system

Abstract

We provide the system. [Solution] A means of sending message data received on the user terminal to the server, A means of analyzing the message data on the server to identify the emotion, A method for analyzing reply patterns by referring to the user's past conversation history on the server, A means of generating appropriate replies using a generation AI based on emotions and user response patterns, A system that includes a means of sending generated replies to the user's terminal and automatically responding.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 modern society with an overabundance of information, it is often difficult for users to give an immediate, emotional, and appropriate reply. In particular, in situations where a quick response is required or when it is difficult to find appropriate words, the smoothness of communication may be impaired. Also, the time and effort required for a user to accurately understand emotions and give a sympathetic response accordingly can be a burden. To solve such problems and further improve the quality of communication, an efficient method is required.

Means for Solving the Problems

[0005] This invention begins with a means for receiving user messages on a terminal and sending them to a server. Next, the server analyzes the message data and identifies emotions using natural language processing techniques. This process also includes inference using emotion tags on stamps and emojis. Based on the identified emotions and response patterns analyzed by referring to the user's past conversation history, the server uses generative AI to generate natural and empathetic responses. These generated responses are sent to the user's terminal and automatically transmitted as messages. Furthermore, the server collects feedback from the user and uses this to improve the accuracy of the AI ​​model, thereby continuously improving the quality of responses. As a result, users can communicate better while reducing stress.

[0006] A "user terminal" is an electronic device that a user can operate to send and receive messages.

[0007] A "server" is a computer system that receives message data sent from a user terminal and performs analysis and data processing on it.

[0008] "Message data" refers to communication information such as text, stamps, images, and voice messages sent by users.

[0009] "Generative AI" is an artificial intelligence technology that generates natural and empathetic responses based on past data.

[0010] "Natural language processing technology" is a computer processing technology used to understand emotions and intentions from text data.

[0011] "Conversation history" refers to a record of messages exchanged between users in the past.

[0012] "Feedback" refers to the evaluation or opinion that users provide regarding automated responses. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0016] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] In embodiments of the present invention, an emotion-based automatic reply system is provided by integrating a user terminal, server-based data processing technology, and generative AI. The user terminal is typically an electronic device such as a smartphone or computer, which has the function of allowing the user to input and send messages. These messages can take a wide range of forms, including text, stamps, images, and voice messages.

[0035] Message data received by the terminal is sent to a server via the internet. The server has powerful processing capabilities and the necessary programs and processes to analyze this data. First, the server applies natural language processing techniques to the received message to identify the emotion contained within it. If stamps or emojis are included, predefined emotion tags are used to infer the emotion.

[0036] Furthermore, the server uses the sentiment analysis results and the user's past conversation history to analyze the user's communication style and reply patterns. Based on this analysis, the generative AI generates an appropriate reply that empathizes with the user's current emotions. The generated reply may be in text format or may include appropriate stamps or emojis. In this process, the server sends the generated reply message to the user's terminal, and the user can receive it immediately.

[0037] As a concrete example, suppose a user sends a message from their device saying, "I'm tired from work today." The server analyzes this message and identifies the emotion as "tired." Then, referring to past conversation history, the generating AI creates a reply saying, "You had a tough day. Take a good rest," and attaches a relaxing stamp to it. This reply is immediately sent to the user's device, and the user feels acknowledged and encouraged. This system allows users to enjoy fast and emotionally empathetic communication.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user composes a message using their device and presses the send button. The device receives this message data and prepares to send it to the server.

[0041] Step 2:

[0042] The device sends message data to the server. The data is sent in various formats, including text, stamps, images, and audio.

[0043] Step 3:

[0044] The server analyzes the message data it receives. A natural language processing model is applied to the text data to identify emotions. For stamps and emojis, the emotional information embedded within them is used to infer emotions.

[0045] Step 4:

[0046] The server extracts past conversation history from the database and analyzes the user's reply patterns and communication style. This forms the basis for determining what kind of reply is appropriate.

[0047] Step 5:

[0048] The server uses a generative AI to generate the optimal response based on the sentiment analysis results and the user's response patterns. During this process, the server also has the option to include stamps or emojis in the generated response.

[0049] Step 6:

[0050] The server generates a reply and sends it to the user's terminal. The user can instantly receive the automatically generated reply.

[0051] Step 7:

[0052] The user enters feedback on the automated reply they received. This feedback is sent to the server via the device.

[0053] Step 8:

[0054] The server receives feedback and stores it in a database. This data is used to improve the AI ​​model later on, playing a role in increasing the accuracy and empathy of the responses.

[0055] (Example 1)

[0056] 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."

[0057] In modern society, there is a demand for rapid and emotionally empathetic communication via information and communication devices. However, conventional automated response systems have faced challenges in accurately identifying users' emotions and generating appropriate responses. Furthermore, systems that provide fixed responses without fully utilizing the user's past interaction history have failed to provide users with a satisfying communication experience.

[0058] 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.

[0059] In this invention, the server includes means for transmitting information received by an information processing device operated by the user to a communication device, means for analyzing the information in the communication device to identify emotions, and means for analyzing response patterns by referring to the user's past interaction history in the communication device. This enables the generation of appropriate responses that empathize with the user's emotions, allowing for rapid and highly satisfying communication.

[0060] An "information processing device" is a device that users operate to send and receive information, and generally refers to electronic devices such as smartphones and computers.

[0061] A "communication device" is a device that analyzes information received from an information processing device and connects it to other system components, and in particular, it plays the role of a server.

[0062] "Means of identifying emotions" refers to technologies that utilize natural language processing techniques to identify emotions contained in messages, symbols, and image representations.

[0063] "Means for analyzing response patterns" refers to technologies that analyze a user's past interaction history and determine what kind of response should be given based on that.

[0064] "Generative AI" refers to an artificial intelligence model that automatically generates appropriate responses based on the user's emotions and interaction history.

[0065] "Training data" refers to data, including evaluations and feedback, used to improve the accuracy of AI models.

[0066] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language, and is used for sentiment recognition and text analysis.

[0067] "Symbols and visual representations" refer to non-textual information such as stamps and emojis, which serve as means to complement the emotions and nuances of a message.

[0068] The automated reply system in this invention provides high-quality communication based on the user's emotions by integrating an information processing device, a communication device, and a generative AI model.

[0069] Users use smartphones or computers as information processing devices to input messages. The information can take various forms, including text, stamps, images, and voice messages. These information processing devices have the function of transmitting the input messages to communication devices.

[0070] Information received by the terminal is transmitted to the server via the internet. The communication device has powerful processing capabilities, and programs including natural language processing technologies (e.g., spaCy and BERT) are used to analyze the information. This identifies the emotions embedded in the message. Emotional information from stamps and emojis is also interpreted by referring to predefined emotion tags.

[0071] The server further analyzes response patterns using the user's past conversation history stored in a database on the server. This analyzed data is then used by a generating AI model (e.g., GPT-4® or LLaMA) to produce an appropriate response that empathizes with the user's emotions. This response may also include appropriate symbols or image representations.

[0072] For example, if a user enters the message "I'm tired from work today," the server identifies the emotion as "tired." The generative AI model then references the user's past interaction history and generates an empathetic reply such as "You had a tough day. Take a good rest," along with a relaxing stamp. This reply message is immediately sent to the user's device.

[0073] An example of a prompt message is: "The user says, 'I'm tired from work today.' Generate an appropriate response to this message. The emotion is 'tired,' and consider past conversation history."

[0074] This system allows users to enjoy quick responses while gaining the reassurance that their feelings are understood.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] When a user enters a message into the information processing device and presses the send button, data is entered in various formats such as text, stamps, images, and voice messages. The entered data is temporarily stored within the information processing device.

[0078] Step 2:

[0079] The terminal receives user input data and transmits it to the server via the internet. The input data is protected by an encryption protocol (e.g., SSL / TLS) before transmission, ensuring data security.

[0080] Step 3:

[0081] The server analyzes the received data. First, natural language processing techniques (e.g., spaCy or BERT) are used to identify the sentiment of the text-based message. If stamps or emojis are included, they are mapped to predefined sentiment tags to infer the sentiment. The output of this step is identified sentiment information, such as "tired."

[0082] Step 4:

[0083] The server references past user interactions and retrieves conversation history from the database. Based on the retrieved history data, it analyzes the user's response patterns. The output of this data analysis process is analytical data showing the user's past response trends.

[0084] Step 5:

[0085] The server uses a generative AI model (e.g., GPT-4 or LLaMA) to generate an appropriate response based on identified emotions and analyzed response patterns. The AI ​​is instructed using example prompts to generate an appropriate empathetic message. The output of this step is the generated response, expressed in text format.

[0086] Step 6:

[0087] The server generates a reply, to which appropriate symbols and image representations are attached to create the final message. The created final message is then sent back to the user's information processing device via the communication network.

[0088] Step 7:

[0089] The device receives the sent reply message and displays it to the user immediately. The user can see this message and feel understood and empathized with.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern communication, there is a need to quickly and accurately share emotions based on the messages sent by users. However, typical automated response systems often fail to adequately identify user emotions, making it difficult to show appropriate empathy. Furthermore, while it is expected that providing appropriate content according to the user's emotions would further increase satisfaction, this is not currently achieved. This invention aims to solve these problems.

[0093] 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.

[0094] In this invention, the server includes means for transmitting communication data received by the user terminal to an information processing device, means for analyzing the communication data in the information processing device to identify emotions, and means for analyzing the user's past history in the information processing device to analyze reaction patterns. This makes it possible to automatically provide appropriate responses and content recommendations based on the user's emotions, thereby providing a more personalized user experience.

[0095] A "user terminal" is an electronic device that has the function of sending and receiving messages, and is a device for users to input information.

[0096] An "information processing device" is a computer system that receives and analyzes data transmitted from a user terminal.

[0097] "Communication data" refers to messages and information transmitted from a user terminal to an information processing device, and includes data such as text, audio, and images.

[0098] "Means of identifying emotions" refers to the techniques and processes of analyzing communication data and identifying the emotions contained within that data.

[0099] A "reaction pattern" refers to a tendency in behavior that responds to specific emotions or situations, determined based on a user's past history.

[0100] "Generative AI" is an artificial intelligence technology that generates appropriate responses and information based on user input and circumstances.

[0101] "Means of automated response" refers to technologies that send rapid responses to users based on analyzed and generated information.

[0102] "Content" refers to various types of information and entertainment provided to users, such as music, videos, and text.

[0103] "Past history" refers to a record of a user's past actions and choices, and is important data used for personalization.

[0104] To implement this invention, a user terminal is required. The user terminal consists of an electronic device such as a smartphone or a computer, and the user uses the terminal to input messages and data. The communication data entered by the user is transmitted to an information processing device via the internet.

[0105] Next, a server is used as the information processing device to analyze the received communication data. Here, natural language processing technology is used to identify the sentiment of the message. The hardware consists of a server with a high-performance processor, and the software utilizes sentiment analysis models such as Hugging Face Transformers.

[0106] Once an emotion is identified, the server then analyzes the user's past history to identify reaction patterns. This analysis requires large-capacity data storage and sophisticated algorithms. Based on the results obtained, a generative AI generates an appropriate response based on that emotion. The generative AI utilizes models such as OpenAI's GPT model.

[0107] The server then determines the generated response and appropriate content, and sends it to the user's terminal. Content selection utilizes services such as the Spotify API and The Movie Database API, and suggestions are made taking into account the user's past viewing history.

[0108] For example, if a user sends a message such as "I'm feeling down today," the server analyzes it to identify the emotion of sadness. Next, the generative AI generates a response using a prompt such as "What movies or music do you recommend for when you're feeling down?" and can suggest movies like "Avatar" or "Spirited Away," or music playlists like "Rainy Day Jazz," to the user.

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

[0110] Step 1:

[0111] The user enters and sends a message on their device (smartphone or computer). This input is processed as text data on the device and sent to the server via the internet. The input can be a natural language message conveying the user's emotions. The output is a data packet sent to the server via the network.

[0112] Step 2:

[0113] The server analyzes the received communication data. The input is text data sent by the user. The server uses natural language processing techniques to identify emotions and processes the data using emotion analysis models such as Hugging Face Transformers. The output is the identified emotion information.

[0114] Step 3:

[0115] The server analyzes reaction patterns by referring to the user's past history. Inputs include identified emotional information and the user's past history data. Based on this information, it extracts and analyzes past reaction patterns from the database to obtain data points for use in the generating AI. The output is the analysis results regarding reaction patterns.

[0116] Step 4:

[0117] The server uses generative AI to generate appropriate responses based on emotions. Inputs include emotion information and response pattern analysis results. It utilizes OpenAI's GPT model to generate the response the user should receive. The type of response generated is controlled by the design of the prompt. The output is a generated natural language reply message.

[0118] Step 5:

[0119] The server selects and suggests content appropriate to the generated response. Inputs include emotion-based response messages and the user's viewing history data. Using APIs such as Spotify and The Movie Database, it searches for selected content and identifies movies and music that match the user's interests. The output is a list of recommended content.

[0120] Step 6:

[0121] The server sends the generated reply message and content list to the user's terminal. This process involves converting the reply message and content data into packets and transmitting them over the network. The input is the reply message and content list stored on the server, and the output is the information displayed on the user's terminal.

[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 is a system combining a user terminal, a server, and an emotion engine that automatically generates empathetic responses while recognizing the user's emotions. The user enters a message using the terminal, and this message is sent to the server in the form of text, stamps, images, voice messages, etc. The server receives the message data and uses the emotion engine to recognize the user's emotions. This emotion engine analyzes emotions using a combination of various data sources, such as voice tone, keyboard input speed, and phrase patterns.

[0124] In addition, the server refers to past conversation history and analyzes the user's reply patterns. This prepares it not only to identify emotions but also to select the most appropriate reply accordingly. The generative AI generates natural and appropriate replies based on the recognized emotions and past data. The generated replies include appropriate stamps and emojis and are sent to the user quickly.

[0125] As a concrete example, consider a scenario where a user sends the message, "I'm so happy today!" The server uses an emotion engine to identify this phrase as "joy." Based on the analysis, the generating AI selects a reply such as, "That's great! I'd love to hear what happened!" and sends it back to the user with an appropriate emoji expressing joy. Through this process, the user experiences pleasant communication because their emotions are correctly recognized and responded to appropriately.

[0126] This system also features a feedback loop; when a user provides feedback on a reply, it is collected by the server. This feedback data is used to improve the accuracy of the AI ​​model, contributing to higher quality replies in the future. In this way, users can consistently enjoy high-quality communication.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user types a message using the terminal and presses the send button. The terminal receives this message data and prepares to send it to the server.

[0130] Step 2:

[0131] The device sends the message data to the server. The data is sent in one of the following formats: text, stamps, images, or audio.

[0132] Step 3:

[0133] The server passes the received message data to the emotion engine. The emotion engine analyzes the text data using natural language processing techniques to identify emotions. Simultaneously, if there is an audio message, it performs audio analysis, and analyzes images using facial recognition technology to supplement the emotions.

[0134] Step 4:

[0135] Based on the sentiment identification results, the server references the user's past conversation history from a database and analyzes their response patterns. This analysis identifies which response is most appropriate for the user.

[0136] Step 5:

[0137] The server uses AI to generate reply messages based on sentiment analysis results and response patterns. The generated messages are enhanced with appropriate emojis and stamps as needed to emphasize the tone of the message to the recipient.

[0138] Step 6:

[0139] The server sends the generated reply message to the user's terminal. The user receives this reply immediately and can confirm the automated response from the system.

[0140] Step 7:

[0141] Users provide feedback on the automated replies they receive. This feedback includes information about the accuracy and satisfaction level of the replies.

[0142] Step 8:

[0143] The device sends user feedback to the server. The server stores this feedback in a database and uses it as training data for an AI model to improve system performance.

[0144] (Example 2)

[0145] 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".

[0146] In modern communication systems, accurately recognizing and responding naturally to user emotions is a challenging task. Existing systems can only superficially interpret the content of written messages and fail to provide responses that adequately reflect the user's inner feelings. This can limit the user's communication experience and potentially reduce satisfaction. Furthermore, simple automated responses alone cannot address the individual needs of users.

[0147] 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.

[0148] In this invention, the server includes means for analyzing the content of data to determine emotions, means for referring to historical information to analyze the reply format, and means for generating an appropriate reply based on the emotions and reply format. This enables a deeper understanding of the user's message and a more empathetic and natural response.

[0149] A "user terminal" refers to a device used by a user to input messages and communicate with a server.

[0150] A "server" refers to a central computing device that analyzes data received from user terminals and identifies emotions.

[0151] "Data" refers to all information, such as text, audio, stamps, and images, that is sent from the user's terminal to the server.

[0152] "Emotional analysis" refers to the process of analyzing data sent by users on a server to determine the user's inner emotional state.

[0153] "History information" refers to information used to analyze reply patterns and other factors based on a user's past conversation history.

[0154] A "generative AI model" refers to a form of artificial intelligence technology that generates appropriate responses based on sentiment analysis and historical information.

[0155] "Automatic response" refers to the process where a server generates a reply and sends it to the user's terminal, providing an instant response to the user's message.

[0156] This invention is implemented by a system combining a user terminal, a server, and a generative AI model. The user operates the terminal to input a message, which is sent to the server in the form of text, stamps, images, or voice messages. The terminal's role is to convert the input message into the appropriate data format and transfer it to the server.

[0157] The server receives data sent from the user's terminal and uses sentiment analysis technology to identify the user's emotions. Sentiment analysis uses various data sources, including voice tone, input speed, and phrase patterns. Furthermore, the server refers to past history information and analyzes response patterns from the user's past conversations to prepare appropriate and personalized responses for the user.

[0158] The generative AI model generates appropriate responses based on emotional and historical information recognized by the server. Stamps and emojis are added to the generated responses as needed, and they are adjusted to be more empathetic and natural. The server then sends the generated response to the user's device, which automatically replies.

[0159] As a concrete example, consider a scenario where a user sends the message "I had a great day!" from their device to the server. The server interprets this message as expressing the emotion of "fun," and based on past interactions, its AI model generates a response such as "I'd love to know what fun things happened!" It then adds an emoji representing the feeling of happiness and sends it back to the user.

[0160] An example of a prompt might be: "Analyze the user's message, identify their emotions, and generate an appropriate response. Consider the emotion analysis data and past conversation history to ensure a natural response." This allows the user to feel understood and experience a pleasant communication experience.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user operates the terminal to input a message. The input message may be in text format, or it may be a stamp or voice message. The terminal converts this message into digital data and sends it to the server. The input is the user's message, and the output is the digital data sent to the server.

[0164] Step 2:

[0165] The server receives digital data transmitted from the terminal. This received data is input into the emotion engine, which analyzes factors such as voice tone, input speed, and phrase patterns. Through this analysis, the server can identify the user's emotions. The input is digital data, and the output is identified emotion information.

[0166] Step 3:

[0167] The server analyzes conversation history by referencing past interactions with the user. From this historical data, it extracts user response patterns associated with identified emotions, preparing more natural and personalized responses. The input is conversation history data, and the output is the analyzed response patterns.

[0168] Step 4:

[0169] Using a generative AI model, the server generates an appropriate response based on identified sentiment information and analyzed response patterns. At this stage, stamps and emojis are also selected and included in the response. The input is sentiment information and response patterns, and the output is a constructed, natural-sounding response message.

[0170] Step 5:

[0171] The server sends the generated reply message to the terminal. The terminal displays the received response to the user, conveying emotions through appropriate stamps and emojis, thereby facilitating natural communication with the user. The input is the reply message, and the output is the visual display to the user.

[0172] (Application Example 2)

[0173] 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".

[0174] In recent years, as online activities have increased, user anxiety about security risks has also grown. Traditional methods require users to assess and respond to risks themselves, which can lead to emotional stress. Furthermore, the difficulty in responding to security concerns immediately and appropriately remains a challenge.

[0175] 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.

[0176] In this invention, the server includes means for transmitting data received by the user terminal to an information processing device; means for analyzing the data in the information processing device to identify emotions; means for analyzing patterns by referring to the user's past behavior history in the information processing device; means for generating appropriate information using a generated AI based on emotions and user patterns; means for transmitting the generated information to the user terminal and automatically providing a response; and means for detecting potential threats from the received data in the user terminal and generating a warning. As a result, users can continue their online activities with peace of mind and receive quick and appropriate responses while reducing emotional burden.

[0177] A "user terminal" is an electronic device owned and used by an individual, equipped with functions for communication and information processing.

[0178] An "information processing device" is a device that electronically receives, analyzes, stores, and transmits data.

[0179] A "means of identifying emotions" refers to a system that analyzes collected data and has the function of identifying the user's emotional state.

[0180] "Past activity history" refers to a record of actions and responses the user has taken up to that point.

[0181] "Methods for analyzing patterns" refer to functions that analyze user behavioral trends based on past behavioral history.

[0182] "Generative AI" refers to artificial intelligence technology that automatically generates information and has the ability to perform natural language processing.

[0183] "Appropriate information" refers to responses and suggestions that are deemed beneficial to the user based on the analyzed data.

[0184] A "potential threat" refers to a situation or factor that could potentially affect the security of user data or systems.

[0185] A "means of generating warnings" refers to a function that alerts the user when a potential threat is detected.

[0186] This system combines a user terminal, an information processing device (server), and a generative AI model to provide empathetic responses based on emotions. The user terminal is a communication device that the user uses on a daily basis, such as a smartphone or tablet, and transmits data obtained during online activities to the information processing device.

[0187] The information processing device functions as a server, receiving data transmitted from user terminals in real time. This data includes text input, voice messages, and browsing history. The server analyzes this data using means to identify emotions, and identifies the user's emotional state, particularly anxiety. The emotion analysis utilizes an emotion engine equipped with natural language processing technology.

[0188] Once an emotion is identified, the server analyzes patterns by referencing past behavioral history. This analysis is performed to understand the user's past behavior and emotional changes, and to predict potential security risks. Based on this information, the generative AI generates appropriate information for the user. In particular, if a potential threat is detected, it generates empathetic and actionable security recommendations.

[0189] The generated information is sent to the user's terminal as a response message and displayed to the user immediately. This allows the user to quickly take action against the risk while receiving emotional support. An example of a prompt message would be: "The user's emotional state has been detected as anxious, and the problematic email has been opened. Please generate empathetic and appropriate suggestions for how to respond."

[0190] This system allows users to use the online environment with peace of mind, reducing the emotional burden associated with the process. Furthermore, this loop continuously improves the quality of security measures.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user terminal prepares data collected from online activities (e.g., browsing history, message content). This data is transmitted to the information processing device using its communication function.

[0194] Step 2:

[0195] The server receives data sent from the user's terminal in real time. To analyze the received data, it uses an emotion engine equipped with natural language processing technology. This engine identifies the user's emotions based on the voice tone and text content of the input data. The output provides information classifying the emotions.

[0196] Step 3:

[0197] The server combines identified sentiment data with past behavioral history data to analyze user behavior patterns. This analysis utilizes machine learning algorithms. Based on the input data, it outputs potential risks and general behavioral tendencies.

[0198] Step 4:

[0199] The server uses a generative AI model to generate appropriate information and suggestions based on the user's emotional state and behavioral patterns. The generated text is created considering specific conditions indicated by prompt sentences. The output is the generated empathetic messages and suggestions.

[0200] Step 5:

[0201] The generated information is sent from the server to the user's terminal. The user's terminal immediately displays the received message on its interface. This allows the user to take immediate action while feeling emotionally supported.

[0202] 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.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] In embodiments of the present invention, an emotion-based automatic reply system is provided by integrating a user terminal, server-based data processing technology, and generative AI. The user terminal is typically an electronic device such as a smartphone or computer, which has the function of allowing the user to input and send messages. These messages can take a wide range of forms, including text, stamps, images, and voice messages.

[0219] Message data received by the terminal is sent to a server via the internet. The server has powerful processing capabilities and the necessary programs and processes to analyze this data. First, the server applies natural language processing techniques to the received message to identify the emotion contained within it. If stamps or emojis are included, predefined emotion tags are used to infer the emotion.

[0220] Furthermore, the server uses the sentiment analysis results and the user's past conversation history to analyze the user's communication style and reply patterns. Based on this analysis, the generative AI generates an appropriate reply that empathizes with the user's current emotions. The generated reply may be in text format or may include appropriate stamps or emojis. In this process, the server sends the generated reply message to the user's terminal, and the user can receive it immediately.

[0221] As a concrete example, suppose a user sends a message from their device saying, "I'm tired from work today." The server analyzes this message and identifies the emotion as "tired." Then, referring to past conversation history, the generating AI creates a reply saying, "You had a tough day. Take a good rest," and attaches a relaxing stamp to it. This reply is immediately sent to the user's device, and the user feels acknowledged and encouraged. This system allows users to enjoy fast and emotionally empathetic communication.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user composes a message using their device and presses the send button. The device receives this message data and prepares to send it to the server.

[0225] Step 2:

[0226] The device sends message data to the server. The data is sent in various formats, including text, stamps, images, and audio.

[0227] Step 3:

[0228] The server analyzes the message data it receives. A natural language processing model is applied to the text data to identify emotions. For stamps and emojis, the emotional information embedded within them is used to infer emotions.

[0229] Step 4:

[0230] The server extracts past conversation history from the database and analyzes the user's reply patterns and communication style. This forms the basis for determining what kind of reply is appropriate.

[0231] Step 5:

[0232] The server uses a generative AI to generate the optimal response based on the sentiment analysis results and the user's response patterns. During this process, the server also has the option to include stamps or emojis in the generated response.

[0233] Step 6:

[0234] The server generates a reply and sends it to the user's terminal. The user can instantly receive the automatically generated reply.

[0235] Step 7:

[0236] The user enters feedback on the automated reply they received. This feedback is sent to the server via the device.

[0237] Step 8:

[0238] The server receives feedback and stores it in a database. This data is used to improve the AI ​​model later on, playing a role in increasing the accuracy and empathy of the responses.

[0239] (Example 1)

[0240] 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."

[0241] In modern society, there is a demand for rapid and emotionally empathetic communication via information and communication devices. However, conventional automated response systems have faced challenges in accurately identifying users' emotions and generating appropriate responses. Furthermore, systems that provide fixed responses without fully utilizing the user's past interaction history have failed to provide users with a satisfying communication experience.

[0242] 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.

[0243] In this invention, the server includes means for transmitting information received by an information processing device operated by the user to a communication device, means for analyzing the information in the communication device to identify emotions, and means for analyzing response patterns by referring to the user's past interaction history in the communication device. This enables the generation of appropriate responses that empathize with the user's emotions, allowing for rapid and highly satisfying communication.

[0244] An "information processing device" is a device that users operate to send and receive information, and generally refers to electronic devices such as smartphones and computers.

[0245] A "communication device" is a device that analyzes information received from an information processing device and connects it to other system components, and in particular, it plays the role of a server.

[0246] "Means of identifying emotions" refers to technologies that utilize natural language processing techniques to identify emotions contained in messages, symbols, and image representations.

[0247] "Means for analyzing response patterns" refers to technologies that analyze a user's past interaction history and determine what kind of response should be given based on that.

[0248] "Generative AI" refers to an artificial intelligence model that automatically generates appropriate responses based on the user's emotions and interaction history.

[0249] "Training data" refers to data, including evaluations and feedback, used to improve the accuracy of AI models.

[0250] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language, and is used for sentiment recognition and text analysis.

[0251] "Symbols and visual representations" refer to non-textual information such as stamps and emojis, which serve as means to complement the emotions and nuances of a message.

[0252] The automated reply system in this invention provides high-quality communication based on the user's emotions by integrating an information processing device, a communication device, and a generative AI model.

[0253] Users use smartphones or computers as information processing devices to input messages. The information can take various forms, including text, stamps, images, and voice messages. These information processing devices have the function of transmitting the input messages to communication devices.

[0254] Information received by the terminal is transmitted to the server via the internet. The communication device has powerful processing capabilities, and programs including natural language processing technologies (e.g., spaCy and BERT) are used to analyze the information. This identifies the emotions embedded in the message. Emotional information from stamps and emojis is also interpreted by referring to predefined emotion tags.

[0255] The server further analyzes response patterns using the user's past conversation history stored in a database on the server. This analyzed data is then used by a generating AI model (e.g., GPT-4 or LLaMA) to produce an appropriate response that empathizes with the user's emotions. This response may also include appropriate symbols or visual representations.

[0256] For example, if a user enters the message "I'm tired from work today," the server identifies the emotion as "tired." The generative AI model then references the user's past interaction history and generates an empathetic reply such as "You had a tough day. Take a good rest," along with a relaxing stamp. This reply message is immediately sent to the user's device.

[0257] An example of a prompt message is: "The user says, 'I'm tired from work today.' Generate an appropriate response to this message. The emotion is 'tired,' and consider past conversation history."

[0258] This system allows users to enjoy quick responses while gaining the reassurance that their feelings are understood.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] When a user enters a message into the information processing device and presses the send button, data is entered in various formats such as text, stamps, images, and voice messages. The entered data is temporarily stored within the information processing device.

[0262] Step 2:

[0263] The terminal receives user input data and transmits it to the server via the internet. The input data is protected by an encryption protocol (e.g., SSL / TLS) before transmission, ensuring data security.

[0264] Step 3:

[0265] The server analyzes the received data. First, natural language processing techniques (e.g., spaCy or BERT) are used to identify the sentiment of the text-based message. If stamps or emojis are included, they are mapped to predefined sentiment tags to infer the sentiment. The output of this step is identified sentiment information, such as "tired."

[0266] Step 4:

[0267] The server references past user interactions and retrieves conversation history from the database. Based on the retrieved history data, it analyzes the user's response patterns. The output of this data analysis process is analytical data showing the user's past response trends.

[0268] Step 5:

[0269] The server uses a generative AI model (e.g., GPT-4 or LLaMA) to generate an appropriate response based on identified emotions and analyzed response patterns. The AI ​​is instructed using example prompts to generate an appropriate empathetic message. The output of this step is the generated response, expressed in text format.

[0270] Step 6:

[0271] The server generates a reply, to which appropriate symbols and image representations are attached to create the final message. The created final message is then sent back to the user's information processing device via the communication network.

[0272] Step 7:

[0273] The device receives the sent reply message and displays it to the user immediately. The user can see this message and feel understood and empathized with.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In modern communication, there is a need to quickly and accurately share emotions based on the messages sent by users. However, typical automated response systems often fail to adequately identify user emotions, making it difficult to show appropriate empathy. Furthermore, while it is expected that providing appropriate content according to the user's emotions would further increase satisfaction, this is not currently achieved. This invention aims to solve these problems.

[0277] 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.

[0278] In this invention, the server includes means for transmitting communication data received by the user terminal to an information processing device, means for analyzing the communication data in the information processing device to identify emotions, and means for analyzing the user's past history in the information processing device to analyze reaction patterns. This makes it possible to automatically provide appropriate responses and content recommendations based on the user's emotions, thereby providing a more personalized user experience.

[0279] A "user terminal" is an electronic device that has the function of sending and receiving messages, and is a device for users to input information.

[0280] An "information processing device" is a computer system that receives and analyzes data transmitted from a user terminal.

[0281] "Communication data" refers to messages and information transmitted from a user terminal to an information processing device, and is data including text, voice, images, etc.

[0282] "Means for identifying emotions" is a technology and process for analyzing communication data and identifying the emotions contained in the data.

[0283] "Reaction pattern" refers to the tendency of actions according to specific emotions and situations, determined based on the user's past history.

[0284] "Generative AI" is an artificial intelligence technology for generating appropriate responses and information based on user input and situations.

[0285] "Means for automatically reacting" is a technology for quickly sending a response to the user based on the analyzed and generated information.

[0286] "Content" refers to various information and entertainment provided to the user, such as music, video, text, etc.

[0287] "Past history" is a record of the user's previous actions and selections, and is important data used for personalization.

[0288] To implement this invention, first a user terminal is required. The user terminal is composed of electronic devices such as smartphones and computers, and the user uses the terminal to input messages and data. The communication data input by the user is transmitted to the information processing device via the Internet.

[0289] Next, a server is used in the information processing device to analyze the received communication data. Here, natural language processing technology is used to identify the emotions of the messages. As hardware, a server with a high-performance processor is used, and as software, an emotion analysis model such as Hugging Face Transformers is utilized.

[0290] Once an emotion is identified, the server then analyzes the user's past history to identify reaction patterns. This analysis requires large-capacity data storage and sophisticated algorithms. Based on the results obtained, a generative AI generates appropriate responses based on the emotion. OpenAI's GPT model, among others, is used for the generative AI.

[0291] The server then determines the generated response and appropriate content, and sends it to the user's terminal. Content selection utilizes services such as the Spotify API and The Movie Database API, and suggestions are made taking into account the user's past viewing history.

[0292] For example, if a user sends a message such as "I'm feeling down today," the server analyzes it to identify the emotion of sadness. Next, the generative AI generates a response using a prompt such as "What movies or music do you recommend for when you're feeling down?" and can suggest movies like "Avatar" or "Spirited Away," or music playlists like "Rainy Day Jazz," to the user.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user enters and sends a message on their device (smartphone or computer). This input is processed as text data on the device and sent to the server via the internet. The input can be a natural language message conveying the user's emotions. The output is a data packet sent to the server via the network.

[0296] Step 2:

[0297] The server analyzes the received communication data. The input is text data sent by the user. The server uses natural language processing techniques to identify emotions and processes the data using emotion analysis models such as Hugging Face Transformers. The output is the identified emotion information.

[0298] Step 3:

[0299] The server analyzes reaction patterns by referring to the user's past history. Inputs include identified emotional information and the user's past history data. Based on this information, it extracts and analyzes past reaction patterns from the database to obtain data points for use in the generating AI. The output is the analysis results regarding reaction patterns.

[0300] Step 4:

[0301] The server uses generative AI to generate appropriate responses based on emotions. Inputs include emotion information and response pattern analysis results. It utilizes OpenAI's GPT model to generate the response the user should receive. The type of response generated is controlled by the design of the prompt. The output is a generated natural language reply message.

[0302] Step 5:

[0303] The server selects and suggests content appropriate to the generated response. Inputs include emotion-based response messages and the user's viewing history data. Using APIs such as Spotify and The Movie Database, it searches for selected content and identifies movies and music that match the user's interests. The output is a list of recommended content.

[0304] Step 6:

[0305] The server sends the generated reply message and the content list to the user's terminal. In this process, the reply message and the content data are converted into packets and sent via the network. The input is the reply message and the content list existing in the server, and the output is the information displayed on the user terminal.

[0306] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0307] The present invention is a system that combines a user terminal, a server, and an emotion engine, and automatically generates a sympathetic reply while recognizing the user's emotion. The user inputs a message using the terminal, and this message is sent to the server in the form of text, a stamp, an image, a voice message, etc. The server receives the message data and utilizes the emotion engine to recognize the user's emotion. This emotion engine analyzes the emotion by using various data sources such as voice tone, keyboard input speed, phrase pattern, etc.

[0308] In addition to this, the server refers to the past conversation history and analyzes the user's reply pattern. Thereby, not only is the emotion identified, but also preparations are made to select the optimal reply form according thereto. The generation AI generates a natural and appropriate reply based on the recognized emotion and past data. The generated reply includes appropriate stamps and emojis and is quickly sent to the user.

[0309] As a concrete example, consider a scenario where a user sends the message, "I'm so happy today!" The server uses an emotion engine to identify this phrase as "joy." Based on the analysis, the generating AI selects a reply such as, "That's great! I'd love to hear what happened!" and sends it back to the user with an appropriate emoji expressing joy. Through this process, the user experiences pleasant communication because their emotions are correctly recognized and responded to appropriately.

[0310] This system also features a feedback loop; when a user provides feedback on a reply, it is collected by the server. This feedback data is used to improve the accuracy of the AI ​​model, contributing to higher quality replies in the future. In this way, users can consistently enjoy high-quality communication.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user types a message using the terminal and presses the send button. The terminal receives this message data and prepares to send it to the server.

[0314] Step 2:

[0315] The device sends the message data to the server. The data is sent in one of the following formats: text, stamps, images, or audio.

[0316] Step 3:

[0317] The server passes the received message data to the emotion engine. The emotion engine analyzes the text data using natural language processing techniques to identify emotions. Simultaneously, if there is an audio message, it performs audio analysis, and analyzes images using facial recognition technology to supplement the emotions.

[0318] Step 4:

[0319] Based on the sentiment identification results, the server references the user's past conversation history from a database and analyzes their response patterns. This analysis identifies which response is most appropriate for the user.

[0320] Step 5:

[0321] The server uses AI to generate reply messages based on sentiment analysis results and response patterns. The generated messages are enhanced with appropriate emojis and stamps as needed to emphasize the tone of the message to the recipient.

[0322] Step 6:

[0323] The server sends the generated reply message to the user's terminal. The user receives this reply immediately and can confirm the automated response from the system.

[0324] Step 7:

[0325] Users provide feedback on the automated replies they receive. This feedback includes information about the accuracy and satisfaction level of the replies.

[0326] Step 8:

[0327] The device sends user feedback to the server. The server stores this feedback in a database and uses it as training data for an AI model to improve system performance.

[0328] (Example 2)

[0329] 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".

[0330] In modern communication systems, accurately recognizing and responding naturally to user emotions is a challenging task. Existing systems can only superficially interpret the content of written messages and fail to provide responses that adequately reflect the user's inner feelings. This can limit the user's communication experience and potentially reduce satisfaction. Furthermore, simple automated responses alone cannot address the individual needs of users.

[0331] 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.

[0332] In this invention, the server includes means for analyzing the content of data to determine emotions, means for referring to historical information to analyze the reply format, and means for generating an appropriate reply based on the emotions and reply format. This enables a deeper understanding of the user's message and a more empathetic and natural response.

[0333] A "user terminal" refers to a device used by a user to input messages and communicate with a server.

[0334] A "server" refers to a central computing device that analyzes data received from user terminals and identifies emotions.

[0335] "Data" refers to all information, such as text, audio, stamps, and images, that is sent from the user's terminal to the server.

[0336] "Emotional analysis" refers to the process of analyzing data sent by users on a server to determine the user's inner emotional state.

[0337] "History information" refers to information used to analyze reply patterns and other factors based on a user's past conversation history.

[0338] A "generative AI model" refers to a form of artificial intelligence technology that generates appropriate responses based on sentiment analysis and historical information.

[0339] "Automatic response" refers to the process where a server generates a reply and sends it to the user's terminal, providing an instant response to the user's message.

[0340] This invention is implemented by a system combining a user terminal, a server, and a generative AI model. The user operates the terminal to input a message, which is sent to the server in the form of text, stamps, images, or voice messages. The terminal's role is to convert the input message into the appropriate data format and transfer it to the server.

[0341] The server receives data sent from the user's terminal and uses sentiment analysis technology to identify the user's emotions. Sentiment analysis uses various data sources, including voice tone, input speed, and phrase patterns. Furthermore, the server refers to past history information and analyzes response patterns from the user's past conversations to prepare appropriate and personalized responses for the user.

[0342] The generative AI model generates appropriate responses based on emotional and historical information recognized by the server. Stamps and emojis are added to the generated responses as needed, and they are adjusted to be more empathetic and natural. The server then sends the generated response to the user's device, which automatically replies.

[0343] As a concrete example, consider a scenario where a user sends the message "I had a great day!" from their device to the server. The server interprets this message as expressing the emotion of "fun," and based on past interactions, its AI model generates a response such as "I'd love to know what fun things happened!" It then adds an emoji representing the feeling of happiness and sends it back to the user.

[0344] An example of a prompt might be: "Analyze the user's message, identify their emotions, and generate an appropriate response. Consider the emotion analysis data and past conversation history to ensure a natural response." This allows the user to feel understood and experience a pleasant communication experience.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The user operates the terminal to input a message. The input message may be in text format, or it may be a stamp or voice message. The terminal converts this message into digital data and sends it to the server. The input is the user's message, and the output is the digital data sent to the server.

[0348] Step 2:

[0349] The server receives digital data transmitted from the terminal. This received data is input into the emotion engine, which analyzes factors such as voice tone, input speed, and phrase patterns. Through this analysis, the server can identify the user's emotions. The input is digital data, and the output is identified emotion information.

[0350] Step 3:

[0351] The server analyzes conversation history by referencing past interactions with the user. From this historical data, it extracts user response patterns associated with identified emotions, preparing more natural and personalized responses. The input is conversation history data, and the output is the analyzed response patterns.

[0352] Step 4:

[0353] Using a generative AI model, the server generates an appropriate response based on identified sentiment information and analyzed response patterns. At this stage, stamps and emojis are also selected and included in the response. The input is sentiment information and response patterns, and the output is a constructed, natural-sounding response message.

[0354] Step 5:

[0355] The server sends the generated reply message to the terminal. The terminal displays the received response to the user, conveying emotions through appropriate stamps and emojis, thereby facilitating natural communication with the user. The input is the reply message, and the output is the visual display to the user.

[0356] (Application Example 2)

[0357] 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."

[0358] In recent years, as online activities have increased, user anxiety about security risks has also grown. Traditional methods require users to assess and respond to risks themselves, which can lead to emotional stress. Furthermore, the difficulty in responding to security concerns immediately and appropriately remains a challenge.

[0359] 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.

[0360] In this invention, the server includes means for transmitting data received by the user terminal to an information processing device; means for analyzing the data in the information processing device to identify emotions; means for analyzing patterns by referring to the user's past behavior history in the information processing device; means for generating appropriate information using a generated AI based on emotions and user patterns; means for transmitting the generated information to the user terminal and automatically providing a response; and means for detecting potential threats from the received data in the user terminal and generating a warning. As a result, users can continue their online activities with peace of mind and receive quick and appropriate responses while reducing emotional burden.

[0361] A "user terminal" is an electronic device owned and used by an individual, equipped with functions for communication and information processing.

[0362] An "information processing device" is a device that electronically receives, analyzes, stores, and transmits data.

[0363] A "means of identifying emotions" refers to a system that analyzes collected data and has the function of identifying the user's emotional state.

[0364] "Past activity history" refers to a record of actions and responses the user has taken up to that point.

[0365] "Methods for analyzing patterns" refer to functions that analyze user behavioral trends based on past behavioral history.

[0366] "Generative AI" refers to artificial intelligence technology that automatically generates information and has the ability to perform natural language processing.

[0367] "Appropriate information" refers to responses and suggestions that are deemed beneficial to the user based on the analyzed data.

[0368] A "potential threat" refers to a situation or factor that could potentially affect the security of user data or systems.

[0369] A "means of generating warnings" refers to a function that alerts the user when a potential threat is detected.

[0370] This system combines a user terminal, an information processing device (server), and a generative AI model to provide empathetic responses based on emotions. The user terminal is a communication device that the user uses on a daily basis, such as a smartphone or tablet, and transmits data obtained during online activities to the information processing device.

[0371] The information processing device functions as a server, receiving data transmitted from user terminals in real time. This data includes text input, voice messages, and browsing history. The server analyzes this data using means to identify emotions, and identifies the user's emotional state, particularly anxiety. The emotion analysis utilizes an emotion engine equipped with natural language processing technology.

[0372] Once an emotion is identified, the server analyzes patterns by referencing past behavioral history. This analysis is performed to understand the user's past behavior and emotional changes, and to predict potential security risks. Based on this information, the generative AI generates appropriate information for the user. In particular, if a potential threat is detected, it generates empathetic and actionable security recommendations.

[0373] The generated information is sent to the user's terminal as a response message and displayed to the user immediately. This allows the user to quickly take action against the risk while receiving emotional support. An example of a prompt message would be: "The user's emotional state has been detected as anxious, and the problematic email has been opened. Please generate empathetic and appropriate suggestions for how to respond."

[0374] This system allows users to use the online environment with peace of mind, reducing the emotional burden associated with the process. Furthermore, this loop continuously improves the quality of security measures.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The user terminal prepares data collected from online activities (e.g., browsing history, message content). This data is transmitted to the information processing device using its communication function.

[0378] Step 2:

[0379] The server receives data sent from the user's terminal in real time. To analyze the received data, it uses an emotion engine equipped with natural language processing technology. This engine identifies the user's emotions based on the voice tone and text content of the input data. The output provides information classifying the emotions.

[0380] Step 3:

[0381] The server combines identified sentiment data with past behavioral history data to analyze user behavior patterns. This analysis utilizes machine learning algorithms. Based on the input data, it outputs potential risks and general behavioral tendencies.

[0382] Step 4:

[0383] The server uses a generative AI model to generate appropriate information and suggestions based on the user's emotional state and behavioral patterns. The generated text is created considering specific conditions indicated by prompt sentences. The output is the generated empathetic messages and suggestions.

[0384] Step 5:

[0385] The generated information is sent from the server to the user's terminal. The user's terminal immediately displays the received message on its interface. This allows the user to take immediate action while feeling emotionally supported.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] In embodiments of the present invention, an emotion-based automatic reply system is provided by integrating a user terminal, server-based data processing technology, and generative AI. The user terminal is typically an electronic device such as a smartphone or computer, which has the function of allowing the user to input and send messages. These messages can take a wide range of forms, including text, stamps, images, and voice messages.

[0403] Message data received by the terminal is sent to a server via the internet. The server has powerful processing capabilities and the necessary programs and processes to analyze this data. First, the server applies natural language processing techniques to the received message to identify the emotion contained within it. If stamps or emojis are included, predefined emotion tags are used to infer the emotion.

[0404] Furthermore, the server uses the sentiment analysis results and the user's past conversation history to analyze the user's communication style and reply patterns. Based on this analysis, the generative AI generates an appropriate reply that empathizes with the user's current emotions. The generated reply may be in text format or may include appropriate stamps or emojis. In this process, the server sends the generated reply message to the user's terminal, and the user can receive it immediately.

[0405] As a concrete example, suppose a user sends a message from their device saying, "I'm tired from work today." The server analyzes this message and identifies the emotion as "tired." Then, referring to past conversation history, the generating AI creates a reply saying, "You had a tough day. Take a good rest," and attaches a relaxing stamp to it. This reply is immediately sent to the user's device, and the user feels acknowledged and encouraged. This system allows users to enjoy fast and emotionally empathetic communication.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The user composes a message using their device and presses the send button. The device receives this message data and prepares to send it to the server.

[0409] Step 2:

[0410] The device sends message data to the server. The data is sent in various formats, including text, stamps, images, and audio.

[0411] Step 3:

[0412] The server analyzes the message data it receives. A natural language processing model is applied to the text data to identify emotions. For stamps and emojis, the emotional information embedded within them is used to infer emotions.

[0413] Step 4:

[0414] The server extracts past conversation history from the database and analyzes the user's reply patterns and communication style. This forms the basis for determining what kind of reply is appropriate.

[0415] Step 5:

[0416] The server uses a generative AI to generate the optimal response based on the sentiment analysis results and the user's response patterns. During this process, the server also has the option to include stamps or emojis in the generated response.

[0417] Step 6:

[0418] The server generates a reply and sends it to the user's terminal. The user can instantly receive the automatically generated reply.

[0419] Step 7:

[0420] The user enters feedback on the automated reply they received. This feedback is sent to the server via the device.

[0421] Step 8:

[0422] The server receives feedback and stores it in a database. This data is used to improve the AI ​​model later on, playing a role in increasing the accuracy and empathy of the responses.

[0423] (Example 1)

[0424] 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."

[0425] In modern society, there is a demand for rapid and emotionally empathetic communication via information and communication devices. However, conventional automated response systems have faced challenges in accurately identifying users' emotions and generating appropriate responses. Furthermore, systems that provide fixed responses without fully utilizing the user's past interaction history have failed to provide users with a satisfying communication experience.

[0426] 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.

[0427] In this invention, the server includes means for transmitting information received by an information processing device operated by the user to a communication device, means for analyzing the information in the communication device to identify emotions, and means for analyzing response patterns by referring to the user's past interaction history in the communication device. This enables the generation of appropriate responses that empathize with the user's emotions, allowing for rapid and highly satisfying communication.

[0428] An "information processing device" is a device that users operate to send and receive information, and generally refers to electronic devices such as smartphones and computers.

[0429] A "communication device" is a device that analyzes information received from an information processing device and connects it to other system components, and in particular, it plays the role of a server.

[0430] "Means of identifying emotions" refers to technologies that utilize natural language processing techniques to identify emotions contained in messages, symbols, and image representations.

[0431] "Means for analyzing response patterns" refers to technologies that analyze a user's past interaction history and determine what kind of response should be given based on that.

[0432] "Generative AI" refers to an artificial intelligence model that automatically generates appropriate responses based on the user's emotions and interaction history.

[0433] "Training data" refers to data, including evaluations and feedback, used to improve the accuracy of AI models.

[0434] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language, and is used for sentiment recognition and text analysis.

[0435] "Symbols and visual representations" refer to non-textual information such as stamps and emojis, which serve as means to complement the emotions and nuances of a message.

[0436] The automated reply system in this invention provides high-quality communication based on the user's emotions by integrating an information processing device, a communication device, and a generative AI model.

[0437] Users use smartphones or computers as information processing devices to input messages. The information can take various forms, including text, stamps, images, and voice messages. These information processing devices have the function of transmitting the input messages to communication devices.

[0438] Information received by the terminal is transmitted to the server via the internet. The communication device has powerful processing capabilities, and programs including natural language processing technologies (e.g., spaCy and BERT) are used to analyze the information. This identifies the emotions embedded in the message. Emotional information from stamps and emojis is also interpreted by referring to predefined emotion tags.

[0439] The server further analyzes response patterns using the user's past conversation history stored in a database on the server. This analyzed data is then used by a generating AI model (e.g., GPT-4 or LLaMA) to produce an appropriate response that empathizes with the user's emotions. This response may also include appropriate symbols or visual representations.

[0440] For example, if a user enters the message "I'm tired from work today," the server identifies the emotion as "tired." The generative AI model then references the user's past interaction history and generates an empathetic reply such as "You had a tough day. Take a good rest," along with a relaxing stamp. This reply message is immediately sent to the user's device.

[0441] An example of a prompt message is: "The user says, 'I'm tired from work today.' Generate an appropriate response to this message. The emotion is 'tired,' and consider past conversation history."

[0442] This system allows users to enjoy quick responses while gaining the reassurance that their feelings are understood.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] When a user enters a message into the information processing device and presses the send button, data is entered in various formats such as text, stamps, images, and voice messages. The entered data is temporarily stored within the information processing device.

[0446] Step 2:

[0447] The terminal receives user input data and transmits it to the server via the internet. The input data is protected by an encryption protocol (e.g., SSL / TLS) before transmission, ensuring data security.

[0448] Step 3:

[0449] The server analyzes the received data. First, natural language processing techniques (e.g., spaCy or BERT) are used to identify the sentiment of the text-based message. If stamps or emojis are included, they are mapped to predefined sentiment tags to infer the sentiment. The output of this step is identified sentiment information, such as "tired."

[0450] Step 4:

[0451] The server references past user interactions and retrieves conversation history from the database. Based on the retrieved history data, it analyzes the user's response patterns. The output of this data analysis process is analytical data showing the user's past response trends.

[0452] Step 5:

[0453] The server uses a generative AI model (e.g., GPT-4 or LLaMA) to generate an appropriate response based on identified emotions and analyzed response patterns. The AI ​​is instructed using example prompts to generate an appropriate empathetic message. The output of this step is the generated response, expressed in text format.

[0454] Step 6:

[0455] The server generates a reply, to which appropriate symbols and image representations are attached to create the final message. The created final message is then sent back to the user's information processing device via the communication network.

[0456] Step 7:

[0457] The device receives the sent reply message and displays it to the user immediately. The user can see this message and feel understood and empathized with.

[0458] (Application Example 1)

[0459] 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."

[0460] In modern communication, there is a need to quickly and accurately share emotions based on the messages sent by users. However, typical automated response systems often fail to adequately identify user emotions, making it difficult to show appropriate empathy. Furthermore, while it is expected that providing appropriate content according to the user's emotions would further increase satisfaction, this is not currently achieved. This invention aims to solve these problems.

[0461] 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.

[0462] In this invention, the server includes means for transmitting communication data received by the user terminal to an information processing device, means for analyzing the communication data in the information processing device to identify emotions, and means for analyzing the user's past history in the information processing device to analyze reaction patterns. This makes it possible to automatically provide appropriate responses and content recommendations based on the user's emotions, thereby providing a more personalized user experience.

[0463] A "user terminal" is an electronic device that has the function of sending and receiving messages, and is a device for users to input information.

[0464] An "information processing device" is a computer system that receives and analyzes data transmitted from a user terminal.

[0465] "Communication data" refers to messages and information transmitted from a user terminal to an information processing device, and includes data such as text, audio, and images.

[0466] "Means of identifying emotions" refers to the techniques and processes of analyzing communication data and identifying the emotions contained within that data.

[0467] A "reaction pattern" refers to a tendency in behavior that responds to specific emotions or situations, determined based on a user's past history.

[0468] "Generative AI" is an artificial intelligence technology that generates appropriate responses and information based on user input and circumstances.

[0469] "Means of automated response" refers to technologies that send rapid responses to users based on analyzed and generated information.

[0470] "Content" refers to various types of information and entertainment provided to users, such as music, videos, and text.

[0471] "Past history" refers to a record of a user's past actions and choices, and is important data used for personalization.

[0472] To implement this invention, a user terminal is required. The user terminal consists of an electronic device such as a smartphone or a computer, and the user uses the terminal to input messages and data. The communication data entered by the user is transmitted to an information processing device via the internet.

[0473] Next, a server is used as the information processing device to analyze the received communication data. Here, natural language processing technology is used to identify the sentiment of the message. The hardware consists of a server with a high-performance processor, and the software utilizes sentiment analysis models such as Hugging Face Transformers.

[0474] Once an emotion is identified, the server then analyzes the user's past history to identify reaction patterns. This analysis requires large-capacity data storage and sophisticated algorithms. Based on the results obtained, a generative AI generates appropriate responses based on the emotion. OpenAI's GPT model, among others, is used for the generative AI.

[0475] The server then determines the generated response and appropriate content, and sends it to the user's terminal. Content selection utilizes services such as the Spotify API and The Movie Database API, and suggestions are made taking into account the user's past viewing history.

[0476] For example, if a user sends a message such as "I'm feeling down today," the server analyzes it to identify the emotion of sadness. Next, the generative AI generates a response using a prompt such as "What movies or music do you recommend for when you're feeling down?" and can suggest movies like "Avatar" or "Spirited Away," or music playlists like "Rainy Day Jazz," to the user.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The user enters and sends a message on their device (smartphone or computer). This input is processed as text data on the device and sent to the server via the internet. The input can be a natural language message conveying the user's emotions. The output is a data packet sent to the server via the network.

[0480] Step 2:

[0481] The server analyzes the received communication data. The input is text data sent by the user. The server uses natural language processing techniques to identify emotions and processes the data using emotion analysis models such as Hugging Face Transformers. The output is the identified emotion information.

[0482] Step 3:

[0483] The server analyzes reaction patterns by referring to the user's past history. Inputs include identified emotional information and the user's past history data. Based on this information, it extracts and analyzes past reaction patterns from the database to obtain data points for use in the generating AI. The output is the analysis results regarding reaction patterns.

[0484] Step 4:

[0485] The server uses generative AI to generate appropriate responses based on emotions. Inputs include emotion information and response pattern analysis results. It utilizes OpenAI's GPT model to generate the response the user should receive. The type of response generated is controlled by the design of the prompt. The output is a generated natural language reply message.

[0486] Step 5:

[0487] The server selects and suggests content appropriate to the generated response. Inputs include emotion-based response messages and the user's viewing history data. Using APIs such as Spotify and The Movie Database, it searches for selected content and identifies movies and music that match the user's interests. The output is a list of recommended content.

[0488] Step 6:

[0489] The server sends the generated reply message and content list to the user's terminal. This process involves converting the reply message and content data into packets and transmitting them over the network. The input is the reply message and content list stored on the server, and the output is the information displayed on the user's terminal.

[0490] 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.

[0491] This invention is a system combining a user terminal, a server, and an emotion engine that automatically generates empathetic responses while recognizing the user's emotions. The user enters a message using the terminal, and this message is sent to the server in the form of text, stamps, images, voice messages, etc. The server receives the message data and uses the emotion engine to recognize the user's emotions. This emotion engine analyzes emotions using a combination of various data sources, such as voice tone, keyboard input speed, and phrase patterns.

[0492] In addition, the server refers to past conversation history and analyzes the user's reply patterns. This prepares it not only to identify emotions but also to select the most appropriate reply accordingly. The generative AI generates natural and appropriate replies based on the recognized emotions and past data. The generated replies include appropriate stamps and emojis and are sent to the user quickly.

[0493] As a concrete example, consider a scenario where a user sends the message, "I'm so happy today!" The server uses an emotion engine to identify this phrase as "joy." Based on the analysis, the generating AI selects a reply such as, "That's great! I'd love to hear what happened!" and sends it back to the user with an appropriate emoji expressing joy. Through this process, the user experiences pleasant communication because their emotions are correctly recognized and responded to appropriately.

[0494] This system also features a feedback loop; when a user provides feedback on a reply, it is collected by the server. This feedback data is used to improve the accuracy of the AI ​​model, contributing to higher quality replies in the future. In this way, users can consistently enjoy high-quality communication.

[0495] The following describes the processing flow.

[0496] Step 1:

[0497] The user types a message using the terminal and presses the send button. The terminal receives this message data and prepares to send it to the server.

[0498] Step 2:

[0499] The device sends the message data to the server. The data is sent in one of the following formats: text, stamps, images, or audio.

[0500] Step 3:

[0501] The server passes the received message data to the emotion engine. The emotion engine analyzes the text data using natural language processing techniques to identify emotions. Simultaneously, if there is an audio message, it performs audio analysis, and analyzes images using facial recognition technology to supplement the emotions.

[0502] Step 4:

[0503] Based on the sentiment identification results, the server references the user's past conversation history from a database and analyzes their response patterns. This analysis identifies which response is most appropriate for the user.

[0504] Step 5:

[0505] The server uses AI to generate reply messages based on sentiment analysis results and response patterns. The generated messages are enhanced with appropriate emojis and stamps as needed to emphasize the tone of the message to the recipient.

[0506] Step 6:

[0507] The server sends the generated reply message to the user's terminal. The user receives this reply immediately and can confirm the automated response from the system.

[0508] Step 7:

[0509] Users provide feedback on the automated replies they receive. This feedback includes information about the accuracy and satisfaction level of the replies.

[0510] Step 8:

[0511] The device sends user feedback to the server. The server stores this feedback in a database and uses it as training data for an AI model to improve system performance.

[0512] (Example 2)

[0513] 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."

[0514] In modern communication systems, accurately recognizing and responding naturally to user emotions is a challenging task. Existing systems can only superficially interpret the content of written messages and fail to provide responses that adequately reflect the user's inner feelings. This can limit the user's communication experience and potentially reduce satisfaction. Furthermore, simple automated responses alone cannot address the individual needs of users.

[0515] 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.

[0516] In this invention, the server includes means for analyzing the content of data to determine emotions, means for referring to historical information to analyze the reply format, and means for generating an appropriate reply based on the emotions and reply format. This enables a deeper understanding of the user's message and a more empathetic and natural response.

[0517] A "user terminal" refers to a device used by a user to input messages and communicate with a server.

[0518] A "server" refers to a central computing device that analyzes data received from user terminals and identifies emotions.

[0519] "Data" refers to all information, such as text, audio, stamps, and images, that is sent from the user's terminal to the server.

[0520] "Emotional analysis" refers to the process of analyzing data sent by users on a server to determine the user's inner emotional state.

[0521] "History information" refers to information used to analyze reply patterns and other factors based on a user's past conversation history.

[0522] A "generative AI model" refers to a form of artificial intelligence technology that generates appropriate responses based on sentiment analysis and historical information.

[0523] "Automatic response" refers to the process where a server generates a reply and sends it to the user's terminal, providing an instant response to the user's message.

[0524] This invention is implemented by a system combining a user terminal, a server, and a generative AI model. The user operates the terminal to input a message, which is sent to the server in the form of text, stamps, images, or voice messages. The terminal's role is to convert the input message into the appropriate data format and transfer it to the server.

[0525] The server receives data sent from the user's terminal and uses sentiment analysis technology to identify the user's emotions. Sentiment analysis uses various data sources, including voice tone, input speed, and phrase patterns. Furthermore, the server refers to past history information and analyzes response patterns from the user's past conversations to prepare appropriate and personalized responses for the user.

[0526] The generative AI model generates appropriate responses based on emotional and historical information recognized by the server. Stamps and emojis are added to the generated responses as needed, and they are adjusted to be more empathetic and natural. The server then sends the generated response to the user's device, which automatically replies.

[0527] As a concrete example, consider a scenario where a user sends the message "I had a great day!" from their device to the server. The server interprets this message as expressing the emotion of "fun," and based on past interactions, its AI model generates a response such as "I'd love to know what fun things happened!" It then adds an emoji representing the feeling of happiness and sends it back to the user.

[0528] An example of a prompt might be: "Analyze the user's message, identify their emotions, and generate an appropriate response. Consider the emotion analysis data and past conversation history to ensure a natural response." This allows the user to feel understood and experience a pleasant communication experience.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] The user operates the terminal to input a message. The input message may be in text format, or it may be a stamp or voice message. The terminal converts this message into digital data and sends it to the server. The input is the user's message, and the output is the digital data sent to the server.

[0532] Step 2:

[0533] The server receives digital data transmitted from the terminal. This received data is input into the emotion engine, which analyzes factors such as voice tone, input speed, and phrase patterns. Through this analysis, the server can identify the user's emotions. The input is digital data, and the output is identified emotion information.

[0534] Step 3:

[0535] The server analyzes conversation history by referencing past interactions with the user. From this historical data, it extracts user response patterns associated with identified emotions, preparing more natural and personalized responses. The input is conversation history data, and the output is the analyzed response patterns.

[0536] Step 4:

[0537] Using a generative AI model, the server generates an appropriate response based on identified sentiment information and analyzed response patterns. At this stage, stamps and emojis are also selected and included in the response. The input is sentiment information and response patterns, and the output is a constructed, natural-sounding response message.

[0538] Step 5:

[0539] The server sends the generated reply message to the terminal. The terminal displays the received response to the user, conveying emotions through appropriate stamps and emojis, thereby facilitating natural communication with the user. The input is the reply message, and the output is the visual display to the user.

[0540] (Application Example 2)

[0541] 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."

[0542] In recent years, as online activities have increased, user anxiety about security risks has also grown. Traditional methods require users to assess and respond to risks themselves, which can lead to emotional stress. Furthermore, the difficulty in responding to security concerns immediately and appropriately remains a challenge.

[0543] 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.

[0544] In this invention, the server includes means for transmitting data received by the user terminal to an information processing device; means for analyzing the data in the information processing device to identify emotions; means for analyzing patterns by referring to the user's past behavior history in the information processing device; means for generating appropriate information using a generated AI based on emotions and user patterns; means for transmitting the generated information to the user terminal and automatically providing a response; and means for detecting potential threats from the received data in the user terminal and generating a warning. As a result, users can continue their online activities with peace of mind and receive quick and appropriate responses while reducing emotional burden.

[0545] A "user terminal" is an electronic device owned and used by an individual, equipped with functions for communication and information processing.

[0546] An "information processing device" is a device that electronically receives, analyzes, stores, and transmits data.

[0547] A "means of identifying emotions" refers to a system that analyzes collected data and has the function of identifying the user's emotional state.

[0548] "Past activity history" refers to a record of actions and responses the user has taken up to that point.

[0549] "Methods for analyzing patterns" refer to functions that analyze user behavioral trends based on past behavioral history.

[0550] "Generative AI" refers to artificial intelligence technology that automatically generates information and has the ability to perform natural language processing.

[0551] "Appropriate information" refers to responses and suggestions that are deemed beneficial to the user based on the analyzed data.

[0552] A "potential threat" refers to a situation or factor that could potentially affect the security of user data or systems.

[0553] A "means of generating warnings" refers to a function that alerts the user when a potential threat is detected.

[0554] This system combines a user terminal, an information processing device (server), and a generative AI model to provide empathetic responses based on emotions. The user terminal is a communication device that the user uses on a daily basis, such as a smartphone or tablet, and transmits data obtained during online activities to the information processing device.

[0555] The information processing device functions as a server, receiving data transmitted from user terminals in real time. This data includes text input, voice messages, and browsing history. The server analyzes this data using means to identify emotions, and identifies the user's emotional state, particularly anxiety. The emotion analysis utilizes an emotion engine equipped with natural language processing technology.

[0556] Once an emotion is identified, the server analyzes patterns by referencing past behavioral history. This analysis is performed to understand the user's past behavior and emotional changes, and to predict potential security risks. Based on this information, the generative AI generates appropriate information for the user. In particular, if a potential threat is detected, it generates empathetic and actionable security recommendations.

[0557] The generated information is sent to the user's terminal as a response message and displayed to the user immediately. This allows the user to quickly take action against the risk while receiving emotional support. An example of a prompt message would be: "The user's emotional state has been detected as anxious, and the problematic email has been opened. Please generate empathetic and appropriate suggestions for how to respond."

[0558] This system allows users to use the online environment with peace of mind, reducing the emotional burden associated with the process. Furthermore, this loop continuously improves the quality of security measures.

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The user terminal prepares data collected from online activities (e.g., browsing history, message content). This data is transmitted to the information processing device using its communication function.

[0562] Step 2:

[0563] The server receives data sent from the user's terminal in real time. To analyze the received data, it uses an emotion engine equipped with natural language processing technology. This engine identifies the user's emotions based on the voice tone and text content of the input data. The output provides information classifying the emotions.

[0564] Step 3:

[0565] The server combines identified sentiment data with past behavioral history data to analyze user behavior patterns. This analysis utilizes machine learning algorithms. Based on the input data, it outputs potential risks and general behavioral tendencies.

[0566] Step 4:

[0567] The server uses a generative AI model to generate appropriate information and suggestions based on the user's emotional state and behavioral patterns. The generated text is created considering specific conditions indicated by prompt sentences. The output is the generated empathetic messages and suggestions.

[0568] Step 5:

[0569] The generated information is sent from the server to the user's terminal. The user's terminal immediately displays the received message on its interface. This allows the user to take immediate action while feeling emotionally supported.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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".

[0587] In embodiments of the present invention, an emotion-based automatic reply system is provided by integrating a user terminal, server-based data processing technology, and generative AI. The user terminal is typically an electronic device such as a smartphone or computer, which has the function of allowing the user to input and send messages. These messages can take a wide range of forms, including text, stamps, images, and voice messages.

[0588] Message data received by the terminal is sent to a server via the internet. The server has powerful processing capabilities and the necessary programs and processes to analyze this data. First, the server applies natural language processing techniques to the received message to identify the emotion contained within it. If stamps or emojis are included, predefined emotion tags are used to infer the emotion.

[0589] Furthermore, the server uses the sentiment analysis results and the user's past conversation history to analyze the user's communication style and reply patterns. Based on this analysis, the generative AI generates an appropriate reply that empathizes with the user's current emotions. The generated reply may be in text format or may include appropriate stamps or emojis. In this process, the server sends the generated reply message to the user's terminal, and the user can receive it immediately.

[0590] As a concrete example, suppose a user sends a message from their device saying, "I'm tired from work today." The server analyzes this message and identifies the emotion as "tired." Then, referring to past conversation history, the generating AI creates a reply saying, "You had a tough day. Take a good rest," and attaches a relaxing stamp to it. This reply is immediately sent to the user's device, and the user feels acknowledged and encouraged. This system allows users to enjoy fast and emotionally empathetic communication.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The user composes a message using their device and presses the send button. The device receives this message data and prepares to send it to the server.

[0594] Step 2:

[0595] The device sends message data to the server. The data is sent in various formats, including text, stamps, images, and audio.

[0596] Step 3:

[0597] The server analyzes the message data it receives. A natural language processing model is applied to the text data to identify emotions. For stamps and emojis, the emotional information embedded within them is used to infer emotions.

[0598] Step 4:

[0599] The server extracts past conversation history from the database and analyzes the user's reply patterns and communication style. This forms the basis for determining what kind of reply is appropriate.

[0600] Step 5:

[0601] The server uses a generative AI to generate the optimal response based on the sentiment analysis results and the user's response patterns. During this process, the server also has the option to include stamps or emojis in the generated response.

[0602] Step 6:

[0603] The server generates a reply and sends it to the user's terminal. The user can instantly receive the automatically generated reply.

[0604] Step 7:

[0605] The user enters feedback on the automated reply they received. This feedback is sent to the server via the device.

[0606] Step 8:

[0607] The server receives feedback and stores it in a database. This data is used to improve the AI ​​model later on, playing a role in increasing the accuracy and empathy of the responses.

[0608] (Example 1)

[0609] 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".

[0610] In modern society, there is a demand for rapid and emotionally empathetic communication via information and communication devices. However, conventional automated response systems have faced challenges in accurately identifying users' emotions and generating appropriate responses. Furthermore, systems that provide fixed responses without fully utilizing the user's past interaction history have failed to provide users with a satisfying communication experience.

[0611] 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.

[0612] In this invention, the server includes means for transmitting information received by an information processing device operated by the user to a communication device, means for analyzing the information in the communication device to identify emotions, and means for analyzing response patterns by referring to the user's past interaction history in the communication device. This enables the generation of appropriate responses that empathize with the user's emotions, allowing for rapid and highly satisfying communication.

[0613] An "information processing device" is a device that users operate to send and receive information, and generally refers to electronic devices such as smartphones and computers.

[0614] A "communication device" is a device that analyzes information received from an information processing device and connects it to other system components, and in particular, it plays the role of a server.

[0615] "Means of identifying emotions" refers to technologies that utilize natural language processing techniques to identify emotions contained in messages, symbols, and image representations.

[0616] "Means for analyzing response patterns" refers to technologies that analyze a user's past interaction history and determine what kind of response should be given based on that.

[0617] "Generative AI" refers to an artificial intelligence model that automatically generates appropriate responses based on the user's emotions and interaction history.

[0618] "Training data" refers to data, including evaluations and feedback, used to improve the accuracy of AI models.

[0619] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language, and is used for sentiment recognition and text analysis.

[0620] "Symbols and visual representations" refer to non-textual information such as stamps and emojis, which serve as means to complement the emotions and nuances of a message.

[0621] The automated reply system in this invention provides high-quality communication based on the user's emotions by integrating an information processing device, a communication device, and a generative AI model.

[0622] Users use smartphones or computers as information processing devices to input messages. The information can take various forms, including text, stamps, images, and voice messages. These information processing devices have the function of transmitting the input messages to communication devices.

[0623] Information received by the terminal is transmitted to the server via the internet. The communication device has powerful processing capabilities, and programs including natural language processing technologies (e.g., spaCy and BERT) are used to analyze the information. This identifies the emotions embedded in the message. Emotional information from stamps and emojis is also interpreted by referring to predefined emotion tags.

[0624] The server further analyzes response patterns using the user's past conversation history stored in a database on the server. This analyzed data is then used by a generating AI model (e.g., GPT-4 or LLaMA) to produce an appropriate response that empathizes with the user's emotions. This response may also include appropriate symbols or visual representations.

[0625] For example, if a user enters the message "I'm tired from work today," the server identifies the emotion as "tired." The generative AI model then references the user's past interaction history and generates an empathetic reply such as "You had a tough day. Take a good rest," along with a relaxing stamp. This reply message is immediately sent to the user's device.

[0626] An example of a prompt message is: "The user says, 'I'm tired from work today.' Generate an appropriate response to this message. The emotion is 'tired,' and consider past conversation history."

[0627] This system allows users to enjoy quick responses while gaining the reassurance that their feelings are understood.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] When a user enters a message into the information processing device and presses the send button, data is entered in various formats such as text, stamps, images, and voice messages. The entered data is temporarily stored within the information processing device.

[0631] Step 2:

[0632] The terminal receives user input data and transmits it to the server via the internet. The input data is protected by an encryption protocol (e.g., SSL / TLS) before transmission, ensuring data security.

[0633] Step 3:

[0634] The server analyzes the received data. First, natural language processing techniques (e.g., spaCy or BERT) are used to identify the sentiment of the text-based message. If stamps or emojis are included, they are mapped to predefined sentiment tags to infer the sentiment. The output of this step is identified sentiment information, such as "tired."

[0635] Step 4:

[0636] The server references past user interactions and retrieves conversation history from the database. Based on the retrieved history data, it analyzes the user's response patterns. The output of this data analysis process is analytical data showing the user's past response trends.

[0637] Step 5:

[0638] The server uses a generative AI model (e.g., GPT-4 or LLaMA) to generate an appropriate response based on identified emotions and analyzed response patterns. The AI ​​is instructed using example prompts to generate an appropriate empathetic message. The output of this step is the generated response, expressed in text format.

[0639] Step 6:

[0640] The server generates a reply, to which appropriate symbols and image representations are attached to create the final message. The created final message is then sent back to the user's information processing device via the communication network.

[0641] Step 7:

[0642] The device receives the sent reply message and displays it to the user immediately. The user can see this message and feel understood and empathized with.

[0643] (Application Example 1)

[0644] 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".

[0645] In modern communication, there is a need to quickly and accurately share emotions based on the messages sent by users. However, typical automated response systems often fail to adequately identify user emotions, making it difficult to show appropriate empathy. Furthermore, while it is expected that providing appropriate content according to the user's emotions would further increase satisfaction, this is not currently achieved. This invention aims to solve these problems.

[0646] 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.

[0647] In this invention, the server includes means for transmitting communication data received by the user terminal to an information processing device, means for analyzing the communication data in the information processing device to identify emotions, and means for analyzing the user's past history in the information processing device to analyze reaction patterns. This makes it possible to automatically provide appropriate responses and content recommendations based on the user's emotions, thereby providing a more personalized user experience.

[0648] A "user terminal" is an electronic device that has the function of sending and receiving messages, and is a device for users to input information.

[0649] An "information processing device" is a computer system that receives and analyzes data transmitted from a user terminal.

[0650] "Communication data" refers to messages and information transmitted from a user terminal to an information processing device, and includes data such as text, audio, and images.

[0651] "Means of identifying emotions" refers to the techniques and processes of analyzing communication data and identifying the emotions contained within that data.

[0652] A "reaction pattern" refers to a tendency in behavior that responds to specific emotions or situations, determined based on a user's past history.

[0653] "Generative AI" is an artificial intelligence technology that generates appropriate responses and information based on user input and circumstances.

[0654] "Means of automated response" refers to technologies that send rapid responses to users based on analyzed and generated information.

[0655] "Content" refers to various types of information and entertainment provided to users, such as music, videos, and text.

[0656] "Past history" refers to a record of a user's past actions and choices, and is important data used for personalization.

[0657] To implement this invention, a user terminal is required. The user terminal consists of an electronic device such as a smartphone or a computer, and the user uses the terminal to input messages and data. The communication data entered by the user is transmitted to an information processing device via the internet.

[0658] Next, a server is used as the information processing device to analyze the received communication data. Here, natural language processing technology is used to identify the sentiment of the message. The hardware consists of a server with a high-performance processor, and the software utilizes sentiment analysis models such as Hugging Face Transformers.

[0659] Once an emotion is identified, the server then analyzes the user's past history to identify reaction patterns. This analysis requires large-capacity data storage and sophisticated algorithms. Based on the results obtained, a generative AI generates appropriate responses based on the emotion. OpenAI's GPT model, among others, is used for the generative AI.

[0660] The server then determines the generated response and appropriate content, and sends it to the user's terminal. Content selection utilizes services such as the Spotify API and The Movie Database API, and suggestions are made taking into account the user's past viewing history.

[0661] For example, if a user sends a message such as "I'm feeling down today," the server analyzes it to identify the emotion of sadness. Next, the generative AI generates a response using a prompt such as "What movies or music do you recommend for when you're feeling down?" and can suggest movies like "Avatar" or "Spirited Away," or music playlists like "Rainy Day Jazz," to the user.

[0662] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0663] Step 1:

[0664] The user enters and sends a message on their device (smartphone or computer). This input is processed as text data on the device and sent to the server via the internet. The input can be a natural language message conveying the user's emotions. The output is a data packet sent to the server via the network.

[0665] Step 2:

[0666] The server analyzes the received communication data. The input is text data sent by the user. The server uses natural language processing techniques to identify emotions and processes the data using emotion analysis models such as Hugging Face Transformers. The output is the identified emotion information.

[0667] Step 3:

[0668] The server analyzes reaction patterns by referring to the user's past history. Inputs include identified emotional information and the user's past history data. Based on this information, it extracts and analyzes past reaction patterns from the database to obtain data points for use in the generating AI. The output is the analysis results regarding reaction patterns.

[0669] Step 4:

[0670] The server uses generative AI to generate appropriate responses based on emotions. Inputs include emotion information and response pattern analysis results. It utilizes OpenAI's GPT model to generate the response the user should receive. The type of response generated is controlled by the design of the prompt. The output is a generated natural language reply message.

[0671] Step 5:

[0672] The server selects and suggests content appropriate to the generated response. Inputs include emotion-based response messages and the user's viewing history data. Using APIs such as Spotify and The Movie Database, it searches for selected content and identifies movies and music that match the user's interests. The output is a list of recommended content.

[0673] Step 6:

[0674] The server sends the generated reply message and content list to the user's terminal. This process involves converting the reply message and content data into packets and transmitting them over the network. The input is the reply message and content list stored on the server, and the output is the information displayed on the user's terminal.

[0675] 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.

[0676] This invention is a system combining a user terminal, a server, and an emotion engine that automatically generates empathetic responses while recognizing the user's emotions. The user enters a message using the terminal, and this message is sent to the server in the form of text, stamps, images, voice messages, etc. The server receives the message data and uses the emotion engine to recognize the user's emotions. This emotion engine analyzes emotions using a combination of various data sources, such as voice tone, keyboard input speed, and phrase patterns.

[0677] In addition, the server refers to past conversation history and analyzes the user's reply patterns. This prepares it not only to identify emotions but also to select the most appropriate reply accordingly. The generative AI generates natural and appropriate replies based on the recognized emotions and past data. The generated replies include appropriate stamps and emojis and are sent to the user quickly.

[0678] As a concrete example, consider a scenario where a user sends the message, "I'm so happy today!" The server uses an emotion engine to identify this phrase as "joy." Based on the analysis, the generating AI selects a reply such as, "That's great! I'd love to hear what happened!" and sends it back to the user with an appropriate emoji expressing joy. Through this process, the user experiences pleasant communication because their emotions are correctly recognized and responded to appropriately.

[0679] This system also features a feedback loop; when a user provides feedback on a reply, it is collected by the server. This feedback data is used to improve the accuracy of the AI ​​model, contributing to higher quality replies in the future. In this way, users can consistently enjoy high-quality communication.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The user types a message using the terminal and presses the send button. The terminal receives this message data and prepares to send it to the server.

[0683] Step 2:

[0684] The device sends the message data to the server. The data is sent in one of the following formats: text, stamps, images, or audio.

[0685] Step 3:

[0686] The server passes the received message data to the emotion engine. The emotion engine analyzes the text data using natural language processing techniques to identify emotions. Simultaneously, if there is an audio message, it performs audio analysis, and analyzes images using facial recognition technology to supplement the emotions.

[0687] Step 4:

[0688] Based on the sentiment identification results, the server references the user's past conversation history from a database and analyzes their response patterns. This analysis identifies which response is most appropriate for the user.

[0689] Step 5:

[0690] The server uses AI to generate reply messages based on sentiment analysis results and response patterns. The generated messages are enhanced with appropriate emojis and stamps as needed to emphasize the tone of the message to the recipient.

[0691] Step 6:

[0692] The server sends the generated reply message to the user's terminal. The user receives this reply immediately and can confirm the automated response from the system.

[0693] Step 7:

[0694] Users provide feedback on the automated replies they receive. This feedback includes information about the accuracy and satisfaction level of the replies.

[0695] Step 8:

[0696] The device sends user feedback to the server. The server stores this feedback in a database and uses it as training data for an AI model to improve system performance.

[0697] (Example 2)

[0698] 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".

[0699] In modern communication systems, accurately recognizing and responding naturally to user emotions is a challenging task. Existing systems can only superficially interpret the content of written messages and fail to provide responses that adequately reflect the user's inner feelings. This can limit the user's communication experience and potentially reduce satisfaction. Furthermore, simple automated responses alone cannot address the individual needs of users.

[0700] 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.

[0701] In this invention, the server includes means for analyzing the content of data to determine emotions, means for referring to historical information to analyze the reply format, and means for generating an appropriate reply based on the emotions and reply format. This enables a deeper understanding of the user's message and a more empathetic and natural response.

[0702] A "user terminal" refers to a device used by a user to input messages and communicate with a server.

[0703] A "server" refers to a central computing device that analyzes data received from user terminals and identifies emotions.

[0704] "Data" refers to all information, such as text, audio, stamps, and images, that is sent from the user's terminal to the server.

[0705] "Emotional analysis" refers to the process of analyzing data sent by users on a server to determine the user's inner emotional state.

[0706] "History information" refers to information used to analyze reply patterns and other factors based on a user's past conversation history.

[0707] A "generative AI model" refers to a form of artificial intelligence technology that generates appropriate responses based on sentiment analysis and historical information.

[0708] "Automatic response" refers to the process where a server generates a reply and sends it to the user's terminal, providing an instant response to the user's message.

[0709] This invention is implemented by a system combining a user terminal, a server, and a generative AI model. The user operates the terminal to input a message, which is sent to the server in the form of text, stamps, images, or voice messages. The terminal's role is to convert the input message into the appropriate data format and transfer it to the server.

[0710] The server receives data sent from the user's terminal and uses sentiment analysis technology to identify the user's emotions. Sentiment analysis uses various data sources, including voice tone, input speed, and phrase patterns. Furthermore, the server refers to past history information and analyzes response patterns from the user's past conversations to prepare appropriate and personalized responses for the user.

[0711] The generative AI model generates appropriate responses based on emotional and historical information recognized by the server. Stamps and emojis are added to the generated responses as needed, and they are adjusted to be more empathetic and natural. The server then sends the generated response to the user's device, which automatically replies.

[0712] As a concrete example, consider a scenario where a user sends the message "I had a great day!" from their device to the server. The server interprets this message as expressing the emotion of "fun," and based on past interactions, its AI model generates a response such as "I'd love to know what fun things happened!" It then adds an emoji representing the feeling of happiness and sends it back to the user.

[0713] An example of a prompt might be: "Analyze the user's message, identify their emotions, and generate an appropriate response. Consider the emotion analysis data and past conversation history to ensure a natural response." This allows the user to feel understood and experience a pleasant communication experience.

[0714] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0715] Step 1:

[0716] The user operates the terminal to input a message. The input message may be in text format, or it may be a stamp or voice message. The terminal converts this message into digital data and sends it to the server. The input is the user's message, and the output is the digital data sent to the server.

[0717] Step 2:

[0718] The server receives digital data transmitted from the terminal. This received data is input into the emotion engine, which analyzes factors such as voice tone, input speed, and phrase patterns. Through this analysis, the server can identify the user's emotions. The input is digital data, and the output is identified emotion information.

[0719] Step 3:

[0720] The server analyzes conversation history by referencing past interactions with the user. From this historical data, it extracts user response patterns associated with identified emotions, preparing more natural and personalized responses. The input is conversation history data, and the output is the analyzed response patterns.

[0721] Step 4:

[0722] Using a generative AI model, the server generates an appropriate response based on identified sentiment information and analyzed response patterns. At this stage, stamps and emojis are also selected and included in the response. The input is sentiment information and response patterns, and the output is a constructed, natural-sounding response message.

[0723] Step 5:

[0724] The server sends the generated reply message to the terminal. The terminal displays the received response to the user, conveying emotions through appropriate stamps and emojis, thereby facilitating natural communication with the user. The input is the reply message, and the output is the visual display to the user.

[0725] (Application Example 2)

[0726] 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".

[0727] In recent years, as online activities have increased, user anxiety about security risks has also grown. Traditional methods require users to assess and respond to risks themselves, which can lead to emotional stress. Furthermore, the difficulty in responding to security concerns immediately and appropriately remains a challenge.

[0728] 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.

[0729] In this invention, the server includes means for transmitting data received by the user terminal to an information processing device; means for analyzing the data in the information processing device to identify emotions; means for analyzing patterns by referring to the user's past behavior history in the information processing device; means for generating appropriate information using a generated AI based on emotions and user patterns; means for transmitting the generated information to the user terminal and automatically providing a response; and means for detecting potential threats from the received data in the user terminal and generating a warning. As a result, users can continue their online activities with peace of mind and receive quick and appropriate responses while reducing emotional burden.

[0730] A "user terminal" is an electronic device owned and used by an individual, equipped with functions for communication and information processing.

[0731] An "information processing device" is a device that electronically receives, analyzes, stores, and transmits data.

[0732] A "means of identifying emotions" refers to a system that analyzes collected data and has the function of identifying the user's emotional state.

[0733] "Past activity history" refers to a record of actions and responses the user has taken up to that point.

[0734] "Methods for analyzing patterns" refer to functions that analyze user behavioral trends based on past behavioral history.

[0735] "Generative AI" refers to artificial intelligence technology that automatically generates information and has the ability to perform natural language processing.

[0736] "Appropriate information" refers to responses and suggestions that are deemed beneficial to the user based on the analyzed data.

[0737] A "potential threat" refers to a situation or factor that could potentially affect the security of user data or systems.

[0738] A "means of generating warnings" refers to a function that alerts the user when a potential threat is detected.

[0739] This system combines a user terminal, an information processing device (server), and a generative AI model to provide empathetic responses based on emotions. The user terminal is a communication device that the user uses on a daily basis, such as a smartphone or tablet, and transmits data obtained during online activities to the information processing device.

[0740] The information processing device functions as a server, receiving data transmitted from user terminals in real time. This data includes text input, voice messages, and browsing history. The server analyzes this data using means to identify emotions, and identifies the user's emotional state, particularly anxiety. The emotion analysis utilizes an emotion engine equipped with natural language processing technology.

[0741] Once an emotion is identified, the server analyzes patterns by referencing past behavioral history. This analysis is performed to understand the user's past behavior and emotional changes, and to predict potential security risks. Based on this information, the generative AI generates appropriate information for the user. In particular, if a potential threat is detected, it generates empathetic and actionable security recommendations.

[0742] The generated information is sent to the user's terminal as a response message and displayed to the user immediately. This allows the user to quickly take action against the risk while receiving emotional support. An example of a prompt message would be: "The user's emotional state has been detected as anxious, and the problematic email has been opened. Please generate empathetic and appropriate suggestions for how to respond."

[0743] This system allows users to use the online environment with peace of mind, reducing the emotional burden associated with the process. Furthermore, this loop continuously improves the quality of security measures.

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] The user terminal prepares data collected from online activities (e.g., browsing history, message content). This data is transmitted to the information processing device using its communication function.

[0747] Step 2:

[0748] The server receives data sent from the user's terminal in real time. To analyze the received data, it uses an emotion engine equipped with natural language processing technology. This engine identifies the user's emotions based on the voice tone and text content of the input data. The output provides information classifying the emotions.

[0749] Step 3:

[0750] The server combines identified sentiment data with past behavioral history data to analyze user behavior patterns. This analysis utilizes machine learning algorithms. Based on the input data, it outputs potential risks and general behavioral tendencies.

[0751] Step 4:

[0752] The server uses a generative AI model to generate appropriate information and suggestions based on the user's emotional state and behavioral patterns. The generated text is created considering specific conditions indicated by prompt sentences. The output is the generated empathetic messages and suggestions.

[0753] Step 5:

[0754] The generated information is sent from the server to the user's terminal. The user's terminal immediately displays the received message on its interface. This allows the user to take immediate action while feeling emotionally supported.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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."

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0776] The following is further disclosed regarding the embodiments described above.

[0777] (Claim 1)

[0778] A means of sending message data received on the user terminal to the server,

[0779] A means of analyzing the message data on the server to identify the emotion,

[0780] A method for analyzing reply patterns by referring to the user's past conversation history on the server,

[0781] A means of generating appropriate replies using a generation AI based on emotions and user response patterns,

[0782] A system that includes a means of sending generated replies to the user's terminal and automatically responding.

[0783] (Claim 2)

[0784] A means for the user to input feedback on an automated response and send that feedback to the server,

[0785] The system according to claim 1, further comprising means for accumulating feedback on a server and using it as training data to improve the accuracy of an AI model.

[0786] (Claim 3)

[0787] Sentiment analysis can be performed using natural language processing techniques,

[0788] The system according to claim 1, comprising means for inferring emotions by referring to emotional information inherent in stamps and emojis.

[0789] "Example 1"

[0790] (Claim 1)

[0791] A means for transmitting information received by an information processing device operated by a user to a communication device,

[0792] A means of analyzing the information using a communication device to identify emotions,

[0793] A means of analyzing response patterns by referring to the user's past communication history using a communication device,

[0794] A means for generating appropriate responses using a generative AI based on emotions and user response patterns,

[0795] A system that includes means for sending the generated response to an information processing device and automatically sending a reply.

[0796] (Claim 2)

[0797] A means by which a user inputs an evaluation of an automated response and transmits that evaluation to a communication device,

[0798] The system according to claim 1, further comprising means for accumulating evaluations using a communication device and using them as training data to improve the accuracy of an AI model.

[0799] (Claim 3)

[0800] Sentiment analysis can be performed using natural language processing techniques,

[0801] The system according to claim 1, comprising means for inferring emotions by referring to emotional information inherent in symbols or image representations.

[0802] "Application Example 1"

[0803] (Claim 1)

[0804] A means for transmitting communication data received by a user terminal to an information processing device,

[0805] A means of analyzing the communication data using an information processing device to identify emotions,

[0806] A means of analyzing response patterns by referring to the user's past history using an information processing device,

[0807] A means of generating appropriate responses using a generative AI based on emotions and user response patterns,

[0808] A means of sending the generated response to the user terminal and automatically performing the response,

[0809] A means of determining the content recommended based on the user's emotions,

[0810] A system including means for transmitting determined content to a communication terminal.

[0811] (Claim 2)

[0812] A means by which a user inputs their opinion on an automated response and transmits that opinion to an information processing device,

[0813] The system according to claim 1, comprising means for accumulating opinions in an information processing device and using them as training data to improve the accuracy of an AI model.

[0814] (Claim 3)

[0815] Sentiment analysis can be performed using natural language processing techniques,

[0816] The system according to claim 1, comprising means for inferring emotions by referring to emotional information inherent in the illustrated information.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] A means of transferring data acquired from a user terminal to a server,

[0820] A means for analyzing the content of the data on a server and determining the emotion,

[0821] A means to refer to the user's history information on the server and analyze the reply format,

[0822] A means for generating appropriate responses using a generative AI model based on emotions and user response formats,

[0823] A system that includes means for transmitting the generated reply to the user's terminal and performing an automated response.

[0824] (Claim 2)

[0825] A means for users to input their opinions on automated responses and for those opinions to be sent to the server,

[0826] The system according to claim 1, further comprising means for accumulating opinions on a server and applying them as training data for the purpose of improving the accuracy of an AI model.

[0827] (Claim 3)

[0828] Methods for employing language processing techniques in emotion analysis,

[0829] The system according to claim 1, comprising means for inferring emotions by referring to emotional information inherent in symbolic information.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] A means for transmitting data received by a user terminal to an information processing device,

[0833] A means of analyzing the data using an information processing device to identify emotions,

[0834] A means of analyzing patterns by referring to the user's past behavior history using an information processing device,

[0835] A means of generating appropriate information using a generative AI based on emotions and user patterns,

[0836] A means of sending the generated information to the user terminal and automatically responding,

[0837] A system that includes means for detecting potential threats from received data on the user's terminal and generating warnings.

[0838] (Claim 2)

[0839] A means for a user to input evaluation data for an automated response and to transmit that evaluation data to an information processing device,

[0840] The system according to claim 1, further comprising means for accumulating evaluation data in an information processing device and using it as training data to improve the accuracy of an AI model.

[0841] (Claim 3)

[0842] Sentiment analysis can be performed using natural language processing techniques,

[0843] A means of inferring emotions by referring to the information inherent in symbols and emojis,

[0844] The system according to claim 1, comprising means for detecting a user's anxiety and generating empathetic suggestions. [Explanation of symbols]

[0845] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of sending message data received on the user terminal to the server, A means of analyzing the message data on the server to identify the emotion, A method for analyzing reply patterns by referring to the user's past conversation history on the server, A means of generating appropriate replies using a generation AI based on emotions and user response patterns, A system that includes a means of sending generated replies to the user's terminal and automatically responding.

2. A means for the user to input feedback on an automated response and send that feedback to the server, The system according to claim 1, further comprising means for accumulating feedback on a server and using it as training data to improve the accuracy of an AI model.

3. Sentiment analysis can be performed using natural language processing techniques, The system according to claim 1, comprising means for inferring emotions by referring to emotional information inherent in stamps and emojis.

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