system

Abstract

Provide a system. 【Solution means】 A device that receives a user's instruction, A device that analyzes the received instruction using language processing technology, A device that searches a data set based on the analyzed data and identifies related data, A device that integrates the identified data and creates a response as natural language, A device that transmits the created response to the user's device, A terminal for the user to input security-related questions, A device that uses statistical information to identify security management information related to the question, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, it is required to immediately respond to various customer questions. However, in a conventional system, it has been difficult to promptly and accurately present information in response to a user's question. As a result, this has sometimes led to a decrease in customer satisfaction and a deterioration in business efficiency, and thus a method for solving this problem is required.

Means for Solving the Problems

[0005] This invention solves the above problem by providing a method for receiving user input and analyzing a question using natural language processing technology. Based on the analysis results, relevant information is searched from a database, an optimal response is generated, and it is sent to the user's terminal, enabling a quick and accurate response to the user's question. This can improve customer satisfaction and operational efficiency.

[0006] A "user" is someone who inputs questions or information into a system.

[0007] "Receiving means" refers to the function that receives input from the user and incorporates it into the system as data.

[0008] "Analysis means" refers to a function that analyzes the received user input using natural language processing technology and understands its content.

[0009] "Search method" refers to the function of finding relevant information from a database based on information obtained through analysis methods.

[0010] A "generation method" is a function that integrates the relevant information it finds and creates a response in natural language.

[0011] "Transmission means" refers to the function for sending the generated response to the user's terminal.

[0012] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0013] A "terminal" refers to a device or system used by a user to input information.

[0014] A "database" is a collection of structured data that is organized and searchable. [Brief explanation of the drawing]

[0015] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the 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.

[0019] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention aims to efficiently perform communication and data processing between users, terminals, and servers in order to realize a digital assistant system.

[0037] First, the user enters a question using a terminal. This input is in natural language, and the terminal sends the input data to the server. The server receives this input and analyzes the text using natural language processing technology. This allows the server to understand the intent of the input and important keywords, and then retrieve relevant information from the database based on the analysis results.

[0038] The server integrates data gathered from multiple sources, summarizing and supplementing it as needed. This allows it to select the most relevant information for the user's question and generate a response. This response generation is designed to consider context and be expressed in more natural language.

[0039] The generated response is sent from the server to the terminal and displayed on the user's screen. The user can use this information to resolve the problem and decide on their next course of action. Furthermore, the user can provide feedback on the response, which is sent to the server and used to improve future responses.

[0040] As a concrete example, suppose a user enters "How do I return an item?" into their terminal. In this case, the server analyzes "return procedure" as a keyword and searches for information in the relevant FAQ database. Based on the results, it can generate a step-by-step guide and provide the user with a specific response such as, "First, put the purchased item back in its original packaging and use the registered shipping carrier."

[0041] This system is an invention that can improve user satisfaction and streamline inquiry processing by enabling quick and accurate responses.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters a question into the terminal and presses the send button. The terminal then packages this input data into packets and sends them to the server over the network.

[0045] Step 2:

[0046] The server analyzes the received data and passes the input to a natural language processing engine to understand the user's intent. The NLP engine analyzes the text and extracts keywords and the user's intent.

[0047] Step 3:

[0048] The server searches the FAQ database and product information database based on the extracted information. It executes database queries to efficiently find related records.

[0049] Step 4:

[0050] The server integrates search results and organizes the information that should be provided to the user. If necessary, it summarizes the information and links it to more detailed information.

[0051] Step 5:

[0052] The server generates a natural language response based on the integrated information. The response is optimized to be in a format that is easy for the user to understand.

[0053] Step 6:

[0054] The server sends the generated response data to the terminal. The terminal displays this received data on its screen so that the user can verify it.

[0055] Step 7:

[0056] The user reviews the information presented, asks further questions as needed, and takes subsequent actions based on the information provided.

[0057] Step 8:

[0058] Users can provide feedback on the usefulness of the response. The terminal sends this feedback to the server, which helps improve the system.

[0059] (Example 1)

[0060] 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."

[0061] Conventional digital assistant systems often suffer from delayed or inaccurate responses to user requests. This is because natural language processing techniques and the identification of relevant data from external information resources are not performed efficiently. Furthermore, the generated language expressions often do not adequately consider the context, failing to provide useful information to the user.

[0062] 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.

[0063] In this invention, the server includes means for receiving a user request and analyzing the request using data transformation technology, means for identifying relevant data from external information resources based on the analyzed information, and means for integrating the identified data and generating a linguistic expression that indicates the request. This makes it possible to respond quickly and accurately to user requests and provide contextually relevant and useful information.

[0064] A "user" refers to an individual or group that makes a request to the system.

[0065] A "request" refers to an inquiry made by a user to the system regarding the acquisition of information or services.

[0066] "Data transformation technology" refers to technical methods used to analyze and understand user requirements.

[0067] "Means of analysis" refers to the function that utilizes data transformation techniques to understand received requests.

[0068] "External information resources" refer to databases and information services that provide relevant data located outside the system.

[0069] "Means for identifying relevant data" refers to the function of finding necessary data from external information resources based on the analysis results.

[0070] "Means of integrating data" refers to the function of gathering identified related data into a single, combined piece of information.

[0071] "Means for generating linguistic expressions" refers to a function that generates appropriate responses for the user based on integrated data.

[0072] "Communication device" refers to a terminal or device used to transmit generated linguistic expressions to the user.

[0073] This invention is a digital assistant system that enables efficient data exchange and information provision between users, terminals, and servers.

[0074] The user first inputs information using a terminal. This terminal is an information processing device such as a smartphone or computer, and can accept text or voice input. For example, the user might input the request, "Please tell me the weather for next week," into the terminal.

[0075] The terminal's role is to send received requests to the server. Secure communication protocols over the internet are used for transmission. The server receives the requests and analyzes them using generative AI models. This analysis uses Python's natural language processing library to extract the intent and important keywords of the requests.

[0076] Next, the server queries external information resources based on the analysis results. These external resources include, for example, information sources that provide weather forecasts and databases that store technical documents. The server identifies and integrates the relevant information to construct the optimal response for the user. The response is generated in natural language, such as, "Next week will be sunny nationwide with an average temperature of 25 degrees Celsius."

[0077] The generated response is sent to the terminal, which then displays it to the user. The user can then make decisions based on the information provided. Furthermore, the user can input feedback on the response through the terminal, which is then returned to the server. The server uses this feedback as data to improve the quality of future information provision.

[0078] As a concrete example, the user's prompt might be in the form of "Please tell me about the return procedure," and the AI ​​model would analyze this information and respond by returning relevant guidelines.

[0079] This invention provides a system that responds quickly and accurately to user requests by integrating natural language processing and data integration technologies.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user inputs requests using natural language via a terminal. These requests consist of specific information or questions, such as "Please tell me the weather for next week." The input data is processed by the terminal as basic text data.

[0083] Step 2:

[0084] The terminal sends the entered request to the server. In this process, data is sent to the server via an encrypted, secure connection. The input data is converted into digital packets according to a specific protocol and reaches the server via the network.

[0085] Step 3:

[0086] The server processes incoming requests. First, it analyzes the request using a generative AI model. The input here is text data, and natural language processing libraries are used to extract the intent and keywords of the request. For example, keywords such as "next week" and "weather" are identified from the request sentence to understand the user's intent.

[0087] Step 4:

[0088] The server queries external information resources based on the analysis results. It collects relevant information through API calls and database queries to obtain the necessary data. At this stage, for example, it might query a weather database based on keywords obtained through the analysis to retrieve information about the weather for the following week.

[0089] Step 5:

[0090] The server integrates the acquired data and generates a response to return to the user. This response generation uses natural language generation technology to construct information using contextually natural expressions. For example, it generates easy-to-understand expressions such as, "It is expected to be sunny nationwide next week."

[0091] Step 6:

[0092] The server sends the generated response back to the terminal. The response data is encrypted again and transferred to the terminal via secure communication. The output here is the specific response message.

[0093] Step 7:

[0094] The user receives the response via the terminal and confirms it on the screen. The terminal displays the received response in the user interface, making the information available to the user. If necessary, user feedback is collected and sent back to the server to contribute to subsequent process improvements.

[0095] (Application Example 1)

[0096] 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."

[0097] Conventional digital assistant systems rely on limited information sources when generating responses to user input, making it difficult to provide specific and practical information, particularly regarding security. Furthermore, achieving rapid and accurate responses tailored to user needs, such as checking security status and providing relevant information, remains a challenge.

[0098] 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.

[0099] In this invention, the server includes a device for receiving user instructions, a device for analyzing the received instructions using language processing techniques, and a device for searching a data set based on the analyzed data and identifying relevant data. This allows the user to quickly receive detailed and contextually relevant responses based on integrated information by entering security questions.

[0100] A "device that receives user instructions" is a device that receives input information from the user and sends it to the next processing stage.

[0101] A "device that analyzes using language processing technology" is a device that implements technology to interpret natural language received from a user and extract intent and important information.

[0102] A "device for exploring data sets and identifying related data" is a device that searches for necessary information from a large amount of data and identifies information relevant to the user's request.

[0103] "Integrated information-based detailed and contextual responses" are responses that combine data obtained from multiple sources to provide specific and relevant answers to user questions.

[0104] A "device for transmitting to an instrument" is a device that sends the generated response to the user's device for display.

[0105] A "security question" is an inquiry made by a user seeking information about their own safety and protection.

[0106] "Security management information" refers to data that includes the status of security measures being implemented and related information.

[0107] The system for implementing this invention begins with the user entering security questions through their terminal. The terminal receives this input and sends it to a server. The server uses natural language processing techniques to analyze this input and extract the user's intent and important information. Existing language processing software libraries such as spaCy and BERT can be used for natural language processing.

[0108] The server searches the database based on the analyzed information and identifies relevant security management information. The database may include data on security measures and security devices installed by the user. The identified information is integrated with additional contextual information, and the server uses this to generate a detailed and contextual response.

[0109] This response is sent to the user's device, through which the user can check the security status and receive specific instructions. For example, if the user asks, "I want to see the camera footage from my front door this morning, how can I do that?", the system will guide them by saying, "Open your security app and select the relevant time period from the list of recorded footage."

[0110] By using a generative AI model, the initial prompt can be set to an example like this: "How can I find information about my home security system?" This allows the system to provide the most appropriate answer tailored to the user's specific needs.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user enters security questions in natural language using a device. This input is recorded on the device in text format and prepared to be sent to the server. The input consists of the user's questions, which are sent for analysis in the next step.

[0114] Step 2:

[0115] The terminal sends user input to the server. The server retrieves the received text data and analyzes it using natural language processing techniques. In this process, it receives text data as input and uses a natural language processing library to extract intent and keywords. The output is the analyzed intent and extracted keywords.

[0116] Step 3:

[0117] The server searches the database based on the analyzed keywords and retrieves relevant security management information. The database contains information on security devices and measures, and search queries are generated to identify relevant data. Keywords are taken as input, and relevant information is obtained as output.

[0118] Step 4:

[0119] The server generates detailed responses by supplementing relevant information obtained through searches based on context. In this process, a generative AI model is used to combine the information and form natural language responses to the user's questions. The input is relevant information, and the output is a detailed, context-based response.

[0120] Step 5:

[0121] The server sends the generated response to the terminal and displays it on the user's screen. The user can then review it and take appropriate security action. Input is a detailed response, and output is information presented to the user. In this step, the user may receive specific instructions such as, "Open your security app and select the relevant time period from the list of recorded video footage."

[0122] 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.

[0123] This invention aims to realize a digital assistant system that analyzes user input and considers not only the information provided but also the user's emotions. This system provides highly accurate services based on communication between the server, terminal, and user.

[0124] The user enters a question using a terminal. The terminal sends the input to the server, which receives this data. The server analyzes the user's input by employing both natural language processing (NLP) technology and an emotion engine. Specifically, it uses the NLP engine to extract keywords from the text and understand the context, while simultaneously recognizing the user's emotions with the emotion engine.

[0125] Based on the analyzed information, the server searches its internal database to identify relevant information. During this process, it also considers emotional information to design the optimal response tailored to the user's current situation. This information is generated as a natural language response, taking emotional aspects into account.

[0126] The generated response is sent from the server to the terminal and presented to the user. This response includes contextual additional information and considerations based on the perceived emotions. For example, if the user expresses dissatisfaction, the response will incorporate particularly flexible approaches and follow-up procedures based on that information.

[0127] As a concrete example, suppose a user enters into their device, "I want to return this immediately, but the process is taking too long and I'm having trouble." The server analyzes this input and identifies the keywords and emotions associated with "return process" and "having trouble." Next, the server generates a response that includes "prompt action" and "an apology," providing solutions to alleviate the user's anxiety.

[0128] This system aims to enhance not only the accuracy of information but also the emotional satisfaction of users, enabling more multifaceted and flexible customer service.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user enters a question and their feelings at the time into the device and presses the send button. The device then sends this as a data packet to the server.

[0132] Step 2:

[0133] The server uses a natural language processing engine to analyze the received data. It extracts keywords from the text and performs processing to understand the context.

[0134] Step 3:

[0135] The server uses an emotion engine to recognize emotions from user input. Emotions are determined based on the tone of the text and specific vocabulary.

[0136] Step 4:

[0137] The server searches the database for the most relevant information based on the analyzed keywords and sentiment data. Search results are generated using optimized queries to ensure speed and accuracy.

[0138] Step 5:

[0139] The server integrates search results and sentiment analysis results to generate a natural language response. In this process, the system is designed to include responses and considerations tailored to the user's situation, based on the recognized emotions.

[0140] Step 6:

[0141] The server sends the generated response to the terminal. The terminal displays this on the user screen, presented through an interface designed with user visibility in mind.

[0142] Step 7:

[0143] Users review the provided responses and can ask additional questions or decide on their course of action based on that information. They can also provide feedback on the responses offered.

[0144] Step 8:

[0145] The terminal sends user feedback to the server. The server receives this feedback and uses it, along with accumulated data, to improve the system.

[0146] (Example 2)

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

[0148] Traditional digital assistants fail to consider user emotions during information retrieval, merely presenting information. This creates a challenge in providing flexible and appropriate responses based on the user's emotional state. Understanding and responding appropriately to user emotions contributes to improved customer satisfaction, but conventional technologies are insufficient in this regard.

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

[0150] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions based on the analyzed information. This enables flexible and appropriate information provision and responses that take the user's emotions into consideration.

[0151] "User input" refers to data that a user sends to the system via a terminal for information retrieval or to ask questions.

[0152] "Natural language processing technology" refers to the technology that enables computers to understand and interpret text written in human language.

[0153] An "analysis device" is a device that uses natural language processing technology to analyze received data and has the function of extracting information and recognizing emotions.

[0154] "Emotion recognition" is the process of identifying a user's emotions and feelings based on the wording and context included in their input.

[0155] "Internal information sources" refer to databases and knowledge resources stored within the system, which are used to provide relevant information based on analysis results.

[0156] A "response generation device" is a device that provides explanations and instructions to the user in natural language based on acquired information.

[0157] A "terminal" is a device used by a user to input information and receive responses from a server; mobile phones and computers are common examples.

[0158] This digital assistant system analyzes user input and provides responses that take emotions into consideration. The system primarily operates through the coordinated operation of a server and terminals.

[0159] Users enter questions and requests using an internet-connected device. This device can be a smartphone or personal computer, and users can send information using a keyboard or voice input. A specific example would be a request such as, "Please tell me the status of the item I ordered yesterday."

[0160] The terminal sends the data entered by the user to the server. The server applies natural language processing technology to the received data. Specifically, it uses software packages such as "spaCy" and "Google Cloud Natural Language API" to extract keywords and understand the context of the input text. The server can also use tools such as "IBM Watson Tone Analyzer" to recognize the user's emotions from the input.

[0161] The server analyzes the information and considers the emotions involved, then searches internal sources to identify relevant information. This search uses SQL databases or NoSQL databases (e.g., MySQL®, MongoDB). Based on the acquired information, the server generates a response in natural language. By using a generative AI model, it can construct contextually appropriate, natural conversational responses and present emotionally sensitive content to the user. For example, a possible response might be, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment."

[0162] An example of a prompt message would be, "Analyze the user's emotions and suggest the best course of action regarding the return process." This system enables the provision of highly accurate service that takes user emotions into consideration.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users enter questions and requests using their devices. Input can be in text or voice format, and users enter the information through an application on their device. For example, they might enter something like, "I want to check the status of my product." This input data is then prepared to be sent from the device to the server.

[0166] Step 2:

[0167] The terminal sends user input to the server. The transmitted data is packaged as text data via the HTTP protocol. Specifically, this includes the communication process from input text to its arrival at the server. As a result, the server receives the user's input data.

[0168] Step 3:

[0169] The server analyzes the user's input data. Here, a natural language processing engine is used to extract keywords from the input and understand the context. Tools such as "spaCy" and "Google Cloud Natural Language API" are used. Important words and phrases are identified from the input text and used as search criteria for the internal data. The information extracted through analysis is then passed on to the next processing step.

[0170] Step 4:

[0171] The server performs emotion recognition based on the analysis results. It infers the user's emotional state from the input data via an emotion engine. The tool used is "IBM Watson Tone Analyzer," which detects emotions from word choice and phrases. This process generates analysis data that includes the user's emotions.

[0172] Step 5:

[0173] The server searches for internal information sources based on the analyzed data. It utilizes SQL or NoSQL databases to identify information relevant to the user's request. For example, it extracts records corresponding to product order status from the database. This search result yields a useful set of information.

[0174] Step 6:

[0175] The server uses a generative AI model to generate natural language responses, taking into account the information and sentiment data it receives. This generates contextually appropriate responses that take the user's emotions into consideration. For example, a response like, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment," might be generated. This response is then produced as the final output.

[0176] Step 7:

[0177] The server sends the generated response to the terminal. The response data is sent in text format using the HTTP protocol. The terminal receiving this response completes the information transfer to the user.

[0178] Step 8:

[0179] The terminal receives a response from the server and displays it to the user. The user confirms this and receives information relevant to the purpose of their input. This fulfills the user's information request, and the interaction is complete.

[0180] (Application Example 2)

[0181] 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".

[0182] Electronic payment services require prompt and appropriate responses to user emotions such as anxiety and dissatisfaction. However, conventional technologies have struggled to generate flexible responses that take user emotions into account, limiting the improvement of the user experience. Solving this problem is essential.

[0183] 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.

[0184] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions. This makes it possible to generate an appropriate response that takes the user's emotions into consideration.

[0185] A "device that receives user input" refers to hardware or software that acquires data entered by a user and transfers it to other system components.

[0186] "Natural language processing technology" is a technology that enables computers to understand and process human language, making it possible to extract keywords and interpret context.

[0187] A "device that recognizes user emotions" is hardware or software that can analyze and determine a user's emotional state based on input data.

[0188] An "internal storage medium" is a storage device that stores analyzed information and related data, and is used to retrieve necessary data based on that information.

[0189] A "device that generates responses in natural language" is a system component that constructs responses in a human-understandable format based on user input data and related information.

[0190] A "device that transmits to the user's terminal" is a device equipped with communication means for transferring the generated response to the terminal used by the user and displaying it to the user.

[0191] This invention presents a digital assistant system that takes user emotions into consideration in electronic payment services. This system is implemented by receiving input from the user's terminal and processing it on a server. Specifically, data entered by the user is transmitted from the terminal to the server via the network. The server first analyzes the user's input data using an NLP engine (natural language processing engine, e.g., spaCy) and extracts keywords. This analysis is performed to understand the content of the input data and grasp the appropriate context.

[0192] Furthermore, the server uses a sentiment analysis engine (e.g., TextBlob) to recognize the user's emotional state. This analysis identifies emotional data such as anxiety or satisfaction. Based on the analyzed keywords and emotional information, the server searches its internal storage medium (database) to identify relevant information.

[0193] The server then generates an appropriate response in natural language based on the user's input and emotions. This response generation includes flexible responses that take the user's emotions into consideration, and may include words of encouragement or apology. The generated response is sent from the server to the user's terminal and presented to the user.

[0194] For example, if a user enters "I'm worried because my payment didn't go through," the server analyzes the input and identifies the keywords "payment" and "anxiety," along with the emotions involved. It then generates a flexible response to alleviate the anxiety, along with a quick solution, and sends it to the user.

[0195] An example of a prompt message is: "The user is feeling anxious because their payment didn't go through. Please suggest flexible solutions to alleviate their anxiety." This can improve the user experience.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user enters questions or comments about electronic payments through the terminal. The terminal sends this input data to the server in its original format. The input is text data and is sent directly to the server via the network.

[0199] Step 2:

[0200] The server receives user input and analyzes the data using a natural language processing engine. The purpose of the analysis is to extract keywords from the text data and understand the context. Since the input is text data, the output will be the extracted keywords and contextual information. Specifically, the server starts an NLP engine (e.g., spaCy) and analyzes the input data.

[0201] Step 3:

[0202] The server uses an emotion analysis engine to recognize the user's emotions based on the analysis results. The input here is the analysis results from step 2, and emotion information is extracted based on that. The output is data on the user's emotional state. The server uses an emotion analysis engine such as TextBlob to identify the emotion, referring to the extracted keywords and context.

[0203] Step 4:

[0204] The server searches its internal storage media based on analyzed keywords and sentiment information. The input consists of keywords and sentiment information, which are used to find relevant records. The output is a dataset of related information. It generates database queries and performs high-speed searches for the relevant information.

[0205] Step 5:

[0206] The server generates natural language responses based on integrated information and sentiment information. The input for this step is the retrieved information and sentiment information. The output is a text response in a human-readable format. A generative AI model is used to generate responses, creating flexible responses that take context and sentiment into account.

[0207] Step 6:

[0208] The text generated as a response is sent from the server to the user's terminal and displayed. The input is the natural language response text generated by the server, and the output is the information displayed on the user's terminal. The terminal processes the received text and displays it on the screen.

[0209] 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.

[0210] Data generation model 58 is a type of 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention aims to efficiently perform communication and data processing between users, terminals, and servers in order to realize a digital assistant system.

[0226] First, the user enters a question using a terminal. This input is in natural language, and the terminal sends the input data to the server. The server receives this input and analyzes the text using natural language processing technology. This allows the server to understand the intent of the input and important keywords, and then retrieve relevant information from the database based on the analysis results.

[0227] The server integrates data gathered from multiple sources, summarizing and supplementing it as needed. This allows it to select the most relevant information for the user's question and generate a response. This response generation is designed to consider context and be expressed in more natural language.

[0228] The generated response is sent from the server to the terminal and displayed on the user's screen. The user can use this information to resolve the problem and decide on their next course of action. Furthermore, the user can provide feedback on the response, which is sent to the server and used to improve future responses.

[0229] As a concrete example, suppose a user enters "How do I return an item?" into their terminal. In this case, the server analyzes "return procedure" as a keyword and searches for information in the relevant FAQ database. Based on the results, it can generate a step-by-step guide and provide the user with a specific response such as, "First, put the purchased item back in its original packaging and use the registered shipping carrier."

[0230] This system is an invention that can improve user satisfaction and streamline inquiry processing by enabling quick and accurate responses.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user enters a question into the terminal and presses the send button. The terminal then packages this input data into packets and sends them to the server over the network.

[0234] Step 2:

[0235] The server analyzes the received data and passes the input to a natural language processing engine to understand the user's intent. The NLP engine analyzes the text and extracts keywords and the user's intent.

[0236] Step 3:

[0237] The server searches the FAQ database and product information database based on the extracted information. It executes database queries to efficiently find related records.

[0238] Step 4:

[0239] The server integrates search results and organizes the information that should be provided to the user. If necessary, it summarizes the information and links it to more detailed information.

[0240] Step 5:

[0241] The server generates a natural language response based on the integrated information. The response is optimized to be in a format that is easy for the user to understand.

[0242] Step 6:

[0243] The server sends the generated response data to the terminal. The terminal displays this received data on its screen so that the user can verify it.

[0244] Step 7:

[0245] The user reviews the information presented, asks further questions as needed, and takes subsequent actions based on the information provided.

[0246] Step 8:

[0247] Users can provide feedback on the usefulness of the response. The terminal sends this feedback to the server, which helps improve the system.

[0248] (Example 1)

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

[0250] Conventional digital assistant systems often suffer from delayed or inaccurate responses to user requests. This is because natural language processing techniques and the identification of relevant data from external information resources are not performed efficiently. Furthermore, the generated language expressions often do not adequately consider the context, failing to provide useful information to the user.

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

[0252] In this invention, the server includes means for receiving a user request and analyzing the request using data transformation technology, means for identifying relevant data from external information resources based on the analyzed information, and means for integrating the identified data and generating a linguistic expression that indicates the request. This makes it possible to respond quickly and accurately to user requests and provide contextually relevant and useful information.

[0253] A "user" refers to an individual or group that makes a request to the system.

[0254] A "request" refers to an inquiry made by a user to the system regarding the acquisition of information or services.

[0255] "Data transformation technology" refers to technical methods used to analyze and understand user requirements.

[0256] "Means of analysis" refers to the function that utilizes data transformation techniques to understand received requests.

[0257] "External information resources" refer to databases and information services that provide relevant data located outside the system.

[0258] "Means for identifying relevant data" refers to the function of finding necessary data from external information resources based on the analysis results.

[0259] "Means of integrating data" refers to the function of gathering identified related data into a single, combined piece of information.

[0260] "Means for generating linguistic expressions" refers to a function that generates appropriate responses for the user based on integrated data.

[0261] "Communication device" refers to a terminal or device used to transmit generated linguistic expressions to the user.

[0262] This invention is a digital assistant system that enables efficient data exchange and information provision between users, terminals, and servers.

[0263] The user first inputs information using a terminal. This terminal is an information processing device such as a smartphone or computer, and can accept text or voice input. For example, the user might input the request, "Please tell me the weather for next week," into the terminal.

[0264] The terminal's role is to send received requests to the server. Secure communication protocols over the internet are used for transmission. The server receives the requests and analyzes them using generative AI models. This analysis uses Python's natural language processing library to extract the intent and important keywords of the requests.

[0265] Next, the server queries external information resources based on the analysis results. These external resources include, for example, information sources that provide weather forecasts and databases that store technical documents. The server identifies and integrates the relevant information to construct the optimal response for the user. The response is generated in natural language, such as, "Next week will be sunny nationwide with an average temperature of 25 degrees Celsius."

[0266] The generated response is sent to the terminal, which then displays it to the user. The user can then make decisions based on the information provided. Furthermore, the user can input feedback on the response through the terminal, which is then returned to the server. The server uses this feedback as data to improve the quality of future information provision.

[0267] As a concrete example, the user's prompt might be in the form of "Please tell me about the return procedure," and the AI ​​model would analyze this information and respond by returning relevant guidelines.

[0268] This invention provides a system that responds quickly and accurately to user requests by integrating natural language processing and data integration technologies.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user inputs requests using natural language via a terminal. These requests consist of specific information or questions, such as "Please tell me the weather for next week." The input data is processed by the terminal as basic text data.

[0272] Step 2:

[0273] The terminal sends the entered request to the server. In this process, data is sent to the server via an encrypted, secure connection. The input data is converted into digital packets according to a specific protocol and reaches the server via the network.

[0274] Step 3:

[0275] The server processes the received request. First, it analyzes the request using a generative AI model. The input here is text data, and it utilizes a natural language processing library to extract the intent and keywords of the request. For example, it identifies keywords such as "next week" and "weather" from the request sentence to understand the user's intent.

[0276] Step 4:

[0277] Based on the analysis results, the server queries external information resources. To obtain the necessary data, it collects relevant information through API calls or database queries. At this stage, based on the keywords obtained from the analysis, for example, it queries a weather database to obtain information about the weather next week.

[0278] Step 5:

[0279] The server integrates the acquired data and generates a response to return to the user. For this response generation, it uses natural language generation technology to construct information using natural expressions based on the context. For example, an easy-to-understand expression such as "It is expected to be sunny across the country next week" is generated.

[0280] Step 6:

[0281] The server sends the generated response back to the terminal. The response data is encrypted again and transferred to the terminal through secure communication. The output here is a specific response message.

[0282] Step 7:

[0283] The user receives the response via the terminal and checks it on the screen. The terminal displays the received response on the user interface to enable the user to utilize the information. If necessary, it collects feedback from the user and sends it back to the server again to contribute to subsequent process improvement.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0286] Conventional digital assistant systems rely on limited information sources when generating responses to user inputs, making it particularly difficult to provide specific and practical information regarding security. Additionally, it is a challenge to achieve quick and accurate responses according to user needs in checking security situations and providing related information.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes a device that receives a user's instruction, a device that analyzes the received instruction using language processing technology, and a device that searches a data set based on the analyzed data and identifies relevant data. Thereby, the user can quickly receive a detailed and context-based response based on integrated information by inputting a security-related question.

[0289] The "device that receives a user's instruction" is a device that receives input information from the user and sends it to the next processing stage.

[0290] The "device that analyzes using language processing technology" is a device that implements a technology for interpreting the natural language received from the user and extracting the intention and important information.

[0291] The "device that searches a data set and identifies relevant data" is a device that searches for necessary information from a large amount of data and specifies information related to the user's request.

[0292] The "detailed and context-based response based on integrated information" is a response that combines data obtained from multiple information sources and provides a specific and highly relevant answer to the user's question.

[0293] A "device for transmitting to an instrument" is a device that sends the generated response to the user's device for display.

[0294] A "security question" is an inquiry made by a user seeking information about their own safety and protection.

[0295] "Security management information" refers to data that includes the status of security measures being implemented and related information.

[0296] The system for implementing this invention begins with the user entering security questions through their terminal. The terminal receives this input and sends it to a server. The server uses natural language processing techniques to analyze this input and extract the user's intent and important information. Existing language processing software libraries such as spaCy and BERT can be used for natural language processing.

[0297] The server searches the database based on the analyzed information and identifies relevant security management information. The database may include data on security measures and security devices installed by the user. The identified information is integrated with additional contextual information, and the server uses this to generate a detailed and contextual response.

[0298] This response is sent to the user's device, through which the user can check the security status and receive specific instructions. For example, if the user asks, "I want to see the camera footage from my front door this morning, how can I do that?", the system will guide them by saying, "Open your security app and select the relevant time period from the list of recorded footage."

[0299] By using the generative AI model, example prompt texts can be set as follows for initial settings. "Please teach me how to check the information about your home security system." With this, the system can provide an optimal answer according to the specific needs of the user.

[0300] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0301] Step 1:

[0302] The user uses the terminal to input a security-related question in natural language. This input is recorded in the terminal in text form and prepared to be sent to the server. The input is the user's question, which is sent for analysis in the next step.

[0303] Step 2:

[0304] The terminal sends the user's input to the server. The server obtains the received text data and performs analysis using natural language processing technology. At this time, it receives text data as input and extracts the intention and keywords using a natural language processing library. The output is the analyzed intention and the extracted keywords.

[0305] Step 3:

[0306] The server searches the database based on the analyzed keywords and retrieves relevant security management information. The database contains information about security devices and measures, and a search query is generated to identify relevant data. Keywords are used as input, and relevant information is obtained as output.

[0307] Step 4:

[0308] The server generates detailed responses by supplementing relevant information obtained through searches based on context. In this process, a generative AI model is used to combine the information and form natural language responses to the user's questions. The input is relevant information, and the output is a detailed, context-based response.

[0309] Step 5:

[0310] The server sends the generated response to the terminal and displays it on the user's screen. The user can then review it and take appropriate security action. Input is a detailed response, and output is information presented to the user. In this step, the user may receive specific instructions such as, "Open your security app and select the relevant time period from the list of recorded video footage."

[0311] 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.

[0312] This invention aims to realize a digital assistant system that analyzes user input and considers not only the information provided but also the user's emotions. This system provides highly accurate services based on communication between the server, terminal, and user.

[0313] The user enters a question using a terminal. The terminal sends the input to the server, which receives this data. The server analyzes the user's input by employing both natural language processing (NLP) technology and an emotion engine. Specifically, it uses the NLP engine to extract keywords from the text and understand the context, while simultaneously recognizing the user's emotions with the emotion engine.

[0314] Based on the analyzed information, the server searches its internal database to identify relevant information. During this process, it also considers emotional information to design the optimal response tailored to the user's current situation. This information is generated as a natural language response, taking emotional aspects into account.

[0315] The generated response is sent from the server to the terminal and presented to the user. This response includes contextual additional information and considerations based on the perceived emotions. For example, if the user expresses dissatisfaction, the response will incorporate particularly flexible approaches and follow-up procedures based on that information.

[0316] As a concrete example, suppose a user enters into their device, "I want to return this immediately, but the process is taking too long and I'm having trouble." The server analyzes this input and identifies the keywords and emotions associated with "return process" and "having trouble." Next, the server generates a response that includes "prompt action" and "an apology," providing solutions to alleviate the user's anxiety.

[0317] This system aims to enhance not only the accuracy of information but also the emotional satisfaction of users, enabling more multifaceted and flexible customer service.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user enters a question and their feelings at the time into the device and presses the send button. The device then sends this as a data packet to the server.

[0321] Step 2:

[0322] The server uses a natural language processing engine to analyze the received data. It extracts keywords from the text and performs processing to understand the context.

[0323] Step 3:

[0324] The server uses an emotion engine to recognize emotions from user input. Emotions are determined based on the tone of the text and specific vocabulary.

[0325] Step 4:

[0326] The server searches the database for the most relevant information based on the analyzed keywords and sentiment data. Search results are generated using optimized queries to ensure speed and accuracy.

[0327] Step 5:

[0328] The server integrates search results and sentiment analysis results to generate a natural language response. In this process, the system is designed to include responses and considerations tailored to the user's situation, based on the recognized emotions.

[0329] Step 6:

[0330] The server sends the generated response to the terminal. The terminal displays this on the user screen, presented through an interface designed with user visibility in mind.

[0331] Step 7:

[0332] Users review the provided responses and can ask additional questions or decide on their course of action based on that information. They can also provide feedback on the responses offered.

[0333] Step 8:

[0334] The terminal sends user feedback to the server. The server receives this feedback and uses it, along with accumulated data, to improve the system.

[0335] (Example 2)

[0336] 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".

[0337] Traditional digital assistants fail to consider user emotions during information retrieval, merely presenting information. This creates a challenge in providing flexible and appropriate responses based on the user's emotional state. Understanding and responding appropriately to user emotions contributes to improved customer satisfaction, but conventional technologies are insufficient in this regard.

[0338] 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.

[0339] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions based on the analyzed information. This enables flexible and appropriate information provision and responses that take the user's emotions into consideration.

[0340] "User input" refers to data that a user sends to the system via a terminal for information retrieval or to ask questions.

[0341] "Natural language processing technology" refers to the technology that enables computers to understand and interpret text written in human language.

[0342] An "analysis device" is a device that uses natural language processing technology to analyze received data and has the function of extracting information and recognizing emotions.

[0343] "Emotion recognition" is the process of identifying a user's emotions and feelings based on the wording and context included in their input.

[0344] "Internal information sources" refer to databases and knowledge resources stored within the system, which are used to provide relevant information based on analysis results.

[0345] A "response generation device" is a device that provides explanations and instructions to the user in natural language based on acquired information.

[0346] A "terminal" is a device used by a user to input information and receive responses from a server; mobile phones and computers are common examples.

[0347] This digital assistant system analyzes user input and provides responses that take emotions into consideration. The system primarily operates through the coordinated operation of a server and terminals.

[0348] Users enter questions and requests using an internet-connected device. This device can be a smartphone or personal computer, and users can send information using a keyboard or voice input. A specific example would be a request such as, "Please tell me the status of the item I ordered yesterday."

[0349] The terminal sends the data entered by the user to the server. The server applies natural language processing techniques to the received data. Specifically, it utilizes software packages such as "spaCy" and "Google Cloud Natural Language API" to extract keywords and understand the context of the input text. The server can also use tools like "IBM Watson Tone Analyzer" to recognize the user's emotions from the input.

[0350] The server analyzes the information and considers the emotions involved, then searches internal sources to identify relevant information. This search uses SQL or NoSQL databases (e.g., MySQL, MongoDB). Based on the retrieved information, the server generates a response in natural language. By using a generative AI model, it can construct contextually appropriate, natural conversational responses and present emotionally sensitive content to the user. For example, a possible response might be, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment."

[0351] An example of a prompt message would be, "Analyze the user's emotions and suggest the best course of action regarding the return process." This system enables the provision of highly accurate service that takes user emotions into consideration.

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

[0353] Step 1:

[0354] Users enter questions and requests using their devices. Input can be in text or voice format, and users enter the information through an application on their device. For example, they might enter something like, "I want to check the status of my product." This input data is then prepared to be sent from the device to the server.

[0355] Step 2:

[0356] The terminal sends user input to the server. The transmitted data is packaged as text data via the HTTP protocol. Specifically, this includes the communication process from input text to its arrival at the server. As a result, the server receives the user's input data.

[0357] Step 3:

[0358] The server analyzes the user's input data. Here, a natural language processing engine is used to extract keywords from the input and understand the context. Tools such as "spaCy" and "Google Cloud Natural Language API" are used. Important words and phrases are identified from the input text and used as search criteria for the internal data. The information extracted through analysis is then passed on to the next processing step.

[0359] Step 4:

[0360] The server performs emotion recognition based on the analysis results. It infers the user's emotional state from the input data via an emotion engine. The tool used is "IBM Watson Tone Analyzer," which detects emotions from word choice and phrases. This process generates analysis data that includes the user's emotions.

[0361] Step 5:

[0362] The server searches for internal information sources based on the analyzed data. It utilizes SQL or NoSQL databases to identify information relevant to the user's request. For example, it extracts records corresponding to product order status from the database. This search result yields a useful set of information.

[0363] Step 6:

[0364] The server uses a generative AI model to generate natural language responses, taking into account the information and sentiment data it receives. This generates contextually appropriate responses that take the user's emotions into consideration. For example, a response like, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment," might be generated. This response is then produced as the final output.

[0365] Step 7:

[0366] The server sends the generated response to the terminal. The response data is sent in text format using the HTTP protocol. The terminal receiving this response completes the information transfer to the user.

[0367] Step 8:

[0368] The terminal receives a response from the server and displays it to the user. The user confirms this and receives information relevant to the purpose of their input. This fulfills the user's information request, and the interaction is complete.

[0369] (Application Example 2)

[0370] 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."

[0371] Electronic payment services require prompt and appropriate responses to user emotions such as anxiety and dissatisfaction. However, conventional technologies have struggled to generate flexible responses that take user emotions into account, limiting the improvement of the user experience. Solving this problem is essential.

[0372] 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.

[0373] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions. This makes it possible to generate an appropriate response that takes the user's emotions into consideration.

[0374] A "device that receives user input" refers to hardware or software that acquires data entered by a user and transfers it to other system components.

[0375] "Natural language processing technology" is a technology that enables computers to understand and process human language, making it possible to extract keywords and interpret context.

[0376] A "device that recognizes user emotions" is hardware or software that can analyze and determine a user's emotional state based on input data.

[0377] An "internal storage medium" is a storage device that stores analyzed information and related data, and is used to retrieve necessary data based on that information.

[0378] A "device that generates responses in natural language" is a system component that constructs responses in a human-understandable format based on user input data and related information.

[0379] A "device that transmits to the user's terminal" is a device equipped with communication means for transferring the generated response to the terminal used by the user and displaying it to the user.

[0380] This invention presents a digital assistant system that takes user emotions into consideration in electronic payment services. This system is implemented by receiving input from the user's terminal and processing it on a server. Specifically, data entered by the user is transmitted from the terminal to the server via the network. The server first analyzes the user's input data using an NLP engine (natural language processing engine, e.g., spaCy) and extracts keywords. This analysis is performed to understand the content of the input data and grasp the appropriate context.

[0381] Furthermore, the server uses a sentiment analysis engine (e.g., TextBlob) to recognize the user's emotional state. This analysis identifies emotional data such as anxiety or satisfaction. Based on the analyzed keywords and emotional information, the server searches its internal storage medium (database) to identify relevant information.

[0382] The server then generates an appropriate response in natural language based on the user's input and emotions. This response generation includes flexible responses that take the user's emotions into consideration, and may include words of encouragement or apology. The generated response is sent from the server to the user's terminal and presented to the user.

[0383] For example, if a user enters "I'm worried because my payment didn't go through," the server analyzes the input and identifies the keywords "payment" and "anxiety," along with the emotions involved. It then generates a flexible response to alleviate the anxiety, along with a quick solution, and sends it to the user.

[0384] An example of a prompt message is: "The user is feeling anxious because their payment didn't go through. Please suggest flexible solutions to alleviate their anxiety." This can improve the user experience.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user enters questions or comments about electronic payments through the terminal. The terminal sends this input data to the server in its original format. The input is text data and is sent directly to the server via the network.

[0388] Step 2:

[0389] The server receives user input and analyzes the data using a natural language processing engine. The purpose of the analysis is to extract keywords from the text data and understand the context. Since the input is text data, the output will be the extracted keywords and contextual information. Specifically, the server starts an NLP engine (e.g., spaCy) and analyzes the input data.

[0390] Step 3:

[0391] The server uses an emotion analysis engine to recognize the user's emotions based on the analysis results. The input here is the analysis results from step 2, and emotion information is extracted based on that. The output is data on the user's emotional state. The server uses an emotion analysis engine such as TextBlob to identify the emotion, referring to the extracted keywords and context.

[0392] Step 4:

[0393] The server searches its internal storage media based on analyzed keywords and sentiment information. The input consists of keywords and sentiment information, which are used to find relevant records. The output is a dataset of related information. It generates database queries and performs high-speed searches for the relevant information.

[0394] Step 5:

[0395] The server generates natural language responses based on integrated information and sentiment information. The input for this step is the retrieved information and sentiment information. The output is a text response in a human-readable format. A generative AI model is used to generate responses, creating flexible responses that take context and sentiment into account.

[0396] Step 6:

[0397] The text generated as a response is sent from the server to the user's terminal and displayed. The input is the natural language response text generated by the server, and the output is the information displayed on the user's terminal. The terminal processes the received text and displays it on the screen.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] This invention aims to efficiently perform communication and data processing between users, terminals, and servers in order to realize a digital assistant system.

[0415] First, the user enters a question using a terminal. This input is in natural language, and the terminal sends the input data to the server. The server receives this input and analyzes the text using natural language processing technology. This allows the server to understand the intent of the input and important keywords, and then retrieve relevant information from the database based on the analysis results.

[0416] The server integrates data gathered from multiple sources, summarizing and supplementing it as needed. This allows it to select the most relevant information for the user's question and generate a response. This response generation is designed to consider context and be expressed in more natural language.

[0417] The generated response is sent from the server to the terminal and displayed on the user's screen. The user can use this information to resolve the problem and decide on their next course of action. Furthermore, the user can provide feedback on the response, which is sent to the server and used to improve future responses.

[0418] As a concrete example, suppose a user enters "How do I return an item?" into their terminal. In this case, the server analyzes "return procedure" as a keyword and searches for information in the relevant FAQ database. Based on the results, it can generate a step-by-step guide and provide the user with a specific response such as, "First, put the purchased item back in its original packaging and use the registered shipping carrier."

[0419] This system is an invention that can improve user satisfaction and streamline inquiry processing by enabling quick and accurate responses.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The user enters a question into the terminal and presses the send button. The terminal then packages this input data into packets and sends them to the server over the network.

[0423] Step 2:

[0424] The server analyzes the received data and passes the input to a natural language processing engine to understand the user's intent. The NLP engine analyzes the text and extracts keywords and the user's intent.

[0425] Step 3:

[0426] The server searches the FAQ database and product information database based on the extracted information. It executes database queries to efficiently find related records.

[0427] Step 4:

[0428] The server integrates search results and organizes the information that should be provided to the user. If necessary, it summarizes the information and links it to more detailed information.

[0429] Step 5:

[0430] The server generates a natural language response based on the integrated information. The response is optimized to be in a format that is easy for the user to understand.

[0431] Step 6:

[0432] The server sends the generated response data to the terminal. The terminal displays this received data on its screen so that the user can verify it.

[0433] Step 7:

[0434] The user reviews the information presented, asks further questions as needed, and takes subsequent actions based on the information provided.

[0435] Step 8:

[0436] Users can provide feedback on the usefulness of the response. The terminal sends this feedback to the server, which helps improve the system.

[0437] (Example 1)

[0438] 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."

[0439] Conventional digital assistant systems often suffer from delayed or inaccurate responses to user requests. This is because natural language processing techniques and the identification of relevant data from external information resources are not performed efficiently. Furthermore, the generated language expressions often do not adequately consider the context, failing to provide useful information to the user.

[0440] 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.

[0441] In this invention, the server includes means for receiving a user request and analyzing the request using data transformation technology, means for identifying relevant data from external information resources based on the analyzed information, and means for integrating the identified data and generating a linguistic expression that indicates the request. This makes it possible to respond quickly and accurately to user requests and provide contextually relevant and useful information.

[0442] A "user" refers to an individual or group that makes a request to the system.

[0443] A "request" refers to an inquiry made by a user to the system regarding the acquisition of information or services.

[0444] "Data transformation technology" refers to technical methods used to analyze and understand user requirements.

[0445] "Means of analysis" refers to the function that utilizes data transformation techniques to understand received requests.

[0446] "External information resources" refer to databases and information services that provide relevant data located outside the system.

[0447] "Means for identifying relevant data" refers to the function of finding necessary data from external information resources based on the analysis results.

[0448] "Means of integrating data" refers to the function of gathering identified related data into a single, combined piece of information.

[0449] "Means for generating linguistic expressions" refers to a function that generates appropriate responses for the user based on integrated data.

[0450] "Communication device" refers to a terminal or device used to transmit generated linguistic expressions to the user.

[0451] This invention is a digital assistant system that enables efficient data exchange and information provision between users, terminals, and servers.

[0452] The user first inputs information using a terminal. This terminal is an information processing device such as a smartphone or computer, and can accept text or voice input. For example, the user might input the request, "Please tell me the weather for next week," into the terminal.

[0453] The terminal's role is to send received requests to the server. Secure communication protocols over the internet are used for transmission. The server receives the requests and analyzes them using generative AI models. This analysis uses Python's natural language processing library to extract the intent and important keywords of the requests.

[0454] Next, the server queries external information resources based on the analysis results. These external resources include, for example, information sources that provide weather forecasts and databases that store technical documents. The server identifies and integrates the relevant information to construct the optimal response for the user. The response is generated in natural language, such as, "Next week will be sunny nationwide with an average temperature of 25 degrees Celsius."

[0455] The generated response is sent to the terminal, which then displays it to the user. The user can then make decisions based on the information provided. Furthermore, the user can input feedback on the response through the terminal, which is then returned to the server. The server uses this feedback as data to improve the quality of future information provision.

[0456] As a concrete example, the user's prompt might be in the form of "Please tell me about the return procedure," and the AI ​​model would analyze this information and respond by returning relevant guidelines.

[0457] This invention provides a system that responds quickly and accurately to user requests by integrating natural language processing and data integration technologies.

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

[0459] Step 1:

[0460] The user inputs requests using natural language via a terminal. These requests consist of specific information or questions, such as "Please tell me the weather for next week." The input data is processed by the terminal as basic text data.

[0461] Step 2:

[0462] The terminal sends the entered request to the server. In this process, data is sent to the server via an encrypted, secure connection. The input data is converted into digital packets according to a specific protocol and reaches the server via the network.

[0463] Step 3:

[0464] The server processes incoming requests. First, it analyzes the request using a generative AI model. The input here is text data, and natural language processing libraries are used to extract the intent and keywords of the request. For example, keywords such as "next week" and "weather" are identified from the request sentence to understand the user's intent.

[0465] Step 4:

[0466] The server queries external information resources based on the analysis results. It collects relevant information through API calls and database queries to obtain the necessary data. At this stage, for example, it might query a weather database based on keywords obtained through the analysis to retrieve information about the weather for the following week.

[0467] Step 5:

[0468] The server integrates the acquired data and generates a response to return to the user. This response generation uses natural language generation technology to construct information using contextually natural expressions. For example, it generates easy-to-understand expressions such as, "It is expected to be sunny nationwide next week."

[0469] Step 6:

[0470] The server sends the generated response back to the terminal. The response data is encrypted again and transferred to the terminal via secure communication. The output here is the specific response message.

[0471] Step 7:

[0472] The user receives the response via the terminal and confirms it on the screen. The terminal displays the received response in the user interface, making the information available to the user. If necessary, user feedback is collected and sent back to the server to contribute to subsequent process improvements.

[0473] (Application Example 1)

[0474] 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."

[0475] Conventional digital assistant systems rely on limited information sources when generating responses to user input, making it difficult to provide specific and practical information, particularly regarding security. Furthermore, achieving rapid and accurate responses tailored to user needs, such as checking security status and providing relevant information, remains a challenge.

[0476] 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.

[0477] In this invention, the server includes a device for receiving user instructions, a device for analyzing the received instructions using language processing techniques, and a device for searching a data set based on the analyzed data and identifying relevant data. This allows the user to quickly receive detailed and contextually relevant responses based on integrated information by entering security questions.

[0478] A "device that receives user instructions" is a device that receives input information from the user and sends it to the next processing stage.

[0479] A "device that analyzes using language processing technology" is a device that implements technology to interpret natural language received from a user and extract intent and important information.

[0480] A "device for exploring data sets and identifying related data" is a device that searches for necessary information from a large amount of data and identifies information relevant to the user's request.

[0481] "Integrated information-based detailed and contextual responses" are responses that combine data obtained from multiple sources to provide specific and relevant answers to user questions.

[0482] A "device for transmitting to an instrument" is a device that sends the generated response to the user's device for display.

[0483] A "security question" is an inquiry made by a user seeking information about their own safety and protection.

[0484] "Security management information" refers to data that includes the status of security measures being implemented and related information.

[0485] The system for implementing this invention begins with the user entering security questions through their terminal. The terminal receives this input and sends it to a server. The server uses natural language processing techniques to analyze this input and extract the user's intent and important information. Existing language processing software libraries such as spaCy and BERT can be used for natural language processing.

[0486] The server searches the database based on the analyzed information and identifies relevant security management information. The database may include data on security measures and security devices installed by the user. The identified information is integrated with additional contextual information, and the server uses this to generate a detailed and contextual response.

[0487] This response is sent to the user's device, through which the user can check the security status and receive specific instructions. For example, if the user asks, "I want to see the camera footage from my front door this morning, how can I do that?", the system will guide them by saying, "Open your security app and select the relevant time period from the list of recorded footage."

[0488] By using a generative AI model, the initial prompt can be set to an example like this: "How can I find information about my home security system?" This allows the system to provide the most appropriate answer tailored to the user's specific needs.

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

[0490] Step 1:

[0491] The user enters security questions in natural language using a device. This input is recorded on the device in text format and prepared to be sent to the server. The input consists of the user's questions, which are sent for analysis in the next step.

[0492] Step 2:

[0493] The terminal sends user input to the server. The server retrieves the received text data and analyzes it using natural language processing techniques. In this process, it receives text data as input and uses a natural language processing library to extract intent and keywords. The output is the analyzed intent and extracted keywords.

[0494] Step 3:

[0495] The server searches the database based on the analyzed keywords and retrieves relevant security management information. The database contains information on security devices and measures, and search queries are generated to identify relevant data. Keywords are taken as input, and relevant information is obtained as output.

[0496] Step 4:

[0497] The server generates detailed responses by supplementing relevant information obtained through searches based on context. In this process, a generative AI model is used to combine the information and form natural language responses to the user's questions. The input is relevant information, and the output is a detailed, context-based response.

[0498] Step 5:

[0499] The server sends the generated response to the terminal and displays it on the user's screen. The user can then review it and take appropriate security action. Input is a detailed response, and output is information presented to the user. In this step, the user may receive specific instructions such as, "Open your security app and select the relevant time period from the list of recorded video footage."

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

[0501] This invention aims to realize a digital assistant system that analyzes user input and considers not only the information provided but also the user's emotions. This system provides highly accurate services based on communication between the server, terminal, and user.

[0502] The user enters a question using a terminal. The terminal sends the input to the server, which receives this data. The server analyzes the user's input by employing both natural language processing (NLP) technology and an emotion engine. Specifically, it uses the NLP engine to extract keywords from the text and understand the context, while simultaneously recognizing the user's emotions with the emotion engine.

[0503] Based on the analyzed information, the server searches its internal database to identify relevant information. During this process, it also considers emotional information to design the optimal response tailored to the user's current situation. This information is generated as a natural language response, taking emotional aspects into account.

[0504] The generated response is sent from the server to the terminal and presented to the user. This response includes contextual additional information and considerations based on the perceived emotions. For example, if the user expresses dissatisfaction, the response will incorporate particularly flexible approaches and follow-up procedures based on that information.

[0505] As a concrete example, suppose a user enters into their device, "I want to return this immediately, but the process is taking too long and I'm having trouble." The server analyzes this input and identifies the keywords and emotions associated with "return process" and "having trouble." Next, the server generates a response that includes "prompt action" and "an apology," providing solutions to alleviate the user's anxiety.

[0506] This system aims to enhance not only the accuracy of information but also the emotional satisfaction of users, enabling more multifaceted and flexible customer service.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The user enters a question and their feelings at the time into the device and presses the send button. The device then sends this as a data packet to the server.

[0510] Step 2:

[0511] The server uses a natural language processing engine to analyze the received data. It extracts keywords from the text and performs processing to understand the context.

[0512] Step 3:

[0513] The server uses an emotion engine to recognize emotions from user input. Emotions are determined based on the tone of the text and specific vocabulary.

[0514] Step 4:

[0515] The server searches the database for the most relevant information based on the analyzed keywords and sentiment data. Search results are generated using optimized queries to ensure speed and accuracy.

[0516] Step 5:

[0517] The server integrates search results and sentiment analysis results to generate a natural language response. In this process, the system is designed to include responses and considerations tailored to the user's situation, based on the recognized emotions.

[0518] Step 6:

[0519] The server sends the generated response to the terminal. The terminal displays this on the user screen, presented through an interface designed with user visibility in mind.

[0520] Step 7:

[0521] Users review the provided responses and can ask additional questions or decide on their course of action based on that information. They can also provide feedback on the responses offered.

[0522] Step 8:

[0523] The terminal sends user feedback to the server. The server receives this feedback and uses it, along with accumulated data, to improve the system.

[0524] (Example 2)

[0525] 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."

[0526] Traditional digital assistants fail to consider user emotions during information retrieval, merely presenting information. This creates a challenge in providing flexible and appropriate responses based on the user's emotional state. Understanding and responding appropriately to user emotions contributes to improved customer satisfaction, but conventional technologies are insufficient in this regard.

[0527] 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.

[0528] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions based on the analyzed information. This enables flexible and appropriate information provision and responses that take the user's emotions into consideration.

[0529] "User input" refers to data that a user sends to the system via a terminal for information retrieval or to ask questions.

[0530] "Natural language processing technology" refers to the technology that enables computers to understand and interpret text written in human language.

[0531] An "analysis device" is a device that uses natural language processing technology to analyze received data and has the function of extracting information and recognizing emotions.

[0532] "Emotion recognition" is the process of identifying a user's emotions and feelings based on the wording and context included in their input.

[0533] "Internal information sources" refer to databases and knowledge resources stored within the system, which are used to provide relevant information based on analysis results.

[0534] A "response generation device" is a device that provides explanations and instructions to the user in natural language based on acquired information.

[0535] A "terminal" is a device used by a user to input information and receive responses from a server; mobile phones and computers are common examples.

[0536] This digital assistant system analyzes user input and provides responses that take emotions into consideration. The system primarily operates through the coordinated operation of a server and terminals.

[0537] Users enter questions and requests using an internet-connected device. This device can be a smartphone or personal computer, and users can send information using a keyboard or voice input. A specific example would be a request such as, "Please tell me the status of the item I ordered yesterday."

[0538] The terminal sends the data entered by the user to the server. The server applies natural language processing techniques to the received data. Specifically, it utilizes software packages such as "spaCy" and "Google Cloud Natural Language API" to extract keywords and understand the context of the input text. The server can also use tools like "IBM Watson Tone Analyzer" to recognize the user's emotions from the input.

[0539] The server analyzes the information and considers the emotions involved, then searches internal sources to identify relevant information. This search uses SQL or NoSQL databases (e.g., MySQL, MongoDB). Based on the retrieved information, the server generates a response in natural language. By using a generative AI model, it can construct contextually appropriate, natural conversational responses and present emotionally sensitive content to the user. For example, a possible response might be, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment."

[0540] An example of a prompt message would be, "Analyze the user's emotions and suggest the best course of action regarding the return process." This system enables the provision of highly accurate service that takes user emotions into consideration.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] Users enter questions and requests using their devices. Input can be in text or voice format, and users enter the information through an application on their device. For example, they might enter something like, "I want to check the status of my product." This input data is then prepared to be sent from the device to the server.

[0544] Step 2:

[0545] The terminal sends user input to the server. The transmitted data is packaged as text data via the HTTP protocol. Specifically, this includes the communication process from input text to its arrival at the server. As a result, the server receives the user's input data.

[0546] Step 3:

[0547] The server analyzes the user's input data. Here, a natural language processing engine is used to extract keywords from the input and understand the context. Tools such as "spaCy" and "Google Cloud Natural Language API" are used. Important words and phrases are identified from the input text and used as search criteria for the internal data. The information extracted through analysis is then passed on to the next processing step.

[0548] Step 4:

[0549] The server performs emotion recognition based on the analysis results. It infers the user's emotional state from the input data via an emotion engine. The tool used is "IBM Watson Tone Analyzer," which detects emotions from word choice and phrases. This process generates analysis data that includes the user's emotions.

[0550] Step 5:

[0551] The server searches for internal information sources based on the analyzed data. It utilizes SQL or NoSQL databases to identify information relevant to the user's request. For example, it extracts records corresponding to product order status from the database. This search result yields a useful set of information.

[0552] Step 6:

[0553] The server uses a generative AI model to generate natural language responses, taking into account the information and sentiment data it receives. This generates contextually appropriate responses that take the user's emotions into consideration. For example, a response like, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment," might be generated. This response is then produced as the final output.

[0554] Step 7:

[0555] The server sends the generated response to the terminal. The response data is sent in text format using the HTTP protocol. The terminal receiving this response completes the information transfer to the user.

[0556] Step 8:

[0557] The terminal receives a response from the server and displays it to the user. The user confirms this and receives information relevant to the purpose of their input. This fulfills the user's information request, and the interaction is complete.

[0558] (Application Example 2)

[0559] 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."

[0560] Electronic payment services require prompt and appropriate responses to user emotions such as anxiety and dissatisfaction. However, conventional technologies have struggled to generate flexible responses that take user emotions into account, limiting the improvement of the user experience. Solving this problem is essential.

[0561] 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.

[0562] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions. This makes it possible to generate an appropriate response that takes the user's emotions into consideration.

[0563] A "device that receives user input" refers to hardware or software that acquires data entered by a user and transfers it to other system components.

[0564] "Natural language processing technology" is a technology that enables computers to understand and process human language, making it possible to extract keywords and interpret context.

[0565] A "device that recognizes user emotions" is hardware or software that can analyze and determine a user's emotional state based on input data.

[0566] An "internal storage medium" is a storage device that stores analyzed information and related data, and is used to retrieve necessary data based on that information.

[0567] A "device that generates responses in natural language" is a system component that constructs responses in a human-understandable format based on user input data and related information.

[0568] A "device that transmits to the user's terminal" is a device equipped with communication means for transferring the generated response to the terminal used by the user and displaying it to the user.

[0569] This invention presents a digital assistant system that takes user emotions into consideration in electronic payment services. This system is implemented by receiving input from the user's terminal and processing it on a server. Specifically, data entered by the user is transmitted from the terminal to the server via the network. The server first analyzes the user's input data using an NLP engine (natural language processing engine, e.g., spaCy) and extracts keywords. This analysis is performed to understand the content of the input data and grasp the appropriate context.

[0570] Furthermore, the server uses a sentiment analysis engine (e.g., TextBlob) to recognize the user's emotional state. This analysis identifies emotional data such as anxiety or satisfaction. Based on the analyzed keywords and emotional information, the server searches its internal storage medium (database) to identify relevant information.

[0571] The server then generates an appropriate response in natural language based on the user's input and emotions. This response generation includes flexible responses that take the user's emotions into consideration, and may include words of encouragement or apology. The generated response is sent from the server to the user's terminal and presented to the user.

[0572] For example, if a user enters "I'm worried because my payment didn't go through," the server analyzes the input and identifies the keywords "payment" and "anxiety," along with the emotions involved. It then generates a flexible response to alleviate the anxiety, along with a quick solution, and sends it to the user.

[0573] An example of a prompt message is: "The user is feeling anxious because their payment didn't go through. Please suggest flexible solutions to alleviate their anxiety." This can improve the user experience.

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

[0575] Step 1:

[0576] The user enters questions or comments about electronic payments through the terminal. The terminal sends this input data to the server in its original format. The input is text data and is sent directly to the server via the network.

[0577] Step 2:

[0578] The server receives user input and analyzes the data using a natural language processing engine. The purpose of the analysis is to extract keywords from the text data and understand the context. Since the input is text data, the output will be the extracted keywords and contextual information. Specifically, the server starts an NLP engine (e.g., spaCy) and analyzes the input data.

[0579] Step 3:

[0580] The server uses an emotion analysis engine to recognize the user's emotions based on the analysis results. The input here is the analysis results from step 2, and emotion information is extracted based on that. The output is data on the user's emotional state. The server uses an emotion analysis engine such as TextBlob to identify the emotion, referring to the extracted keywords and context.

[0581] Step 4:

[0582] The server searches its internal storage media based on analyzed keywords and sentiment information. The input consists of keywords and sentiment information, which are used to find relevant records. The output is a dataset of related information. It generates database queries and performs high-speed searches for the relevant information.

[0583] Step 5:

[0584] The server generates natural language responses based on integrated information and sentiment information. The input for this step is the retrieved information and sentiment information. The output is a text response in a human-readable format. A generative AI model is used to generate responses, creating flexible responses that take context and sentiment into account.

[0585] Step 6:

[0586] The text generated as a response is sent from the server to the user's terminal and displayed. The input is the natural language response text generated by the server, and the output is the information displayed on the user's terminal. The terminal processes the received text and displays it on the screen.

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

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

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

[0590] [Fourth Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

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

[0604] This invention aims to efficiently perform communication and data processing between users, terminals, and servers in order to realize a digital assistant system.

[0605] First, the user enters a question using a terminal. This input is in natural language, and the terminal sends the input data to the server. The server receives this input and analyzes the text using natural language processing technology. This allows the server to understand the intent of the input and important keywords, and then retrieve relevant information from the database based on the analysis results.

[0606] The server integrates data gathered from multiple sources, summarizing and supplementing it as needed. This allows it to select the most relevant information for the user's question and generate a response. This response generation is designed to consider context and be expressed in more natural language.

[0607] The generated response is sent from the server to the terminal and displayed on the user's screen. The user can use this information to resolve the problem and decide on their next course of action. Furthermore, the user can provide feedback on the response, which is sent to the server and used to improve future responses.

[0608] As a concrete example, suppose a user enters "How do I return an item?" into their terminal. In this case, the server analyzes "return procedure" as a keyword and searches for information in the relevant FAQ database. Based on the results, it can generate a step-by-step guide and provide the user with a specific response such as, "First, put the purchased item back in its original packaging and use the registered shipping carrier."

[0609] This system is an invention that can improve user satisfaction and streamline inquiry processing by enabling quick and accurate responses.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The user enters a question into the terminal and presses the send button. The terminal then packages this input data into packets and sends them to the server over the network.

[0613] Step 2:

[0614] The server analyzes the received data and passes the input to a natural language processing engine to understand the user's intent. The NLP engine analyzes the text and extracts keywords and the user's intent.

[0615] Step 3:

[0616] The server searches the FAQ database and product information database based on the extracted information. It executes database queries to efficiently find related records.

[0617] Step 4:

[0618] The server integrates search results and organizes the information that should be provided to the user. If necessary, it summarizes the information and links it to more detailed information.

[0619] Step 5:

[0620] The server generates a natural language response based on the integrated information. The response is optimized to be in a format that is easy for the user to understand.

[0621] Step 6:

[0622] The server sends the generated response data to the terminal. The terminal displays this received data on its screen so that the user can verify it.

[0623] Step 7:

[0624] The user reviews the information presented, asks further questions as needed, and takes subsequent actions based on the information provided.

[0625] Step 8:

[0626] Users can provide feedback on the usefulness of the response. The terminal sends this feedback to the server, which helps improve the system.

[0627] (Example 1)

[0628] 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".

[0629] Conventional digital assistant systems often suffer from delayed or inaccurate responses to user requests. This is because natural language processing techniques and the identification of relevant data from external information resources are not performed efficiently. Furthermore, the generated language expressions often do not adequately consider the context, failing to provide useful information to the user.

[0630] 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.

[0631] In this invention, the server includes means for receiving a user request and analyzing the request using data transformation technology, means for identifying relevant data from external information resources based on the analyzed information, and means for integrating the identified data and generating a linguistic expression that indicates the request. This makes it possible to respond quickly and accurately to user requests and provide contextually relevant and useful information.

[0632] A "user" refers to an individual or group that makes a request to the system.

[0633] A "request" refers to an inquiry made by a user to the system regarding the acquisition of information or services.

[0634] "Data transformation technology" refers to technical methods used to analyze and understand user requirements.

[0635] "Means of analysis" refers to the function that utilizes data transformation techniques to understand received requests.

[0636] "External information resources" refer to databases and information services that provide relevant data located outside the system.

[0637] "Means for identifying relevant data" refers to the function of finding necessary data from external information resources based on the analysis results.

[0638] "Means of integrating data" refers to the function of gathering identified related data into a single, combined piece of information.

[0639] "Means for generating linguistic expressions" refers to a function that generates appropriate responses for the user based on integrated data.

[0640] "Communication device" refers to a terminal or device used to transmit generated linguistic expressions to the user.

[0641] This invention is a digital assistant system that enables efficient data exchange and information provision between users, terminals, and servers.

[0642] The user first inputs information using a terminal. This terminal is an information processing device such as a smartphone or computer, and can accept text or voice input. For example, the user might input the request, "Please tell me the weather for next week," into the terminal.

[0643] The terminal's role is to send received requests to the server. Secure communication protocols over the internet are used for transmission. The server receives the requests and analyzes them using generative AI models. This analysis uses Python's natural language processing library to extract the intent and important keywords of the requests.

[0644] Next, the server queries external information resources based on the analysis results. These external resources include, for example, information sources that provide weather forecasts and databases that store technical documents. The server identifies and integrates the relevant information to construct the optimal response for the user. The response is generated in natural language, such as, "Next week will be sunny nationwide with an average temperature of 25 degrees Celsius."

[0645] The generated response is sent to the terminal, which then displays it to the user. The user can then make decisions based on the information provided. Furthermore, the user can input feedback on the response through the terminal, which is then returned to the server. The server uses this feedback as data to improve the quality of future information provision.

[0646] As a concrete example, the user's prompt might be in the form of "Please tell me about the return procedure," and the AI ​​model would analyze this information and respond by returning relevant guidelines.

[0647] This invention provides a system that responds quickly and accurately to user requests by integrating natural language processing and data integration technologies.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The user inputs requests using natural language via a terminal. These requests consist of specific information or questions, such as "Please tell me the weather for next week." The input data is processed by the terminal as basic text data.

[0651] Step 2:

[0652] The terminal sends the entered request to the server. In this process, data is sent to the server via an encrypted, secure connection. The input data is converted into digital packets according to a specific protocol and reaches the server via the network.

[0653] Step 3:

[0654] The server processes incoming requests. First, it analyzes the request using a generative AI model. The input here is text data, and natural language processing libraries are used to extract the intent and keywords of the request. For example, keywords such as "next week" and "weather" are identified from the request sentence to understand the user's intent.

[0655] Step 4:

[0656] The server queries external information resources based on the analysis results. It collects relevant information through API calls and database queries to obtain the necessary data. At this stage, for example, it might query a weather database based on keywords obtained through the analysis to retrieve information about the weather for the following week.

[0657] Step 5:

[0658] The server integrates the acquired data and generates a response to return to the user. This response generation uses natural language generation technology to construct information using contextually natural expressions. For example, it generates easy-to-understand expressions such as, "It is expected to be sunny nationwide next week."

[0659] Step 6:

[0660] The server sends the generated response back to the terminal. The response data is encrypted again and transferred to the terminal via secure communication. The output here is the specific response message.

[0661] Step 7:

[0662] The user receives the response via the terminal and confirms it on the screen. The terminal displays the received response in the user interface, making the information available to the user. If necessary, user feedback is collected and sent back to the server to contribute to subsequent process improvements.

[0663] (Application Example 1)

[0664] 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".

[0665] Conventional digital assistant systems rely on limited information sources when generating responses to user input, making it difficult to provide specific and practical information, particularly regarding security. Furthermore, achieving rapid and accurate responses tailored to user needs, such as checking security status and providing relevant information, remains a challenge.

[0666] 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.

[0667] In this invention, the server includes a device for receiving user instructions, a device for analyzing the received instructions using language processing techniques, and a device for searching a data set based on the analyzed data and identifying relevant data. This allows the user to quickly receive detailed and contextually relevant responses based on integrated information by entering security questions.

[0668] A "device that receives user instructions" is a device that receives input information from the user and sends it to the next processing stage.

[0669] A "device that analyzes using language processing technology" is a device that implements technology to interpret natural language received from a user and extract intent and important information.

[0670] A "device for exploring data sets and identifying related data" is a device that searches for necessary information from a large amount of data and identifies information relevant to the user's request.

[0671] "Integrated information-based detailed and contextual responses" are responses that combine data obtained from multiple sources to provide specific and relevant answers to user questions.

[0672] A "device for transmitting to an instrument" is a device that sends the generated response to the user's device for display.

[0673] A "security question" is an inquiry made by a user seeking information about their own safety and protection.

[0674] "Security management information" refers to data that includes the status of security measures being implemented and related information.

[0675] The system for implementing this invention begins with the user entering security questions through their terminal. The terminal receives this input and sends it to a server. The server uses natural language processing techniques to analyze this input and extract the user's intent and important information. Existing language processing software libraries such as spaCy and BERT can be used for natural language processing.

[0676] The server searches the database based on the analyzed information and identifies relevant security management information. The database may include data on security measures and security devices installed by the user. The identified information is integrated with additional contextual information, and the server uses this to generate a detailed and contextual response.

[0677] This response is sent to the user's device, through which the user can check the security status and receive specific instructions. For example, if the user asks, "I want to see the camera footage from my front door this morning, how can I do that?", the system will guide them by saying, "Open your security app and select the relevant time period from the list of recorded footage."

[0678] By using a generative AI model, the initial prompt can be set to an example like this: "How can I find information about my home security system?" This allows the system to provide the most appropriate answer tailored to the user's specific needs.

[0679] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0680] Step 1:

[0681] The user enters security questions in natural language using a device. This input is recorded on the device in text format and prepared to be sent to the server. The input consists of the user's questions, which are sent for analysis in the next step.

[0682] Step 2:

[0683] The terminal sends user input to the server. The server retrieves the received text data and analyzes it using natural language processing techniques. In this process, it receives text data as input and uses a natural language processing library to extract intent and keywords. The output is the analyzed intent and extracted keywords.

[0684] Step 3:

[0685] The server searches the database based on the analyzed keywords and retrieves relevant security management information. The database contains information on security devices and measures, and search queries are generated to identify relevant data. Keywords are taken as input, and relevant information is obtained as output.

[0686] Step 4:

[0687] The server generates detailed responses by supplementing relevant information obtained through searches based on context. In this process, a generative AI model is used to combine the information and form natural language responses to the user's questions. The input is relevant information, and the output is a detailed, context-based response.

[0688] Step 5:

[0689] The server sends the generated response to the terminal and displays it on the user's screen. The user can then review it and take appropriate security action. Input is a detailed response, and output is information presented to the user. In this step, the user may receive specific instructions such as, "Open your security app and select the relevant time period from the list of recorded video footage."

[0690] 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.

[0691] This invention aims to realize a digital assistant system that analyzes user input and considers not only the information provided but also the user's emotions. This system provides highly accurate services based on communication between the server, terminal, and user.

[0692] The user enters a question using a terminal. The terminal sends the input to the server, which receives this data. The server analyzes the user's input by employing both natural language processing (NLP) technology and an emotion engine. Specifically, it uses the NLP engine to extract keywords from the text and understand the context, while simultaneously recognizing the user's emotions with the emotion engine.

[0693] Based on the analyzed information, the server searches its internal database to identify relevant information. During this process, it also considers emotional information to design the optimal response tailored to the user's current situation. This information is generated as a natural language response, taking emotional aspects into account.

[0694] The generated response is sent from the server to the terminal and presented to the user. This response includes contextual additional information and considerations based on the perceived emotions. For example, if the user expresses dissatisfaction, the response will incorporate particularly flexible approaches and follow-up procedures based on that information.

[0695] As a concrete example, suppose a user enters into their device, "I want to return this immediately, but the process is taking too long and I'm having trouble." The server analyzes this input and identifies the keywords and emotions associated with "return process" and "having trouble." Next, the server generates a response that includes "prompt action" and "an apology," providing solutions to alleviate the user's anxiety.

[0696] This system aims to enhance not only the accuracy of information but also the emotional satisfaction of users, enabling more multifaceted and flexible customer service.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] The user enters a question and their feelings at the time into the device and presses the send button. The device then sends this as a data packet to the server.

[0700] Step 2:

[0701] The server uses a natural language processing engine to analyze the received data. It extracts keywords from the text and performs processing to understand the context.

[0702] Step 3:

[0703] The server uses an emotion engine to recognize emotions from user input. Emotions are determined based on the tone of the text and specific vocabulary.

[0704] Step 4:

[0705] The server searches the database for the most relevant information based on the analyzed keywords and sentiment data. Search results are generated using optimized queries to ensure speed and accuracy.

[0706] Step 5:

[0707] The server integrates search results and sentiment analysis results to generate a natural language response. In this process, the system is designed to include responses and considerations tailored to the user's situation, based on the recognized emotions.

[0708] Step 6:

[0709] The server sends the generated response to the terminal. The terminal displays this on the user screen, presented through an interface designed with user visibility in mind.

[0710] Step 7:

[0711] Users review the provided responses and can ask additional questions or decide on their course of action based on that information. They can also provide feedback on the responses offered.

[0712] Step 8:

[0713] The terminal sends user feedback to the server. The server receives this feedback and uses it, along with accumulated data, to improve the system.

[0714] (Example 2)

[0715] 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".

[0716] Traditional digital assistants fail to consider user emotions during information retrieval, merely presenting information. This creates a challenge in providing flexible and appropriate responses based on the user's emotional state. Understanding and responding appropriately to user emotions contributes to improved customer satisfaction, but conventional technologies are insufficient in this regard.

[0717] 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.

[0718] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions based on the analyzed information. This enables flexible and appropriate information provision and responses that take the user's emotions into consideration.

[0719] "User input" refers to data that a user sends to the system via a terminal for information retrieval or to ask questions.

[0720] "Natural language processing technology" refers to the technology that enables computers to understand and interpret text written in human language.

[0721] An "analysis device" is a device that uses natural language processing technology to analyze received data and has the function of extracting information and recognizing emotions.

[0722] "Emotion recognition" is the process of identifying a user's emotions and feelings based on the wording and context included in their input.

[0723] "Internal information sources" refer to databases and knowledge resources stored within the system, which are used to provide relevant information based on analysis results.

[0724] A "response generation device" is a device that provides explanations and instructions to the user in natural language based on acquired information.

[0725] A "terminal" is a device used by a user to input information and receive responses from a server; mobile phones and computers are common examples.

[0726] This digital assistant system analyzes user input and provides responses that take emotions into consideration. The system primarily operates through the coordinated operation of a server and terminals.

[0727] Users enter questions and requests using an internet-connected device. This device can be a smartphone or personal computer, and users can send information using a keyboard or voice input. A specific example would be a request such as, "Please tell me the status of the item I ordered yesterday."

[0728] The terminal sends the data entered by the user to the server. The server applies natural language processing techniques to the received data. Specifically, it utilizes software packages such as "spaCy" and "Google Cloud Natural Language API" to extract keywords and understand the context of the input text. The server can also use tools like "IBM Watson Tone Analyzer" to recognize the user's emotions from the input.

[0729] The server analyzes the information and considers the emotions involved, then searches internal sources to identify relevant information. This search uses SQL or NoSQL databases (e.g., MySQL, MongoDB). Based on the retrieved information, the server generates a response in natural language. By using a generative AI model, it can construct contextually appropriate, natural conversational responses and present emotionally sensitive content to the user. For example, a possible response might be, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment."

[0730] An example of a prompt message would be, "Analyze the user's emotions and suggest the best course of action regarding the return process." This system enables the provision of highly accurate service that takes user emotions into consideration.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] Users enter questions and requests using their devices. Input can be in text or voice format, and users enter the information through an application on their device. For example, they might enter something like, "I want to check the status of my product." This input data is then prepared to be sent from the device to the server.

[0734] Step 2:

[0735] The terminal sends user input to the server. The transmitted data is packaged as text data via the HTTP protocol. Specifically, this includes the communication process from input text to its arrival at the server. As a result, the server receives the user's input data.

[0736] Step 3:

[0737] The server analyzes the user's input data. Here, a natural language processing engine is used to extract keywords from the input and understand the context. Tools such as "spaCy" and "Google Cloud Natural Language API" are used. Important words and phrases are identified from the input text and used as search criteria for the internal data. The information extracted through analysis is then passed on to the next processing step.

[0738] Step 4:

[0739] The server performs emotion recognition based on the analysis results. It infers the user's emotional state from the input data via an emotion engine. The tool used is "IBM Watson Tone Analyzer," which detects emotions from word choice and phrases. This process generates analysis data that includes the user's emotions.

[0740] Step 5:

[0741] The server searches for internal information sources based on the analyzed data. It utilizes SQL or NoSQL databases to identify information relevant to the user's request. For example, it extracts records corresponding to product order status from the database. This search result yields a useful set of information.

[0742] Step 6:

[0743] The server uses a generative AI model to generate natural language responses, taking into account the information and sentiment data it receives. This generates contextually appropriate responses that take the user's emotions into consideration. For example, a response like, "We would like to inform you about the status of your order. We apologize for the delay, but your item is currently being prepared for shipment," might be generated. This response is then produced as the final output.

[0744] Step 7:

[0745] The server sends the generated response to the terminal. The response data is sent in text format using the HTTP protocol. The terminal receiving this response completes the information transfer to the user.

[0746] Step 8:

[0747] The terminal receives a response from the server and displays it to the user. The user confirms this and receives information relevant to the purpose of their input. This fulfills the user's information request, and the interaction is complete.

[0748] (Application Example 2)

[0749] 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".

[0750] Electronic payment services require prompt and appropriate responses to user emotions such as anxiety and dissatisfaction. However, conventional technologies have struggled to generate flexible responses that take user emotions into account, limiting the improvement of the user experience. Solving this problem is essential.

[0751] 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.

[0752] In this invention, the server includes a device for receiving user input, a device for analyzing the received input using natural language processing technology, and a device for recognizing the user's emotions. This makes it possible to generate an appropriate response that takes the user's emotions into consideration.

[0753] A "device that receives user input" refers to hardware or software that acquires data entered by a user and transfers it to other system components.

[0754] "Natural language processing technology" is a technology that enables computers to understand and process human language, making it possible to extract keywords and interpret context.

[0755] A "device that recognizes user emotions" is hardware or software that can analyze and determine a user's emotional state based on input data.

[0756] An "internal storage medium" is a storage device that stores analyzed information and related data, and is used to retrieve necessary data based on that information.

[0757] A "device that generates responses in natural language" is a system component that constructs responses in a human-understandable format based on user input data and related information.

[0758] A "device that transmits to the user's terminal" is a device equipped with communication means for transferring the generated response to the terminal used by the user and displaying it to the user.

[0759] This invention presents a digital assistant system that takes user emotions into consideration in electronic payment services. This system is implemented by receiving input from the user's terminal and processing it on a server. Specifically, data entered by the user is transmitted from the terminal to the server via the network. The server first analyzes the user's input data using an NLP engine (natural language processing engine, e.g., spaCy) and extracts keywords. This analysis is performed to understand the content of the input data and grasp the appropriate context.

[0760] Furthermore, the server uses a sentiment analysis engine (e.g., TextBlob) to recognize the user's emotional state. This analysis identifies emotional data such as anxiety or satisfaction. Based on the analyzed keywords and emotional information, the server searches its internal storage medium (database) to identify relevant information.

[0761] The server then generates an appropriate response in natural language based on the user's input and emotions. This response generation includes flexible responses that take the user's emotions into consideration, and may include words of encouragement or apology. The generated response is sent from the server to the user's terminal and presented to the user.

[0762] For example, if a user enters "I'm worried because my payment didn't go through," the server analyzes the input and identifies the keywords "payment" and "anxiety," along with the emotions involved. It then generates a flexible response to alleviate the anxiety, along with a quick solution, and sends it to the user.

[0763] An example of a prompt message is: "The user is feeling anxious because their payment didn't go through. Please suggest flexible solutions to alleviate their anxiety." This can improve the user experience.

[0764] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0765] Step 1:

[0766] The user enters questions or comments about electronic payments through the terminal. The terminal sends this input data to the server in its original format. The input is text data and is sent directly to the server via the network.

[0767] Step 2:

[0768] The server receives user input and analyzes the data using a natural language processing engine. The purpose of the analysis is to extract keywords from the text data and understand the context. Since the input is text data, the output will be the extracted keywords and contextual information. Specifically, the server starts an NLP engine (e.g., spaCy) and analyzes the input data.

[0769] Step 3:

[0770] The server uses an emotion analysis engine to recognize the user's emotions based on the analysis results. The input here is the analysis results from step 2, and emotion information is extracted based on that. The output is data on the user's emotional state. The server uses an emotion analysis engine such as TextBlob to identify the emotion, referring to the extracted keywords and context.

[0771] Step 4:

[0772] The server searches its internal storage media based on analyzed keywords and sentiment information. The input consists of keywords and sentiment information, which are used to find relevant records. The output is a dataset of related information. It generates database queries and performs high-speed searches for the relevant information.

[0773] Step 5:

[0774] The server generates natural language responses based on integrated information and sentiment information. The input for this step is the retrieved information and sentiment information. The output is a text response in a human-readable format. A generative AI model is used to generate responses, creating flexible responses that take context and sentiment into account.

[0775] Step 6:

[0776] The text generated as a response is sent from the server to the user's terminal and displayed. The input is the natural language response text generated by the server, and the output is the information displayed on the user's terminal. The terminal processes the received text and displays it on the screen.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] The following is further disclosed regarding the embodiments described above.

[0799] (Claim 1)

[0800] A receiving means for receiving user input,

[0801] An analysis means for analyzing the received input using natural language processing technology,

[0802] A search means that searches an internal database based on the analyzed information and identifies relevant information,

[0803] A generation means that integrates the identified information and generates a response in natural language,

[0804] A transmission means for sending the generated response to the user's terminal,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, wherein the analysis means includes a function for extracting keywords from user input.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the generation means has a function to generate a response that includes contextually appropriate additional information based on integrated information.

[0810] "Example 1"

[0811] (Claim 1)

[0812] A means for receiving user requests and analyzing those requests using data transformation technology,

[0813] A means for identifying relevant data from external information resources based on the analyzed information,

[0814] A means for integrating the identified data and generating a linguistic expression to indicate,

[0815] Means for delivering the generated language expression to the user's communication device,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the means for analysis includes a function for collecting words from user requests.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the generating means has a function to generate a linguistic expression that includes additional information appropriate to the situation, based on integrated data.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A device that receives user instructions,

[0824] A device that analyzes the received instructions using language processing technology,

[0825] A device that searches for a data set based on the analyzed data and identifies related data,

[0826] A device that integrates the identified data and creates a response in natural language,

[0827] A device for transmitting the generated response to the user's device,

[0828] A terminal for users to enter security-related questions,

[0829] A device that uses statistical information to identify safety management information related to the aforementioned question,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, wherein the analysis device has a function to identify an indicator from the user's instructions.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the creation device has a function to create a response that includes additional data depending on the situation, based on the integrated data.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] A device that receives user input,

[0838] A device that analyzes the received input using natural language processing technology,

[0839] Based on the analyzed information, a device is used to recognize the user's emotions.

[0840] A device that, taking into account the recognized emotional information, searches for internal information sources and identifies relevant information,

[0841] A device that integrates the identified information and generates a response in natural language,

[0842] A device that transmits the generated response to the user's terminal,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the analysis device has the function of extracting keywords from user input and simultaneously performing contextual understanding and user sentiment analysis.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the generating device has a function to generate a response that includes additional information corresponding to context and emotion based on integrated information.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A device that receives user input,

[0851] A device that analyzes the received input using natural language processing technology,

[0852] A device that recognizes the user's emotions,

[0853] A device that searches an internal storage medium based on the analyzed information and emotional information and identifies related information,

[0854] A device that integrates the identified information and generates a response in natural language that corresponds to the user's emotions,

[0855] A device that transmits the generated response to the user's terminal,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the analysis device has a function for extracting concepts from user input.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the generating device has a function to generate a response that includes contextually appropriate additional information based on integrated information and emotional information. [Explanation of Symbols]

[0861] 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 device that receives user instructions, A device that analyzes the received instructions using language processing technology, A device that searches for a data set based on the analyzed data and identifies related data, A device that integrates the identified data and creates a response in natural language, A device for transmitting the generated response to the user's device, A terminal for users to enter security-related questions, A device that uses statistical information to identify safety management information related to the aforementioned question, A system that includes this.

2. The system according to claim 1, wherein the analysis device has a function to identify an indicator from the user's instructions.

3. The system according to claim 1, wherein the creation device has a function to create a response that includes additional data depending on the situation, based on the integrated data.

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