information is selected.

[0255] Step 5:

[0256] The server formats the selected event information and transmits it to the user's terminal via a presentation device in order to provide it to the user.

[0257] Step 6:

[0258] The user terminal displays received event information and provides an interface that prompts for further information and subsequent actions (e.g., ticket booking). Users can review the details here and, if necessary, request a booking.

[0259] Step 7:

[0260] When a reservation request is received, the server uses the reservation mechanism to connect with an external reservation service, check ticket availability, and complete the reservation for the user. The process is completed when the reservation result is notified to the user's device.

[0261] (Example 1)

[0262] 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."

[0263] Conventional information systems have struggled to efficiently collect event information based on user interests and present it in an optimal format. Furthermore, users had to manually research and make reservations when they showed interest in a particular event, which was time-consuming. Moreover, the systems were not adequately equipped to dynamically understand user interests and provide personalized information.

[0264] 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.

[0265] In this invention, the server includes a natural language processing means that analyzes information requests from users and extracts keywords as necessary; an information gathering means that converts the analyzed requests into prompt sentences, accesses external information sources, and collects relevant information; and an information processing means that compares the collected information with the user's past history data, selects information considering the user's interests, and visualizes it. This enables the effective collection of event information based on the user's interests, provides it in a visually easy-to-understand format, and allows for the automated execution of reservation procedures.

[0266] "Natural language processing means" refers to a program or technology that analyzes information requests received from users to understand keywords and intent.

[0267] "Information gathering means" refers to a program or technology that accesses external information sources and obtains relevant data based on analyzed information requests.

[0268] "Information processing means" refers to a program or technology for comparing collected data with the user's past history data and selecting and processing it to match the user's interests.

[0269] "Presentation means" refers to a program or interface for displaying selected and processed information to the user in an easily understandable manner.

[0270] A "reservation method" is a program or technology that automatically handles reservation and purchase procedures for events that users are interested in, by linking with external reservation services.

[0271] A "database management means" is a program or technology for dynamically collecting user preferences through interaction with users and for building and updating a database.

[0272] This invention is implemented as a system for efficiently collecting and presenting information based on user interests. The system consists of a user terminal, a server, and a database.

[0273] The user terminal provides an interface for users to input event information of interest. Specifically, it includes text input fields and category selection options. This terminal performs basic parsing to send the received input as data to the server.

[0274] The server uses natural language processing (NLP) to analyze the information request received from the user. Here, natural language processing libraries (e.g., spaCy and the GPT model) are used to understand the user's intent. At this stage, the user's input is converted into a prompt. For example, a prompt such as "Please retrieve information about the idol live concert being held next week and inform the user." is generated.

[0275] Next, the server uses information gathering tools to collect necessary data from external sources. This data is typically obtained via APIs and received in JSON format. The retrieved information is then compared with the user's past history data by information processing tools within the server, and selected and processed to reflect the user's interests.

[0276] Finally, the selected information is sent from the server to the user's terminal, which then presents the information to the user in an intuitively viewable format. For example, list format or calendar display may be used.

[0277] This system allows users to efficiently obtain information on events of interest, view details, and make reservations with minimal effort. Furthermore, it provides personalized information based on user preferences, resulting in a highly satisfying user experience.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user inputs event information of interest to their terminal. The terminal receives the user's request via a text input field, performs a basic analysis of the entered text, and extracts key keywords. It then structures this extracted data and sends it to the server. In this step, the input is the user's text data, and the output is structured data containing the extracted keywords.

[0281] Step 2:

[0282] The server analyzes the received data in detail using natural language processing means. Using a generative AI model, a prompt sentence is generated to understand the user's intention. This prompt sentence is used as a data collection request to an external information source. The input of this step is the user's structured data, and the output is the generated prompt sentence.

[0283] Step 3:

[0284] The server accesses an external information source using information collection means and obtains relevant information based on the prompt sentence. At this stage, information is collected from an external event database through a RESTful API. The collected information is imported into the server in JSON format. The input of this step is the prompt sentence, and the output is the raw data obtained from the external information source.

[0285] Step 4:

[0286] The server compares the collected information with the user's past history data. Using information processing means, data is selected based on the user's interests and preferences and processed into a visually understandable form. In this process, data filtering and ranking are performed to identify the most relevant event information. The input of this step is the raw data and the user history data, and the output is the selected and processed event information.

[0287] Step 5:

[0288] The server sends the selected information to the user terminal. The terminal displays the received information on the interface so that the user can view the details. A method that the user can intuitively operate, such as a list format or a calendar display, is used. The input of this step is the selected and processed information, and the output is the visual display provided to the user.

[0289] Step 6:

[0290] If a user is interested in a displayed event and wishes to make a reservation, their device sends an additional request to the server. The server then interacts with an external reservation system to check ticket availability and complete the reservation process if necessary. The input for this step is the user's additional request, and the output is reservation confirmation information.

[0291] (Application Example 1)

[0292] 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."

[0293] Traditional information gathering systems lacked sufficient personalization capabilities to effectively provide information based on users' interests and preferences. Furthermore, the use of location information within physical stores was limited, making it difficult for users to obtain the most relevant information upon arrival. This often resulted in a limited user experience.

[0294] 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.

[0295] In this invention, the server includes a natural language processing means for analyzing information requests from users, an information gathering means for collecting relevant information from external sources based on the analyzed requests, an information processing means for comparing the collected information with the user's historical data and selecting and processing appropriate information, a presentation means for presenting the selected and processed information to the user and providing detailed information about products or services based on additional requests, and a location information analysis means for providing in-store information using local location information. This enables users to receive optimal information based on their personal tastes and interests in real time within the store.

[0296] "Natural language processing means" refers to technologies for understanding information requests entered by users and analyzing their intent.

[0297] "Information gathering means" refers to the means of efficiently obtaining relevant information from external sources based on analyzed requirements.

[0298] "Information processing means" refers to a means of comparing collected information with the user's past history data, selecting information suitable for the user, and processing it.

[0299] "Presentation methods" refer to means of providing selected and processed information to users in an easy-to-view format, and of presenting detailed information about products and services upon request.

[0300] "Location information analysis means" refers to technology that determines the user's current location and provides appropriate geographical information and services based on that location information.

[0301] A "reservation method" refers to a means of automatically executing reservation and purchase procedures for events specified by the user, in conjunction with external services.

[0302] A "database management system" is a management function that builds preference data collected through interaction with users and continuously updates it to provide information tailored to each individual user.

[0303] To realize this invention, the system consists of a program that integrates multiple technologies. The server interprets information requests from users using natural language processing algorithms. Natural language processing libraries such as Google's TensorFlow and Hugging Face's Transformers are used. Based on the analysis results, the server utilizes external information gathering channels (such as APIs and web crawlers) to collect information that meets the conditions specified by the user.

[0304] After that, the information processing means compares the information collected with the user's history data. Here, Amazon Web Services (AWS)'s DynamoDB is used as the database system. Based on the user data, a process of selecting and processing personalized information is carried out. In particular, the information most suitable for the user's interests is selected through data collation and filtering.

[0305] On the terminal, the selected information is displayed while also considering the user's location information. For location information analysis, the built-in GPS of the smartphone and BLE beacon technology are combined. As a result, the user can obtain relevant information in real time when visiting a physical store. Furthermore, through the reservation means, support is also provided for the user to quickly register for events and purchase products.

[0306] As a specific example, when the user scans product A at a store using the smartphone app, special event information and promotions related to that product are immediately displayed. At this time, by utilizing the generated AI model and leveraging prompt texts such as "Tell me the recommended information to be displayed during the store visit", rapid and accurate information provision is achieved.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The user inputs an information request using the app installed on the smartphone terminal. The input request is temporarily stored in the terminal as text data and is prepared to be sent to the server. The input of this step is the user's information request, and the output is the request data to the server.

[0310] Step 2:

[0311] The server analyzes the received text data using natural language processing techniques. In this step, it understands the intent of the input request and identifies relevant information based on extracted keywords and elements. The technology used is a natural language processing library, and the output is a search query tailored to the user's request.

[0312] Step 3:

[0313] The server collects relevant data using external information gathering methods based on the analysis results. It obtains specified event and product information via API or web scraping. The input is a search query, and the output is a dataset of event details and product information. Within this process, it specifically accesses the selected information sources and collects the data.

[0314] Step 4:

[0315] The collected information data is cross-referenced with user history data by information processing tools on the server. User data stored in Amazon Web Services' DynamoDB is referenced, and highly relevant information is selected. The input for this step is an information dataset and a user database, and the output is a set of personalized information.

[0316] Step 5:

[0317] The device displays personalized information received from the server, linked to the user's location information. In this process, location information is obtained from the smartphone's GPS function or BLE beacons, providing information relevant to the user's specific location. Input consists of user location data and a set of personalized information, while output is the information displayed on the user interface.

[0318] Step 6:

[0319] Based on the information provided, the user makes further reservation or purchase requests through the terminal. Requests received from the terminal are sent to an external server via the reservation system, where ticket reservations or product reservations are made. The input in this step is a reservation request, and the output is a reservation confirmation notification. During this process, data communication between the server and the external service is specifically performed.

[0320] 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.

[0321] The present invention is implemented as a system that incorporates an emotion engine for recognizing user emotions into a conventional information provision system. This system consists of a user terminal, a server, an internal database, and the emotion engine.

[0322] The user terminal provides an interface for users to input information they are interested in and to engage in conversations. If a user inputs a message that includes an emotion, such as "I'm disappointed I couldn't go to the music event recently," the terminal sends that data to the server.

[0323] The server analyzes the received data using natural language processing and an emotion engine. The natural language processing analyzes the message content, and the emotion engine identifies the user's emotions and records them in a database. For example, if a message expressing disappointment is recognized, "sadness" will be recorded as the user's recent emotion.

[0324] The analyzed information is used to acquire event information from relevant external sources using information gathering methods. During this process, information relevant to the user's emotions is prioritized and selected based on the analysis results of the emotion engine. This process allows for the provision of event information that is highly likely to evoke feelings of joy and excitement in the user.

[0325] The acquired information is processed within the server using data processing tools and then cross-referenced with the user's individual history database along with sentiment data. This enables more personalized suggestions.

[0326] The selected and processed information is then sent to the user's terminal via a presentation device. For example, if a user is feeling emotionally exhausted, the system can suggest relaxing events or activities that are ideal for changing their mood.

[0327] Furthermore, if a user expresses interest in a particular event, the server utilizes reservation mechanisms to execute ticket reservations for the event through integration with external services. Through this series of processes, the present invention can realize flexible information provision and experiences based on the user's emotions and interests, thereby improving user satisfaction.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] A user enters "I'm disappointed I haven't been able to go to any music events lately" into their device. The device then sends this input to a server. It is assumed that this input will also include data related to the user's emotions.

[0331] Step 2:

[0332] The server analyzes the received data using natural language processing to understand the message content and the request regarding the event. It also uses an emotion engine to recognize the user's emotional state, in this case, "sadness."

[0333] Step 3:

[0334] Based on the analysis results, the server uses information gathering tools to access external music event information providers and retrieve relevant event information. Here, priority is given to collecting information on enjoyable events that might alleviate the user's "sadness," as well as information on artists of interest.

[0335] Step 4:

[0336] The acquired event information is processed within the server using information processing tools. This information is then compared with the user's history data to select information that is likely to be of particular interest. Sentimental data is also considered at this stage.

[0337] Step 5:

[0338] The server formats the selected information into a user-friendly format and transmits it to the user's terminal via a presentation device.

[0339] Step 6:

[0340] The user terminal displays the received information on the screen and guides the user in a way that captures their interest. It provides an interface that allows users to view event details and prompts them to take booking action as needed.

[0341] Step 7:

[0342] When a user expresses interest in an event and makes an additional request such as "I want to reserve tickets," the device sends that request to the server.

[0343] Step 8:

[0344] The server uses a reservation mechanism to coordinate with an external reservation service, check the availability of tickets for the specified event, and make a reservation. The process is completed by notifying the user's terminal of the reservation result.

[0345] (Example 2)

[0346] 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".

[0347] While modern information systems can provide information in response to user requests, they are not adequately capable of providing more personalized and appropriate information based on individual user emotions. Furthermore, there are challenges in flexibly selecting events and facilitating booking procedures that align with user sentiment.

[0348] 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.

[0349] In this invention, the server includes data analysis means for analyzing information requests from users, information gathering means for collecting relevant knowledge from external sources based on the analyzed requests and the user's emotional state, information processing means for comparing the collected knowledge with the user's historical data and processing it to make personalized suggestions, output means for presenting the selected and processed knowledge to the user, and emotion analysis means for identifying the user's emotional state and preferentially selecting event information appropriate to that emotion. This enables flexible and highly personalized information provision based on the user's emotions.

[0350] "Data analysis means" refers to technologies for analyzing information requests provided by users and understanding their content.

[0351] "Information gathering methods" refer to techniques for acquiring relevant knowledge from external sources based on analyzed information needs and emotional states.

[0352] "Information processing means" refers to technology that compares collected knowledge with the user's historical data, processes it as personalized information, and makes suggestions.

[0353] "Output method" refers to a method for presenting selected and processed knowledge to the user.

[0354] "Emotional analysis means" refers to a technology that identifies a user's emotional state during the process of analyzing information requests from users and selects information appropriate to that emotion.

[0355] This invention is a system that provides information while taking user emotions into consideration, and is realized through the following configuration and operation. The main components include a user terminal, a server, an emotion analysis engine, and a database.

[0356] The user terminal provides an interface for receiving messages from the user that include information requests and emotions. This interface is designed to support various input methods, such as voice input, text input, and touch operation. When a user enters a message that includes emotions, such as "I'm disappointed I couldn't go to the music event recently," the user terminal immediately sends that information to the server.

[0357] The server uses a natural language processing framework to analyze the received message. Specifically, an open-source natural language processing library is used. The server then uses a sentiment analysis engine to identify the emotions within the message. Through this process, it determines whether the user is expressing emotions such as "sadness" or "joy."

[0358] Based on the analyzed information, the server activates an information gathering module to acquire relevant knowledge from external data sources. Publicly available APIs on the internet may be used for data collection. The system is designed to prioritize event information that aligns with the user's emotions.

[0359] The acquired information is processed within the server using information processing technology. The processed information is then compared with the user's history database to generate personalized suggestions based on the user's past behavior and preferences.

[0360] Finally, the selected information is sent to the user's device and displayed on the screen. This allows the user to obtain information about events and activities that match their emotions.

[0361] As a concrete example, here is an example of a prompt: "Suggest ways for the user to spend their holidays in a way they will enjoy. However, their recent emotional state is recorded as 'fatigued'."

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] Users input emotionally charged messages using their devices. The message input interface allows users to deliver messages via voice or text.

[0365] Input: A message that includes the user's emotions (e.g., "I'm sad I haven't been able to go to music events lately")

[0366] Specifically, the terminal sends this message to the server in the specified format.

[0367] Step 2:

[0368] The server receives the message and parses its content using a natural language processing framework.

[0369] Input: Message sent by the user

[0370] Data processing: Perform grammatical analysis and keyword extraction.

[0371] Output: Analyzed message content (e.g., hobby "music", emotion "disappointed")

[0372] In concrete terms, the server analyzes the grammatical structure and identifies important words and phrases.

[0373] Step 3:

[0374] The server uses an emotion analysis engine to identify the emotional components of the message.

[0375] Input: Analyzed message content

[0376] Data processing: Identify emotional states and classify them into emotional categories such as "sadness."

[0377] Output: User's emotional state (e.g., "sadness")

[0378] Specifically, the server executes the emotion engine's algorithm and generates emotion vectors.

[0379] Step 4:

[0380] The server activates the information gathering module and collects relevant information from external sources.

[0381] Input: Data on the user's emotional state and interests

[0382] Data processing: Prioritize collecting event information that is relevant to the emotions being expressed.

[0383] Output: Related information list (e.g., list of music events)

[0384] Specifically, the server calls an external API to retrieve event information from the database.

[0385] Step 5:

[0386] The server processes the collected information using information processing technology and compares it with the user's history data.

[0387] Input: Related information list, user history database

[0388] Data processing: Perform matching filtering to generate personalized suggestions.

[0389] Output: Personalized event suggestions (e.g., specific music events)

[0390] Specifically, the server executes a filtering algorithm to select events that match past user behavior.

[0391] Step 6:

[0392] The server sends the selected information to the user's terminal and displays it on the screen.

[0393] Input: Personalized event suggestions

[0394] Output: Event information presented to the user

[0395] Specifically, the device displays a notification, allowing the user to view the information.

[0396] (Application Example 2)

[0397] 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."

[0398] Modern information delivery systems often lack sufficient personalization that takes user emotions into account, making it difficult to immediately provide users with specific and appropriate information and content they seek. Furthermore, the inefficiency of emotion-based information recommendations and event bookings makes improving user satisfaction a challenge.

[0399] 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.

[0400] In this invention, the server includes natural language processing means for analyzing information requests from users and recognizing emotions; information gathering means for collecting relevant information from external sources based on the analyzed requests and emotions; and information processing means for comparing the collected information with the user's history database and selecting and processing appropriate information according to the user's emotions. This makes it possible to provide specific and attractive information and suggest content based on the user's emotions.

[0401] "Natural language processing means" refers to technologies that analyze text data entered by users and understand its content and sentiment.

[0402] "Information gathering means" refers to a mechanism for obtaining relevant information from external sources based on analyzed user requests and emotions.

[0403] "Information processing means" refers to the process of comparing collected information with the user's history database and selecting and processing necessary information according to the user's emotions.

[0404] "Presentation means" refers to an interface or mechanism for appropriately displaying selected and processed information to the user.

[0405] "Emotion" refers to elements that identify mental states such as joy, sadness, and stress by analyzing the user's emotional state expressed in their text input.

[0406] A "user history database" refers to a collection of information that accumulates past user behavior, preferences, and emotional data to support the provision of personalized services.

[0407] "External information sources" refer to sources of information that may exist outside the device, such as publicly available data on the internet or commercial databases.

[0408] This invention aims to construct an information provision and content suggestion system that takes user emotions into consideration. Implementation will utilize user terminals, servers, and the network environment connecting them.

[0409] The user terminal provides an interface for receiving text input that reflects the user's emotions. Through this interface, the user can input their emotions and information requests in natural language.

[0410] The server first receives data sent by the user and analyzes it using natural language processing techniques. This analysis process utilizes natural language processing libraries such as Hugging Face Transformers and the Google Cloud Natural Language API. Using these technologies, the server identifies the user's emotions and generates and records emotion data.

[0411] Based on the analyzed emotions and data, the server utilizes information gathering tools to acquire relevant videos and content from external sources. The collected data is then cross-referenced with the user's history database and processed by information processing tools to select and create the most relevant content.

[0412] The selected information is presented to the user. Furthermore, if the user shows interest in specific content, the system can integrate with external services to make reservations or recommendations. For example, if a user enters "I've been feeling stressed lately," the system can suggest relaxing videos and music. An example of a prompt to the generating AI model in this case would be, "The user is currently feeling 'stressed.' Please suggest three relaxing content options."

[0413] This system allows users to receive personalized information and content that responds to their emotions, leading to increased satisfaction.

[0414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0415] Step 1:

[0416] The user terminal receives input from the user. The user inputs emotions and information requests in natural language. The entered text data is sent from the terminal to the server. At this stage, the input data may contain specific emotions or requests.

[0417] Step 2:

[0418] The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Hugging Face Transformers library to analyze the text content and the Google Cloud Natural Language API to determine emotions. Text data is provided as input, and emotion data and request data are generated as output. The server records this information in an internal database.

[0419] Step 3:

[0420] Based on the analyzed requests and sentiments, the server uses information gathering tools to acquire relevant information from external sources. It sends prompt sentences to a generating AI model to obtain a list of suitable content candidates. The input consists of sentiment data and request data, and the output is a list of relevant content candidates. This process includes specific actions such as accessing external APIs via the internet.

[0421] Step 4:

[0422] The server retrieves potential content and compares it with the user's history database, then uses information processing tools to select and process the content. By referring to the user's history in the database, the most appropriate content is selected as output. This process involves specific actions that predict user preferences using machine learning algorithms.

[0423] Step 5:

[0424] The server presents selected information to the user's terminal. This information includes personalized content tailored to the user's current mood. The output information is displayed on the terminal in a format easily accessible to the user.

[0425] Step 6:

[0426] The server collaborates with external services to reserve or recommend content that the user has shown interest in. If necessary, it can trigger calls to external service APIs based on user actions, such as ticket reservations. Input includes the user's selected actions, and output includes reservation confirmation information.

[0427] Throughout this entire process, users can receive emotion-responsive information in real time and be prompted to take relevant actions.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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".

[0444] This invention is implemented as a system that efficiently collects information related to users' hobbies and fan activities and provides it to users in an appropriate format. This system consists of a user terminal, a server, and an internal database, and functions as follows.

[0445] The user terminal provides an interface for users to input information they are interested in. If a user inputs "Tell me about idol events next week," the request is initially parsed within the terminal and sent to the server as data.

[0446] The server analyzes received user requests using natural language processing to understand the user's intent. Based on the analysis results, information gathering tools access external event information services and related websites to collect relevant data. For example, it might retrieve information about idol events held within a specified period.

[0447] The acquired information is compared with the user's past history data by information processing tools on the server, and information that matches the user's interests is selected and processed. This history data is an accumulation of information on events and artists that the user has shown interest in in the past, and forms the basis for providing personalized suggestions.

[0448] The selected information is sent from the server to the user's terminal and presented to the user. This information is displayed in a format tailored to the user's interests, allowing them to view details and request ticket reservations for events that interest them.

[0449] For example, if a user becomes interested in a particular idol's live performance from the event information presented, the user's device sends an additional request to the server, and the reservation process begins. The server collaborates with external services to check ticket availability and completes the reservation if necessary. Through this process, the user can participate in events they are interested in with minimal effort.

[0450] In this way, the present invention can smoothly support users' hobbies and fan activities, and provide them with a highly satisfying experience.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The user enters "Tell me about next week's idol event information" into their terminal. The user's terminal performs an initial analysis of the input and sends the analyzed data to the server.

[0454] Step 2:

[0455] The server analyzes the received request data in detail using natural language processing techniques. As a result of the analysis, attribute information such as event type, date, and region is extracted.

[0456] Step 3:

[0457] The server uses information gathering methods based on the analysis results to obtain relevant event information from external event information providers. It collects idle event data for a specified period using an external API or web crawler.

[0458] Step 4:

[0459] The collected event information is evaluated within the server using information processing tools. By referring to the user's history database and considering information about past events attended and idols they have shown interest in, the most relevant information is selected.

[0460] Step 5:

[0461] The server formats the selected event information and transmits it to the user's terminal via a presentation device in order to provide it to the user.

[0462] Step 6:

[0463] The user terminal displays received event information and provides an interface that prompts for further information and subsequent actions (e.g., ticket booking). Users can review the details here and, if necessary, request a booking.

[0464] Step 7:

[0465] When a reservation request is received, the server uses the reservation mechanism to connect with an external reservation service, check ticket availability, and complete the reservation for the user. The process is completed when the reservation result is notified to the user's device.

[0466] (Example 1)

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

[0468] Conventional information systems have struggled to efficiently collect event information based on user interests and present it in an optimal format. Furthermore, users had to manually research and make reservations when they showed interest in a particular event, which was time-consuming. Moreover, the systems were not adequately equipped to dynamically understand user interests and provide personalized information.

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

[0470] In this invention, the server includes a natural language processing means that analyzes information requests from users and extracts keywords as necessary; an information gathering means that converts the analyzed requests into prompt sentences, accesses external information sources, and collects relevant information; and an information processing means that compares the collected information with the user's past history data, selects information considering the user's interests, and visualizes it. This enables the effective collection of event information based on the user's interests, provides it in a visually easy-to-understand format, and allows for the automated execution of reservation procedures.

[0471] "Natural language processing means" refers to a program or technology that analyzes information requests received from users to understand keywords and intent.

[0472] "Information gathering means" refers to a program or technology that accesses external information sources and obtains relevant data based on analyzed information requests.

[0473] "Information processing means" refers to a program or technology for comparing collected data with the user's past history data and selecting and processing it to match the user's interests.

[0474] "Presentation means" refers to a program or interface for displaying selected and processed information to the user in an easily understandable manner.

[0475] A "reservation method" is a program or technology that automatically handles reservation and purchase procedures for events that users are interested in, by linking with external reservation services.

[0476] A "database management means" is a program or technology for dynamically collecting user preferences through interaction with users and for building and updating a database.

[0477] This invention is implemented as a system for efficiently collecting and presenting information based on user interests. The system consists of a user terminal, a server, and a database.

[0478] The user terminal provides an interface for users to input event information of interest. Specifically, it includes text input fields and category selection options. This terminal performs basic parsing to send the received input as data to the server.

[0479] The server uses natural language processing (NLP) to analyze the information request received from the user. Here, natural language processing libraries (e.g., spaCy and the GPT model) are used to understand the user's intent. At this stage, the user's input is converted into a prompt. For example, a prompt such as "Please retrieve information about the idol live concert being held next week and inform the user." is generated.

[0480] Next, the server uses information gathering tools to collect necessary data from external sources. This data is typically obtained via APIs and received in JSON format. The retrieved information is then compared with the user's past history data by information processing tools within the server, and selected and processed to reflect the user's interests.

[0481] Finally, the selected information is sent from the server to the user's terminal, which then presents the information to the user in an intuitively viewable format. For example, list format or calendar display may be used.

[0482] This system allows users to efficiently obtain information on events of interest, view details, and make reservations with minimal effort. Furthermore, it provides personalized information based on user preferences, resulting in a highly satisfying user experience.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The user inputs event information of interest to their terminal. The terminal receives the user's request via a text input field, performs a basic analysis of the entered text, and extracts key keywords. It then structures this extracted data and sends it to the server. In this step, the input is the user's text data, and the output is structured data containing the extracted keywords.

[0486] Step 2:

[0487] The server analyzes the received data in detail using natural language processing techniques. Using a generative AI model, it generates prompts to understand the user's intent. These prompts are used as data collection requests to external sources. The input for this step is the user's structured data, and the output is the generated prompts.

[0488] Step 3:

[0489] The server accesses external information sources using information gathering tools and retrieves relevant information based on prompt messages. At this stage, information is collected from an external event database via a RESTful API. The collected information is imported into the server in JSON format. The input for this step is a prompt message, and the output is raw data retrieved from the external information source.

[0490] Step 4:

[0491] The server compares the collected information with the user's past history data. Using information processing tools, data is selected based on the user's interests and preferences and processed into a visually easy-to-understand format. This process involves filtering and ranking the data to identify the most relevant event information. The input for this step is raw data and user history data, and the output is selected and processed event information.

[0492] Step 5:

[0493] The server sends the selected information to the user's terminal. The terminal displays the received information on its interface, allowing the user to view the details. Methods that allow the user to operate intuitively, such as list format or calendar display, are used. The input in this step is the selected and processed information, and the output is the visual display provided to the user.

[0494] Step 6:

[0495] If a user is interested in a displayed event and wishes to make a reservation, their device sends an additional request to the server. The server then interacts with an external reservation system to check ticket availability and complete the reservation process if necessary. The input for this step is the user's additional request, and the output is reservation confirmation information.

[0496] (Application Example 1)

[0497] 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."

[0498] Traditional information gathering systems lacked sufficient personalization capabilities to effectively provide information based on users' interests and preferences. Furthermore, the use of location information within physical stores was limited, making it difficult for users to obtain the most relevant information upon arrival. This often resulted in a limited user experience.

[0499] 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.

[0500] In this invention, the server includes a natural language processing means for analyzing information requests from users, an information gathering means for collecting relevant information from external sources based on the analyzed requests, an information processing means for comparing the collected information with the user's historical data and selecting and processing appropriate information, a presentation means for presenting the selected and processed information to the user and providing detailed information about products or services based on additional requests, and a location information analysis means for providing in-store information using local location information. This enables users to receive optimal information based on their personal tastes and interests in real time within the store.

[0501] "Natural language processing means" refers to technologies for understanding information requests entered by users and analyzing their intent.

[0502] "Information gathering means" refers to the means of efficiently obtaining relevant information from external sources based on analyzed requirements.

[0503] "Information processing means" refers to a means of comparing collected information with the user's past history data, selecting information suitable for the user, and processing it.

[0504] "Presentation methods" refer to means of providing selected and processed information to users in an easy-to-view format, and of presenting detailed information about products and services upon request.

[0505] "Location information analysis means" refers to technology that determines the user's current location and provides appropriate geographical information and services based on that location information.

[0506] A "reservation method" refers to a means of automatically executing reservation and purchase procedures for events specified by the user, in conjunction with external services.

[0507] A "database management system" is a management function that builds preference data collected through interaction with users and continuously updates it to provide information tailored to each individual user.

[0508] To realize this invention, the system consists of a program that integrates multiple technologies. The server interprets information requests from users using natural language processing algorithms. Natural language processing libraries such as Google's TensorFlow and Hugging Face's Transformers are used. Based on the analysis results, the server utilizes external information gathering channels (such as APIs and web crawlers) to collect information that meets the conditions specified by the user.

[0509] Subsequently, the information collected by the information processing system is compared with the user's historical data. Amazon Web Services (AWS) DynamoDB is used as the database system here. Based on the user data, a process is carried out to select and process personalized information. In particular, data matching and filtering are used to select information that best matches the user's interests.

[0510] On the device, selected information is displayed while also considering the user's location. Location analysis combines the smartphone's built-in GPS and BLE beacon technology. This allows users to obtain relevant information in real time when visiting physical stores. Furthermore, support is provided for users to quickly register for events or purchase products through reservation methods.

[0511] As a concrete example, when a user scans product A with a smartphone app in a store, special event information and promotions related to that product are immediately displayed. In this process, by utilizing a generative AI model and employing prompts such as "Tell me what recommended information should be displayed while I'm in the store," rapid and accurate information provision is achieved.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The user enters the information request using an app installed on their smartphone. The entered request is temporarily stored on the device as text data and prepared to be sent to the server. In this step, the input is the user's information request, and the output is the request data sent to the server.

[0515] Step 2:

[0516] The server analyzes the received text data using natural language processing techniques. In this step, it understands the intent of the input request and identifies relevant information based on extracted keywords and elements. The technology used is a natural language processing library, and the output is a search query tailored to the user's request.

[0517] Step 3:

[0518] The server collects relevant data using external information gathering methods based on the analysis results. It obtains specified event and product information via API or web scraping. The input is a search query, and the output is a dataset of event details and product information. Within this process, it specifically accesses the selected information sources and collects the data.

[0519] Step 4:

[0520] The collected information data is cross-referenced with user history data by information processing tools on the server. User data stored in Amazon Web Services' DynamoDB is referenced, and highly relevant information is selected. The input for this step is an information dataset and a user database, and the output is a set of personalized information.

[0521] Step 5:

[0522] The device displays personalized information received from the server, linked to the user's location information. In this process, location information is obtained from the smartphone's GPS function or BLE beacons, providing information relevant to the user's specific location. Input consists of user location data and a set of personalized information, while output is the information displayed on the user interface.

[0523] Step 6:

[0524] Based on the information provided, the user makes further reservation or purchase requests through the terminal. Requests received from the terminal are sent to an external server via the reservation system, where ticket reservations or product reservations are made. The input in this step is a reservation request, and the output is a reservation confirmation notification. During this process, data communication between the server and the external service is specifically performed.

[0525] 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.

[0526] The present invention is implemented as a system that incorporates an emotion engine for recognizing user emotions into a conventional information provision system. This system consists of a user terminal, a server, an internal database, and the emotion engine.

[0527] The user terminal provides an interface for users to input information they are interested in and to engage in conversations. If a user inputs a message that includes an emotion, such as "I'm disappointed I couldn't go to the music event recently," the terminal sends that data to the server.

[0528] The server analyzes the received data using natural language processing and an emotion engine. The natural language processing analyzes the message content, and the emotion engine identifies the user's emotions and records them in a database. For example, if a message expressing disappointment is recognized, "sadness" will be recorded as the user's recent emotion.

[0529] The analyzed information is used to acquire event information from relevant external sources using information gathering methods. During this process, information relevant to the user's emotions is prioritized and selected based on the analysis results of the emotion engine. This process allows for the provision of event information that is highly likely to evoke feelings of joy and excitement in the user.

[0530] The acquired information is processed within the server using data processing tools and then cross-referenced with the user's individual history database along with sentiment data. This enables more personalized suggestions.

[0531] The selected and processed information is then sent to the user's terminal via a presentation device. For example, if a user is feeling emotionally exhausted, the system can suggest relaxing events or activities that are ideal for changing their mood.

[0532] Furthermore, if a user expresses interest in a particular event, the server utilizes reservation mechanisms to execute ticket reservations for the event through integration with external services. Through this series of processes, the present invention can realize flexible information provision and experiences based on the user's emotions and interests, thereby improving user satisfaction.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] A user enters "I'm disappointed I haven't been able to go to any music events lately" into their device. The device then sends this input to a server. It is assumed that this input will also include data related to the user's emotions.

[0536] Step 2:

[0537] The server analyzes the received data using natural language processing to understand the message content and the request regarding the event. It also uses an emotion engine to recognize the user's emotional state, in this case, "sadness."

[0538] Step 3:

[0539] Based on the analysis results, the server uses information gathering tools to access external music event information providers and retrieve relevant event information. Here, priority is given to collecting information on enjoyable events that might alleviate the user's "sadness," as well as information on artists of interest.

[0540] Step 4:

[0541] The acquired event information is processed within the server using information processing tools. This information is then compared with the user's history data to select information that is likely to be of particular interest. Sentimental data is also considered at this stage.

[0542] Step 5:

[0543] The server formats the selected information into a user-friendly format and transmits it to the user's terminal via a presentation device.

[0544] Step 6:

[0545] The user terminal displays the received information on the screen and guides the user in a way that captures their interest. It provides an interface that allows users to view event details and prompts them to take booking action as needed.

[0546] Step 7:

[0547] When a user expresses interest in an event and makes an additional request such as "I want to reserve tickets," the device sends that request to the server.

[0548] Step 8:

[0549] The server uses a reservation mechanism to coordinate with an external reservation service, check the availability of tickets for the specified event, and make a reservation. The process is completed by notifying the user's terminal of the reservation result.

[0550] (Example 2)

[0551] 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."

[0552] While modern information systems can provide information in response to user requests, they are not adequately capable of providing more personalized and appropriate information based on individual user emotions. Furthermore, there are challenges in flexibly selecting events and facilitating booking procedures that align with user sentiment.

[0553] 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.

[0554] In this invention, the server includes data analysis means for analyzing information requests from users, information gathering means for collecting relevant knowledge from external sources based on the analyzed requests and the user's emotional state, information processing means for comparing the collected knowledge with the user's historical data and processing it to make personalized suggestions, output means for presenting the selected and processed knowledge to the user, and emotion analysis means for identifying the user's emotional state and preferentially selecting event information appropriate to that emotion. This enables flexible and highly personalized information provision based on the user's emotions.

[0555] "Data analysis means" refers to technologies for analyzing information requests provided by users and understanding their content.

[0556] "Information gathering methods" refer to techniques for acquiring relevant knowledge from external sources based on analyzed information needs and emotional states.

[0557] "Information processing means" refers to technology that compares collected knowledge with the user's historical data, processes it as personalized information, and makes suggestions.

[0558] "Output method" refers to a method for presenting selected and processed knowledge to the user.

[0559] "Emotional analysis means" refers to a technology that identifies a user's emotional state during the process of analyzing information requests from users and selects information appropriate to that emotion.

[0560] This invention is a system that provides information while taking user emotions into consideration, and is realized through the following configuration and operation. The main components include a user terminal, a server, an emotion analysis engine, and a database.

[0561] The user terminal provides an interface for receiving messages from the user that include information requests and emotions. This interface is designed to support various input methods, such as voice input, text input, and touch operation. When a user enters a message that includes emotions, such as "I'm disappointed I couldn't go to the music event recently," the user terminal immediately sends that information to the server.

[0562] The server uses a natural language processing framework to analyze the received message. Specifically, an open-source natural language processing library is used. The server then uses a sentiment analysis engine to identify the emotions within the message. Through this process, it determines whether the user is expressing emotions such as "sadness" or "joy."

[0563] Based on the analyzed information, the server activates an information gathering module to acquire relevant knowledge from external data sources. Publicly available APIs on the internet may be used for data collection. The system is designed to prioritize event information that aligns with the user's emotions.

[0564] The acquired information is processed within the server using information processing technology. The processed information is then compared with the user's history database to generate personalized suggestions based on the user's past behavior and preferences.

[0565] Finally, the selected information is sent to the user's device and displayed on the screen. This allows the user to obtain information about events and activities that match their emotions.

[0566] As a concrete example, here is an example of a prompt: "Suggest ways for the user to spend their holidays in a way they will enjoy. However, their recent emotional state is recorded as 'fatigued'."

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] Users input emotionally charged messages using their devices. The message input interface allows users to deliver messages via voice or text.

[0570] Input: A message that includes the user's emotions (e.g., "I'm sad I haven't been able to go to music events lately")

[0571] Specifically, the terminal sends this message to the server in the specified format.

[0572] Step 2:

[0573] The server receives the message and parses its content using a natural language processing framework.

[0574] Input: Message sent by the user

[0575] Data processing: Perform grammatical analysis and keyword extraction.

[0576] Output: Analyzed message content (e.g., hobby "music", emotion "disappointed")

[0577] In concrete terms, the server analyzes the grammatical structure and identifies important words and phrases.

[0578] Step 3:

[0579] The server uses an emotion analysis engine to identify the emotional components of the message.

[0580] Input: Analyzed message content

[0581] Data processing: Identify emotional states and classify them into emotional categories such as "sadness."

[0582] Output: User's emotional state (e.g., "sadness")

[0583] Specifically, the server executes the emotion engine's algorithm and generates emotion vectors.

[0584] Step 4:

[0585] The server activates the information gathering module and collects relevant information from external sources.

[0586] Input: Data on the user's emotional state and interests

[0587] Data processing: Prioritize collecting event information that is relevant to the emotions being expressed.

[0588] Output: Related information list (e.g., list of music events)

[0589] Specifically, the server calls an external API to retrieve event information from the database.

[0590] Step 5:

[0591] The server processes the collected information using information processing technology and compares it with the user's history data.

[0592] Input: Related information list, user history database

[0593] Data processing: Perform matching filtering to generate personalized suggestions.

[0594] Output: Personalized event suggestions (e.g., specific music events)

[0595] Specifically, the server executes a filtering algorithm to select events that match past user behavior.

[0596] Step 6:

[0597] The server sends the selected information to the user's terminal and displays it on the screen.

[0598] Input: Personalized event suggestions

[0599] Output: Event information presented to the user

[0600] Specifically, the device displays a notification, allowing the user to view the information.

[0601] (Application Example 2)

[0602] 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."

[0603] Modern information delivery systems often lack sufficient personalization that takes user emotions into account, making it difficult to immediately provide users with specific and appropriate information and content they seek. Furthermore, the inefficiency of emotion-based information recommendations and event bookings makes improving user satisfaction a challenge.

[0604] 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.

[0605] In this invention, the server includes natural language processing means for analyzing information requests from users and recognizing emotions; information gathering means for collecting relevant information from external sources based on the analyzed requests and emotions; and information processing means for comparing the collected information with the user's history database and selecting and processing appropriate information according to the user's emotions. This makes it possible to provide specific and attractive information and suggest content based on the user's emotions.

[0606] "Natural language processing means" refers to technologies that analyze text data entered by users and understand its content and sentiment.

[0607] "Information gathering means" refers to a mechanism for obtaining relevant information from external sources based on analyzed user requests and emotions.

[0608] "Information processing means" refers to the process of comparing collected information with the user's history database and selecting and processing necessary information according to the user's emotions.

[0609] "Presentation means" refers to an interface or mechanism for appropriately displaying selected and processed information to the user.

[0610] "Emotion" refers to elements that identify mental states such as joy, sadness, and stress by analyzing the user's emotional state expressed in their text input.

[0611] A "user history database" refers to a collection of information that accumulates past user behavior, preferences, and emotional data to support the provision of personalized services.

[0612] "External information sources" refer to sources of information that may exist outside the device, such as publicly available data on the internet or commercial databases.

[0613] This invention aims to construct an information provision and content suggestion system that takes user emotions into consideration. Implementation will utilize user terminals, servers, and the network environment connecting them.

[0614] The user terminal provides an interface for receiving text input that reflects the user's emotions. Through this interface, the user can input their emotions and information requests in natural language.

[0615] The server first receives data sent by the user and analyzes it using natural language processing techniques. This analysis process utilizes natural language processing libraries such as Hugging Face Transformers and the Google Cloud Natural Language API. Using these technologies, the server identifies the user's emotions and generates and records emotion data.

[0616] Based on the analyzed emotions and data, the server utilizes information gathering tools to acquire relevant videos and content from external sources. The collected data is then cross-referenced with the user's history database and processed by information processing tools to select and create the most relevant content.

[0617] The selected information is presented to the user. Furthermore, if the user shows interest in specific content, the system can integrate with external services to make reservations or recommendations. For example, if a user enters "I've been feeling stressed lately," the system can suggest relaxing videos and music. An example of a prompt to the generating AI model in this case would be, "The user is currently feeling 'stressed.' Please suggest three relaxing content options."

[0618] This system allows users to receive personalized information and content that responds to their emotions, leading to increased satisfaction.

[0619] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0620] Step 1:

[0621] The user terminal receives input from the user. The user inputs emotions and information requests in natural language. The entered text data is sent from the terminal to the server. At this stage, the input data may contain specific emotions or requests.

[0622] Step 2:

[0623] The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Hugging Face Transformers library to analyze the text content and the Google Cloud Natural Language API to determine emotions. Text data is provided as input, and emotion data and request data are generated as output. The server records this information in an internal database.

[0624] Step 3:

[0625] Based on the analyzed requests and sentiments, the server uses information gathering tools to acquire relevant information from external sources. It sends prompt sentences to a generating AI model to obtain a list of suitable content candidates. The input consists of sentiment data and request data, and the output is a list of relevant content candidates. This process includes specific actions such as accessing external APIs via the internet.

[0626] Step 4:

[0627] The server retrieves potential content and compares it with the user's history database, then uses information processing tools to select and process the content. By referring to the user's history in the database, the most appropriate content is selected as output. This process involves specific actions that predict user preferences using machine learning algorithms.

[0628] Step 5:

[0629] The server presents selected information to the user's terminal. This information includes personalized content tailored to the user's current mood. The output information is displayed on the terminal in a format easily accessible to the user.

[0630] Step 6:

[0631] The server collaborates with external services to reserve or recommend content that the user has shown interest in. If necessary, it can trigger calls to external service APIs based on user actions, such as ticket reservations. Input includes the user's selected actions, and output includes reservation confirmation information.

[0632] Throughout this entire process, users can receive emotion-responsive information in real time and be prompted to take relevant actions.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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".

[0650] This invention is implemented as a system that efficiently collects information related to users' hobbies and fan activities and provides it to users in an appropriate format. This system consists of a user terminal, a server, and an internal database, and functions as follows.

[0651] The user terminal provides an interface for users to input information they are interested in. If a user inputs "Tell me about idol events next week," the request is initially parsed within the terminal and sent to the server as data.

[0652] The server analyzes received user requests using natural language processing to understand the user's intent. Based on the analysis results, information gathering tools access external event information services and related websites to collect relevant data. For example, it might retrieve information about idol events held within a specified period.

[0653] The acquired information is compared with the user's past history data by information processing tools on the server, and information that matches the user's interests is selected and processed. This history data is an accumulation of information on events and artists that the user has shown interest in in the past, and forms the basis for providing personalized suggestions.

[0654] The selected information is sent from the server to the user's terminal and presented to the user. This information is displayed in a format tailored to the user's interests, allowing them to view details and request ticket reservations for events that interest them.

[0655] For example, if a user becomes interested in a particular idol's live performance from the event information presented, the user's device sends an additional request to the server, and the reservation process begins. The server collaborates with external services to check ticket availability and completes the reservation if necessary. Through this process, the user can participate in events they are interested in with minimal effort.

[0656] In this way, the present invention can smoothly support users' hobbies and fan activities, and provide them with a highly satisfying experience.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The user enters "Tell me about next week's idol event information" into their terminal. The user's terminal performs an initial analysis of the input and sends the analyzed data to the server.

[0660] Step 2:

[0661] The server analyzes the received request data in detail using natural language processing techniques. As a result of the analysis, attribute information such as event type, date, and region is extracted.

[0662] Step 3:

[0663] The server uses information gathering methods based on the analysis results to obtain relevant event information from external event information providers. It collects idle event data for a specified period using an external API or web crawler.

[0664] Step 4:

[0665] The collected event information is evaluated within the server using information processing tools. By referring to the user's history database and considering information about past events attended and idols they have shown interest in, the most relevant information is selected.

[0666] Step 5:

[0667] The server formats the selected event information and transmits it to the user's terminal via a presentation device in order to provide it to the user.

[0668] Step 6:

[0669] The user terminal displays received event information and provides an interface that prompts for further information and subsequent actions (e.g., ticket booking). Users can review the details here and, if necessary, request a booking.

[0670] Step 7:

[0671] When a reservation request is received, the server uses the reservation mechanism to connect with an external reservation service, check ticket availability, and complete the reservation for the user. The process is completed when the reservation result is notified to the user's device.

[0672] (Example 1)

[0673] 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".

[0674] Conventional information systems have struggled to efficiently collect event information based on user interests and present it in an optimal format. Furthermore, users had to manually research and make reservations when they showed interest in a particular event, which was time-consuming. Moreover, the systems were not adequately equipped to dynamically understand user interests and provide personalized information.

[0675] 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.

[0676] In this invention, the server includes a natural language processing means that analyzes information requests from users and extracts keywords as necessary; an information gathering means that converts the analyzed requests into prompt sentences, accesses external information sources, and collects relevant information; and an information processing means that compares the collected information with the user's past history data, selects information considering the user's interests, and visualizes it. This enables the effective collection of event information based on the user's interests, provides it in a visually easy-to-understand format, and allows for the automated execution of reservation procedures.

[0677] "Natural language processing means" refers to a program or technology that analyzes information requests received from users to understand keywords and intent.

[0678] "Information gathering means" refers to a program or technology that accesses external information sources and obtains relevant data based on analyzed information requests.

[0679] "Information processing means" refers to a program or technology for comparing collected data with the user's past history data and selecting and processing it to match the user's interests.

[0680] "Presentation means" refers to a program or interface for displaying selected and processed information to the user in an easily understandable manner.

[0681] A "reservation method" is a program or technology that automatically handles reservation and purchase procedures for events that users are interested in, by linking with external reservation services.

[0682] A "database management means" is a program or technology for dynamically collecting user preferences through interaction with users and for building and updating a database.

[0683] This invention is implemented as a system for efficiently collecting and presenting information based on user interests. The system consists of a user terminal, a server, and a database.

[0684] The user terminal provides an interface for users to input event information of interest. Specifically, it includes text input fields and category selection options. This terminal performs basic parsing to send the received input as data to the server.

[0685] The server uses natural language processing (NLP) to analyze the information request received from the user. Here, natural language processing libraries (e.g., spaCy and the GPT model) are used to understand the user's intent. At this stage, the user's input is converted into a prompt. For example, a prompt such as "Please retrieve information about the idol live concert being held next week and inform the user." is generated.

[0686] Next, the server uses information gathering tools to collect necessary data from external sources. This data is typically obtained via APIs and received in JSON format. The retrieved information is then compared with the user's past history data by information processing tools within the server, and selected and processed to reflect the user's interests.

[0687] Finally, the selected information is sent from the server to the user's terminal, which then presents the information to the user in an intuitively viewable format. For example, list format or calendar display may be used.

[0688] This system allows users to efficiently obtain information on events of interest, view details, and make reservations with minimal effort. Furthermore, it provides personalized information based on user preferences, resulting in a highly satisfying user experience.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The user inputs event information of interest to their terminal. The terminal receives the user's request via a text input field, performs a basic analysis of the entered text, and extracts key keywords. It then structures this extracted data and sends it to the server. In this step, the input is the user's text data, and the output is structured data containing the extracted keywords.

[0692] Step 2:

[0693] The server analyzes the received data in detail using natural language processing techniques. Using a generative AI model, it generates prompts to understand the user's intent. These prompts are used as data collection requests to external sources. The input for this step is the user's structured data, and the output is the generated prompts.

[0694] Step 3:

[0695] The server accesses external information sources using information gathering tools and retrieves relevant information based on prompt messages. At this stage, information is collected from an external event database via a RESTful API. The collected information is imported into the server in JSON format. The input for this step is a prompt message, and the output is raw data retrieved from the external information source.

[0696] Step 4:

[0697] The server compares the collected information with the user's past history data. Using information processing tools, data is selected based on the user's interests and preferences and processed into a visually easy-to-understand format. This process involves filtering and ranking the data to identify the most relevant event information. The input for this step is raw data and user history data, and the output is selected and processed event information.

[0698] Step 5:

[0699] The server sends the selected information to the user's terminal. The terminal displays the received information on its interface, allowing the user to view the details. Methods that allow the user to operate intuitively, such as list format or calendar display, are used. The input in this step is the selected and processed information, and the output is the visual display provided to the user.

[0700] Step 6:

[0701] If a user is interested in a displayed event and wishes to make a reservation, their device sends an additional request to the server. The server then interacts with an external reservation system to check ticket availability and complete the reservation process if necessary. The input for this step is the user's additional request, and the output is reservation confirmation information.

[0702] (Application Example 1)

[0703] 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".

[0704] Traditional information gathering systems lacked sufficient personalization capabilities to effectively provide information based on users' interests and preferences. Furthermore, the use of location information within physical stores was limited, making it difficult for users to obtain the most relevant information upon arrival. This often resulted in a limited user experience.

[0705] 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.

[0706] In this invention, the server includes a natural language processing means for analyzing information requests from users, an information gathering means for collecting relevant information from external sources based on the analyzed requests, an information processing means for comparing the collected information with the user's historical data and selecting and processing appropriate information, a presentation means for presenting the selected and processed information to the user and providing detailed information about products or services based on additional requests, and a location information analysis means for providing in-store information using local location information. This enables users to receive optimal information based on their personal tastes and interests in real time within the store.

[0707] "Natural language processing means" refers to technologies for understanding information requests entered by users and analyzing their intent.

[0708] "Information gathering means" refers to the means of efficiently obtaining relevant information from external sources based on analyzed requirements.

[0709] "Information processing means" refers to a means of comparing collected information with the user's past history data, selecting information suitable for the user, and processing it.

[0710] "Presentation methods" refer to means of providing selected and processed information to users in an easy-to-view format, and of presenting detailed information about products and services upon request.

[0711] "Location information analysis means" refers to technology that determines the user's current location and provides appropriate geographical information and services based on that location information.

[0712] A "reservation method" refers to a means of automatically executing reservation and purchase procedures for events specified by the user, in conjunction with external services.

[0713] A "database management system" is a management function that builds preference data collected through interaction with users and continuously updates it to provide information tailored to each individual user.

[0714] To realize this invention, the system consists of a program that integrates multiple technologies. The server interprets information requests from users using natural language processing algorithms. Natural language processing libraries such as Google's TensorFlow and Hugging Face's Transformers are used. Based on the analysis results, the server utilizes external information gathering channels (such as APIs and web crawlers) to collect information that meets the conditions specified by the user.

[0715] Subsequently, the information collected by the information processing system is compared with the user's historical data. Amazon Web Services (AWS) DynamoDB is used as the database system here. Based on the user data, a process is carried out to select and process personalized information. In particular, data matching and filtering are used to select information that best matches the user's interests.

[0716] On the device, selected information is displayed while also considering the user's location. Location analysis combines the smartphone's built-in GPS and BLE beacon technology. This allows users to obtain relevant information in real time when visiting physical stores. Furthermore, support is provided for users to quickly register for events or purchase products through reservation methods.

[0717] As a concrete example, when a user scans product A with a smartphone app in a store, special event information and promotions related to that product are immediately displayed. In this process, by utilizing a generative AI model and employing prompts such as "Tell me what recommended information should be displayed while I'm in the store," rapid and accurate information provision is achieved.

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The user enters the information request using an app installed on their smartphone. The entered request is temporarily stored on the device as text data and prepared to be sent to the server. In this step, the input is the user's information request, and the output is the request data sent to the server.

[0721] Step 2:

[0722] The server analyzes the received text data using natural language processing techniques. In this step, it understands the intent of the input request and identifies relevant information based on extracted keywords and elements. The technology used is a natural language processing library, and the output is a search query tailored to the user's request.

[0723] Step 3:

[0724] The server collects relevant data using external information gathering methods based on the analysis results. It obtains specified event and product information via API or web scraping. The input is a search query, and the output is a dataset of event details and product information. Within this process, it specifically accesses the selected information sources and collects the data.

[0725] Step 4:

[0726] The collected information data is cross-referenced with user history data by information processing tools on the server. User data stored in Amazon Web Services' DynamoDB is referenced, and highly relevant information is selected. The input for this step is an information dataset and a user database, and the output is a set of personalized information.

[0727] Step 5:

[0728] The device displays personalized information received from the server, linked to the user's location information. In this process, location information is obtained from the smartphone's GPS function or BLE beacons, providing information relevant to the user's specific location. Input consists of user location data and a set of personalized information, while output is the information displayed on the user interface.

[0729] Step 6:

[0730] Based on the information provided, the user makes further reservation or purchase requests through the terminal. Requests received from the terminal are sent to an external server via the reservation system, where ticket reservations or product reservations are made. The input in this step is a reservation request, and the output is a reservation confirmation notification. During this process, data communication between the server and the external service is specifically performed.

[0731] 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.

[0732] The present invention is implemented as a system that incorporates an emotion engine for recognizing user emotions into a conventional information provision system. This system consists of a user terminal, a server, an internal database, and the emotion engine.

[0733] The user terminal provides an interface for users to input information they are interested in and to engage in conversations. If a user inputs a message that includes an emotion, such as "I'm disappointed I couldn't go to the music event recently," the terminal sends that data to the server.

[0734] The server analyzes the received data using natural language processing and an emotion engine. The natural language processing analyzes the message content, and the emotion engine identifies the user's emotions and records them in a database. For example, if a message expressing disappointment is recognized, "sadness" will be recorded as the user's recent emotion.

[0735] The analyzed information is used to acquire event information from relevant external sources using information gathering methods. During this process, information relevant to the user's emotions is prioritized and selected based on the analysis results of the emotion engine. This process allows for the provision of event information that is highly likely to evoke feelings of joy and excitement in the user.

[0736] The acquired information is processed within the server using data processing tools and then cross-referenced with the user's individual history database along with sentiment data. This enables more personalized suggestions.

[0737] The selected and processed information is then sent to the user's terminal via a presentation device. For example, if a user is feeling emotionally exhausted, the system can suggest relaxing events or activities that are ideal for changing their mood.

[0738] Furthermore, if a user expresses interest in a particular event, the server utilizes reservation mechanisms to execute ticket reservations for the event through integration with external services. Through this series of processes, the present invention can realize flexible information provision and experiences based on the user's emotions and interests, thereby improving user satisfaction.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] A user enters "I'm disappointed I haven't been able to go to any music events lately" into their device. The device then sends this input to a server. It is assumed that this input will also include data related to the user's emotions.

[0742] Step 2:

[0743] The server analyzes the received data using natural language processing to understand the message content and the request regarding the event. It also uses an emotion engine to recognize the user's emotional state, in this case, "sadness."

[0744] Step 3:

[0745] Based on the analysis results, the server uses information gathering tools to access external music event information providers and retrieve relevant event information. Here, priority is given to collecting information on enjoyable events that might alleviate the user's "sadness," as well as information on artists of interest.

[0746] Step 4:

[0747] The acquired event information is processed within the server using information processing tools. This information is then compared with the user's history data to select information that is likely to be of particular interest. Sentimental data is also considered at this stage.

[0748] Step 5:

[0749] The server formats the selected information into a user-friendly format and transmits it to the user's terminal via a presentation device.

[0750] Step 6:

[0751] The user terminal displays the received information on the screen and guides the user in a way that captures their interest. It provides an interface that allows users to view event details and prompts them to take booking action as needed.

[0752] Step 7:

[0753] When a user expresses interest in an event and makes an additional request such as "I want to reserve tickets," the device sends that request to the server.

[0754] Step 8:

[0755] The server uses a reservation mechanism to coordinate with an external reservation service, check the availability of tickets for the specified event, and make a reservation. The process is completed by notifying the user's terminal of the reservation result.

[0756] (Example 2)

[0757] 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".

[0758] While modern information systems can provide information in response to user requests, they are not adequately capable of providing more personalized and appropriate information based on individual user emotions. Furthermore, there are challenges in flexibly selecting events and facilitating booking procedures that align with user sentiment.

[0759] 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.

[0760] In this invention, the server includes data analysis means for analyzing information requests from users, information gathering means for collecting relevant knowledge from external sources based on the analyzed requests and the user's emotional state, information processing means for comparing the collected knowledge with the user's historical data and processing it to make personalized suggestions, output means for presenting the selected and processed knowledge to the user, and emotion analysis means for identifying the user's emotional state and preferentially selecting event information appropriate to that emotion. This enables flexible and highly personalized information provision based on the user's emotions.

[0761] "Data analysis means" refers to technologies for analyzing information requests provided by users and understanding their content.

[0762] "Information gathering methods" refer to techniques for acquiring relevant knowledge from external sources based on analyzed information needs and emotional states.

[0763] "Information processing means" refers to technology that compares collected knowledge with the user's historical data, processes it as personalized information, and makes suggestions.

[0764] "Output method" refers to a method for presenting selected and processed knowledge to the user.

[0765] "Emotional analysis means" refers to a technology that identifies a user's emotional state during the process of analyzing information requests from users and selects information appropriate to that emotion.

[0766] This invention is a system that provides information while taking user emotions into consideration, and is realized through the following configuration and operation. The main components include a user terminal, a server, an emotion analysis engine, and a database.

[0767] The user terminal provides an interface for receiving messages from the user that include information requests and emotions. This interface is designed to support various input methods, such as voice input, text input, and touch operation. When a user enters a message that includes emotions, such as "I'm disappointed I couldn't go to the music event recently," the user terminal immediately sends that information to the server.

[0768] The server uses a natural language processing framework to analyze the received message. Specifically, an open-source natural language processing library is used. The server then uses a sentiment analysis engine to identify the emotions within the message. Through this process, it determines whether the user is expressing emotions such as "sadness" or "joy."

[0769] Based on the analyzed information, the server activates an information gathering module to acquire relevant knowledge from external data sources. Publicly available APIs on the internet may be used for data collection. The system is designed to prioritize event information that aligns with the user's emotions.

[0770] The acquired information is processed within the server using information processing technology. The processed information is then compared with the user's history database to generate personalized suggestions based on the user's past behavior and preferences.

[0771] Finally, the selected information is sent to the user's device and displayed on the screen. This allows the user to obtain information about events and activities that match their emotions.

[0772] As a concrete example, here is an example of a prompt: "Suggest ways for the user to spend their holidays in a way they will enjoy. However, their recent emotional state is recorded as 'fatigued'."

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] Users input emotionally charged messages using their devices. The message input interface allows users to deliver messages via voice or text.

[0776] Input: A message that includes the user's emotions (e.g., "I'm sad I haven't been able to go to music events lately")

[0777] Specifically, the terminal sends this message to the server in the specified format.

[0778] Step 2:

[0779] The server receives the message and parses its content using a natural language processing framework.

[0780] Input: Message sent by the user

[0781] Data processing: Perform grammatical analysis and keyword extraction.

[0782] Output: Analyzed message content (e.g., hobby "music", emotion "disappointed")

[0783] In concrete terms, the server analyzes the grammatical structure and identifies important words and phrases.

[0784] Step 3:

[0785] The server uses an emotion analysis engine to identify the emotional components of the message.

[0786] Input: Analyzed message content

[0787] Data processing: Identify emotional states and classify them into emotional categories such as "sadness."

[0788] Output: User's emotional state (e.g., "sadness")

[0789] Specifically, the server executes the emotion engine's algorithm and generates emotion vectors.

[0790] Step 4:

[0791] The server activates the information gathering module and collects relevant information from external sources.

[0792] Input: Data on the user's emotional state and interests

[0793] Data processing: Prioritize collecting event information that is relevant to the emotions being expressed.

[0794] Output: Related information list (e.g., list of music events)

[0795] Specifically, the server calls an external API to retrieve event information from the database.

[0796] Step 5:

[0797] The server processes the collected information using information processing technology and compares it with the user's history data.

[0798] Input: Related information list, user history database

[0799] Data processing: Perform matching filtering to generate personalized suggestions.

[0800] Output: Personalized event suggestions (e.g., specific music events)

[0801] Specifically, the server executes a filtering algorithm to select events that match past user behavior.

[0802] Step 6:

[0803] The server sends the selected information to the user's terminal and displays it on the screen.

[0804] Input: Personalized event suggestions

[0805] Output: Event information presented to the user

[0806] Specifically, the device displays a notification, allowing the user to view the information.

[0807] (Application Example 2)

[0808] 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".

[0809] Modern information delivery systems often lack sufficient personalization that takes user emotions into account, making it difficult to immediately provide users with specific and appropriate information and content they seek. Furthermore, the inefficiency of emotion-based information recommendations and event bookings makes improving user satisfaction a challenge.

[0810] 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.

[0811] In this invention, the server includes natural language processing means for analyzing information requests from users and recognizing emotions; information gathering means for collecting relevant information from external sources based on the analyzed requests and emotions; and information processing means for comparing the collected information with the user's history database and selecting and processing appropriate information according to the user's emotions. This makes it possible to provide specific and attractive information and suggest content based on the user's emotions.

[0812] "Natural language processing means" refers to technologies that analyze text data entered by users and understand its content and sentiment.

[0813] "Information gathering means" refers to a mechanism for obtaining relevant information from external sources based on analyzed user requests and emotions.

[0814] "Information processing means" refers to the process of comparing collected information with the user's history database and selecting and processing necessary information according to the user's emotions.

[0815] "Presentation means" refers to an interface or mechanism for appropriately displaying selected and processed information to the user.

[0816] "Emotion" refers to elements that identify mental states such as joy, sadness, and stress by analyzing the user's emotional state expressed in their text input.

[0817] A "user history database" refers to a collection of information that accumulates past user behavior, preferences, and emotional data to support the provision of personalized services.

[0818] "External information sources" refer to sources of information that may exist outside the device, such as publicly available data on the internet or commercial databases.

[0819] This invention aims to construct an information provision and content suggestion system that takes user emotions into consideration. Implementation will utilize user terminals, servers, and the network environment connecting them.

[0820] The user terminal provides an interface for receiving text input that reflects the user's emotions. Through this interface, the user can input their emotions and information requests in natural language.

[0821] The server first receives data sent by the user and analyzes it using natural language processing techniques. This analysis process utilizes natural language processing libraries such as Hugging Face Transformers and the Google Cloud Natural Language API. Using these technologies, the server identifies the user's emotions and generates and records emotion data.

[0822] Based on the analyzed emotions and data, the server utilizes information gathering tools to acquire relevant videos and content from external sources. The collected data is then cross-referenced with the user's history database and processed by information processing tools to select and create the most relevant content.

[0823] The selected information is presented to the user. Furthermore, if the user shows interest in specific content, the system can integrate with external services to make reservations or recommendations. For example, if a user enters "I've been feeling stressed lately," the system can suggest relaxing videos and music. An example of a prompt to the generating AI model in this case would be, "The user is currently feeling 'stressed.' Please suggest three relaxing content options."

[0824] This system allows users to receive personalized information and content that responds to their emotions, leading to increased satisfaction.

[0825] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0826] Step 1:

[0827] The user terminal receives input from the user. The user inputs emotions and information requests in natural language. The entered text data is sent from the terminal to the server. At this stage, the input data may contain specific emotions or requests.

[0828] Step 2:

[0829] The server analyzes the received text data using natural language processing techniques. Specifically, it uses the Hugging Face Transformers library to analyze the text content and the Google Cloud Natural Language API to determine emotions. Text data is provided as input, and emotion data and request data are generated as output. The server records this information in an internal database.

[0830] Step 3:

[0831] Based on the analyzed requests and sentiments, the server uses information gathering tools to acquire relevant information from external sources. It sends prompt sentences to a generating AI model to obtain a list of suitable content candidates. The input consists of sentiment data and request data, and the output is a list of relevant content candidates. This process includes specific actions such as accessing external APIs via the internet.

[0832] Step 4:

[0833] The server retrieves potential content and compares it with the user's history database, then uses information processing tools to select and process the content. By referring to the user's history in the database, the most appropriate content is selected as output. This process involves specific actions that predict user preferences using machine learning algorithms.

[0834] Step 5:

[0835] The server presents selected information to the user's terminal. This information includes personalized content tailored to the user's current mood. The output information is displayed on the terminal in a format easily accessible to the user.

[0836] Step 6:

[0837] The server collaborates with external services to reserve or recommend content that the user has shown interest in. If necessary, it can trigger calls to external service APIs based on user actions, such as ticket reservations. Input includes the user's selected actions, and output includes reservation confirmation information.

[0838] Throughout this entire process, users can receive emotion-responsive information in real time and be prompted to take relevant actions.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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."

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] The following is further disclosed regarding the embodiments described above.

[0861] (Claim 1)

[0862] A natural language processing method for analyzing information requests from users,

[0863] Information gathering means for collecting relevant information from external sources based on the analyzed requirements,

[0864] Information processing means that compares collected information with user history data and selects and processes appropriate information,

[0865] A means of presenting selected and processed information to the user,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, which includes a reservation means for performing reservation or purchase procedures for events specified by the user in cooperation with an external service.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising a database management means for building and updating a user preference database collected through continuous interaction with users.

[0871] "Example 1"

[0872] (Claim 1)

[0873] A natural language processing method that analyzes information requests from users and extracts keywords as needed,

[0874] Information gathering means that converts the parsed request into a prompt statement and accesses external information sources to collect relevant information,

[0875] An information processing method that compares collected information with the user's past history data, selects information considering the user's interests, and visualizes it.

[0876] A presentation method that transmits selected information to the user's device and presents it in an intuitively viewable format,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, which includes means for automatically processing additional requests for events that a user is interested in and for coordinating with an external reservation service to execute reservation or purchase procedures.

[0880] (Claim 3)

[0881] The system according to claim 1, comprising a database management means for constructing a user preference database that is dynamically updated through continuous interaction with the user.

[0882] "Application Example 1"

[0883] (Claim 1)

[0884] A natural language processing method for analyzing information requests from users,

[0885] Information gathering means for collecting relevant information from external sources based on the analyzed requirements,

[0886] Information processing means that compares collected information with user history data and selects and processes appropriate information,

[0887] A presentation means that presents selected and processed information to the user and provides detailed information about a product or service based on additional requests,

[0888] A location information analysis method that provides in-store information using local location information,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, which includes a reservation means that performs the reservation or purchase procedure for an event specified by the user in cooperation with an external service, and provides suggestions and recommendations for related products.

[0892] (Claim 3)

[0893] The system according to claim 1, comprising a database management means for building and updating a user preference database collected through continuous interaction with users, and for appropriately updating relevant information by referring to location information.

[0894] "Example 2 of combining an emotion engine"

[0895] (Claim 1)

[0896] A data analysis method for analyzing information requests from users,

[0897] Information gathering means for collecting relevant knowledge from external sources based on analyzed requests and the user's emotional state,

[0898] Information processing means that compares collected knowledge with user history data and processes it to make personalized suggestions,

[0899] An output method that presents selected and processed knowledge to the user,

[0900] A sentiment analysis method that identifies the user's emotional state and prioritizes selecting event information appropriate to that emotion,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, comprising a reservation means for executing the reservation or purchase of an activity selected by a user in cooperation with an external information service.

[0904] (Claim 3)

[0905] The system according to claim 1, comprising data management means for constructing and updating a database based on user preferences collected through continuous information exchange with users.

[0906] "Application example 2 when combining with an emotional engine"

[0907] (Claim 1)

[0908] A natural language processing method that analyzes information requests from users and recognizes emotions,

[0909] Information gathering means for collecting relevant information from external sources based on analyzed requests and emotions,

[0910] Information processing means that compares collected information with the user's history database and selects and processes appropriate information according to the user's emotions,

[0911] A means of presenting selected and processed information to the user,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, including means for performing procedures to reserve or recommend events or content specified by the user in cooperation with external providers.

[0915] (Claim 3)

[0916] The system according to claim 1, comprising a database management means for building and updating a user preference database, including emotional data, through continuous interaction with users. [Explanation of Symbols]

[0917] 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 natural language processing method for analyzing information requests from users, Information gathering means for collecting relevant information from external sources based on the analyzed requirements, Information processing means that compares collected information with user history data and selects and processes appropriate information, A means of presenting selected and processed information to the user, A system that includes this.

2. The system according to claim 1, which includes a reservation means for performing reservation or purchase procedures for events specified by the user in cooperation with an external service.

3. The system according to claim 1, comprising a database management means for building and updating a user preference database collected through continuous interaction with users.

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